Merge pull request #290 from rsepassi/push

v1.2.2

README.md

@@ -214,7 +214,8 @@ on the task (e.g. fed through a final linear transform to produce logits for a
 softmax over classes). All models are imported in
 [`models.py`](https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/models/models.py),
 inherit from `T2TModel` - defined in
-[`t2t_model.py`](https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/utils/t2t_model.py) - and are registered with
+[`t2t_model.py`](https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/utils/t2t_model.py) -
+and are registered with
 [`@registry.register_model`](https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/utils/registry.py).
 
 ### Hyperparameter Sets

docs/new_problem.md

@@ -15,9 +15,17 @@ Let's add a new dataset together and train the transformer model. We'll be learn
 
 For each problem we want to tackle we create a new problem class and register it. Let's call our problem `Word2def`.
 
-Since many text2text problems share similar methods, there's already a class called [`Text2TextProblem`](https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/data_generators/problem.py#L354) that extends the base problem class, `Problem` (both found in [`problem.py`](https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/data_generators/problem.py)).
-
-For our problem, we can go ahead and create the file `word2def.py` in the [`data_generators`](https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/data_generators/) folder and add our new problem, `Word2def`, which extends [`Text2TextProblem`](https://github.com/tensorflow/tensor2tensor/blob/24071ba07d5a14c170044c5e60a24bda8179fb7a/tensor2tensor/data_generators/problem.py#L354). Let's also register it while we're at it so we can specify the problem through flags.
+Since many text2text problems share similar methods, there's already a class
+called `Text2TextProblem` that extends the base problem class, `Problem`
+(both found in
+[`problem.py`](https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/data_generators/problem.py)).
+
+For our problem, we can go ahead and create the file `word2def.py` in the
+[`data_generators`](https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/data_generators/)
+folder and add our new problem, `Word2def`, which extends
+[`Text2TextProblem`](https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/data_generators/problem.py).
+Let's also register it while we're at it so we can specify the problem through
+flags.
 
 ```python
 @registry.register_problem
@@ -28,7 +36,9 @@ class Word2def(problem.Text2TextProblem):
     ...
 ```
 
-We need to implement the following methods from [`Text2TextProblem`](https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/data_generators/problem.py#L354) in our new class:
+We need to implement the following methods from
+[`Text2TextProblem`](https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/data_generators/problem.py).
+in our new class:
 * is_character_level
 * targeted_vocab_size
 * generator
@@ -42,7 +52,12 @@ Let's tackle them one by one:
 
 **input_space_id, target_space_id, is_character_level, targeted_vocab_size, use_subword_tokenizer**:
 
-SpaceIDs tell Tensor2Tensor what sort of space the input and target tensors are in. These are things like, EN_CHR (English character), EN_TOK (English token), AUDIO_WAV (audio waveform), IMAGE, DNA (genetic bases). The complete list can be found at [`data_generators/problem.py`](https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/data_generators/problem.py) in the class `SpaceID`.
+SpaceIDs tell Tensor2Tensor what sort of space the input and target tensors are
+in. These are things like, EN_CHR (English character), EN_TOK (English token),
+AUDIO_WAV (audio waveform), IMAGE, DNA (genetic bases). The complete list can be
+found at
+[`data_generators/problem.py`](https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/data_generators/problem.py).
+in the class `SpaceID`.
 
 Since we're generating definitions and feeding in words at the character level, we set `is_character_level` to true, and use the same SpaceID, EN_CHR, for both input and target. Additionally, since we aren't using tokens, we don't need to give a `targeted_vocab_size` or define `use_subword_tokenizer`.
 
@@ -58,7 +73,7 @@ The number of shards to break data files into.
 @registry.register_problem()
 class Word2def(problem.Text2TextProblem):
   """Problem spec for English word to dictionary definition."""
-  
+
   @property
   def is_character_level(self):
     return True
@@ -86,7 +101,15 @@ class Word2def(problem.Text2TextProblem):
 
 **generator**:
 
-We're almost done. `generator` generates the training and evaluation data and stores them in files like  "word2def_train.lang1" in your DATA_DIR. Thankfully several commonly used methods like `character_generator`, and `token_generator` are already written in the file [`wmt.py`](https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/data_generators/wmt.py). We will import `character_generator` and [`text_encoder`](https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/data_generators/text_encoder.py) to write:
+We're almost done. `generator` generates the training and evaluation data and
+stores them in files like "word2def_train.lang1" in your DATA_DIR. Thankfully
+several commonly used methods like `character_generator`, and `token_generator`
+are already written in the file
+[`wmt.py`](https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/data_generators/wmt.py).
+We will import `character_generator` and
+[`text_encoder`](https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/data_generators/text_encoder.py)
+to write:
+
 ```python
   def generator(self, data_dir, tmp_dir, train):
     character_vocab = text_encoder.ByteTextEncoder()
@@ -152,6 +175,7 @@ _WORD2DEF_TEST_DATASETS = [
 ## Putting it all together
 
 Now our `word2def.py` file looks like:
+
 ```python
 """ Problem definition for word to dictionary definition.
 """
@@ -210,7 +234,7 @@ class Word2def(problem.Text2TextProblem):
 ```
 
 # Hyperparameters
-All hyperparamters inherit from `_default_hparams()` in `problem.py.` If you would like to customize your hyperparameters, register a new hyperparameter set in `word2def.py` like the example provided in the walkthrough.  For example:
+All hyperparamters inherit from `_default_hparams()` in `problem.py.` If you would like to customize your hyperparameters, register a new hyperparameter set in `word2def.py` like the example provided in the walkthrough. For example:
 
 ```python
 from tensor2tensor.models import transformer

setup.py

@@ -5,7 +5,7 @@
 
 setup(
     name='tensor2tensor',
-    version='1.2.1',
+    version='1.2.2',
     description='Tensor2Tensor',
     author='Google Inc.',
     author_email='[email protected]',

tensor2tensor/data_generators/all_problems.py

@@ -45,4 +45,3 @@
   pass
 # pylint: enable=g-import-not-at-top
 # pylint: enable=unused-import
-

tensor2tensor/data_generators/cnn_dailymail.py

@@ -53,8 +53,8 @@ def _maybe_download_corpora(tmp_dir):
     filepath of the downloaded corpus file.
   """
   cnn_filename = "cnn_stories.tgz"
-  dailymail_filename = "dailymail_stories.tgz"
   cnn_finalpath = os.path.join(tmp_dir, "cnn/stories/")
+  dailymail_filename = "dailymail_stories.tgz"
   dailymail_finalpath = os.path.join(tmp_dir, "dailymail/stories/")
   if not tf.gfile.Exists(cnn_finalpath):
     cnn_file = generator_utils.maybe_download_from_drive(
@@ -63,7 +63,7 @@ def _maybe_download_corpora(tmp_dir):
       cnn_tar.extractall(tmp_dir)
   if not tf.gfile.Exists(dailymail_finalpath):
     dailymail_file = generator_utils.maybe_download_from_drive(
-        tmp_dir, dailymail_filename, _CNN_STORIES_DRIVE_URL)
+        tmp_dir, dailymail_filename, _DAILYMAIL_STORIES_DRIVE_URL)
     with tarfile.open(dailymail_file, "r:gz") as dailymail_tar:
       dailymail_tar.extractall(tmp_dir)
   return [cnn_finalpath, dailymail_finalpath]

tensor2tensor/data_generators/gene_expression.py

@@ -142,7 +142,7 @@ def generate_data(self, data_dir, tmp_dir, task_id=-1):
     # Shuffle
     generator_utils.shuffle_dataset(all_filepaths)
 
-  def hparams(self, defaults, model_hparams):
+  def hparams(self, defaults, unused_model_hparams):
     p = defaults
     vocab_size = self._encoders["inputs"].vocab_size
     p.input_modality = {"inputs": (registry.Modalities.SYMBOL, vocab_size)}
@@ -159,9 +159,8 @@ def example_reading_spec(self):
     data_items_to_decoders = None
     return (data_fields, data_items_to_decoders)
 
-  def preprocess_examples(self, examples, mode, hparams):
+  def preprocess_examples(self, examples, mode, unused_hparams):
     del mode
-    del hparams
 
     # Reshape targets to contain num_output_predictions per output timestep
     examples["targets"] = tf.reshape(examples["targets"],

tensor2tensor/data_generators/ice_parsing.py

@@ -109,7 +109,7 @@ def generate_data(self, data_dir, tmp_dir, task_id=-1):
                                        self.targeted_vocab_size),
         self.dev_filepaths(data_dir, 1, shuffled=False))
 
-  def hparams(self, defaults, model_hparams):
+  def hparams(self, defaults, unused_model_hparams):
     p = defaults
     source_vocab_size = self._encoders["inputs"].vocab_size
     p.input_modality = {"inputs": (registry.Modalities.SYMBOL,

tensor2tensor/data_generators/image.py

@@ -105,7 +105,7 @@ def resize(img, size):
     examples["targets"] = resize(inputs, 32)
     return examples
 
-  def hparams(self, defaults, model_hparams):
+  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)
@@ -229,7 +229,7 @@ def feature_encoders(self, data_dir):
         "targets": text_encoder.SubwordTextEncoder(vocab_filename)
     }
 
-  def hparams(self, defaults, model_hparams):
+  def hparams(self, defaults, unused_model_hparams):
     p = defaults
     p.input_modality = {"inputs": (registry.Modalities.IMAGE, None)}
     vocab_size = self._encoders["targets"].vocab_size
@@ -264,10 +264,21 @@ def train_shards(self):
   def dev_shards(self):
     return 1
 
+  @property
+  def class_labels(self):
+    return ["ID_%d" % i for i in range(self.num_classes)]
+
+  def feature_encoders(self, data_dir):
+    del data_dir
+    return {
+        "inputs": text_encoder.TextEncoder(),
+        "targets": text_encoder.ClassLabelEncoder(self.class_labels)
+    }
+
   def generator(self, data_dir, tmp_dir, is_training):
     raise NotImplementedError()
 
-  def hparams(self, defaults, model_hparams):
+  def hparams(self, defaults, unused_model_hparams):
     p = defaults
     small_modality = "%s:small_image_modality" % registry.Modalities.IMAGE
     modality = small_modality if self.is_small else registry.Modalities.IMAGE
@@ -302,7 +313,7 @@ def resize(img):
     return tf.to_int64(tf.image.resize_images(img, [299, 299]))
 
   inputs = tf.cast(examples["inputs"], tf.int64)
-  if mode == tf.contrib.learn.ModeKeys.TRAIN:
+  if mode == tf.estimator.ModeKeys.TRAIN:
     examples["inputs"] = tf.cond(  # Preprocess 90% of the time.
         tf.less(tf.random_uniform([]), 0.9),
         lambda img=inputs: preprocess(img),
@@ -349,7 +360,7 @@ def is_small(self):
   def num_classes(self):
     return 1000
 
-  def preprocess_examples(self, examples, mode, hparams):
+  def preprocess_examples(self, examples, mode, unused_hparams):
     # Just resize with area.
     if self._was_reversed:
       examples["inputs"] = tf.to_int64(
@@ -491,6 +502,10 @@ def is_small(self):
   def num_classes(self):
     return 10
 
+  @property
+  def class_labels(self):
+    return [str(c) for c in range(self.num_classes)]
+
   @property
   def train_shards(self):
     return 10
@@ -564,9 +579,17 @@ def cifar10_generator(tmp_dir, training, how_many, start_from=0):
 
 @registry.register_problem
 class ImageCifar10Tune(ImageMnistTune):
+  """Cifar-10 Tune."""
+
+  @property
+  def class_labels(self):
+    return [
+        "airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse",
+        "ship", "truck"
+    ]
 
-  def preprocess_examples(self, examples, mode, hparams):
-    if mode == tf.contrib.learn.ModeKeys.TRAIN:
+  def preprocess_examples(self, examples, mode, unused_hparams):
+    if mode == tf.estimator.ModeKeys.TRAIN:
       examples["inputs"] = common_layers.cifar_image_augmentation(
           examples["inputs"])
     return examples
@@ -591,7 +614,7 @@ def generator(self, data_dir, tmp_dir, is_training):
 @registry.register_problem
 class ImageCifar10Plain(ImageCifar10):
 
-  def preprocess_examples(self, examples, mode, hparams):
+  def preprocess_examples(self, examples, mode, unused_hparams):
     return examples
 
 
@@ -730,7 +753,7 @@ def feature_encoders(self, data_dir):
       encoder = text_encoder.SubwordTextEncoder(vocab_filename)
     return {"targets": encoder}
 
-  def hparams(self, defaults, model_hparams):
+  def hparams(self, defaults, unused_model_hparams):
     p = defaults
     p.input_modality = {"inputs": (registry.Modalities.IMAGE, None)}
     encoder = self._encoders["targets"]

tensor2tensor/data_generators/imdb.py

@@ -97,7 +97,7 @@ def generate_data(self, data_dir, tmp_dir, task_id=-1):
         self.generator(data_dir, tmp_dir, True), train_paths,
         self.generator(data_dir, tmp_dir, False), dev_paths)
 
-  def hparams(self, defaults, model_hparams):
+  def hparams(self, defaults, unused_model_hparams):
     p = defaults
     source_vocab_size = self._encoders["inputs"].vocab_size
     p.input_modality = {
@@ -112,7 +112,7 @@ def feature_encoders(self, data_dir):
     encoder = text_encoder.SubwordTextEncoder(vocab_filename)
     return {
         "inputs": encoder,
-        "targets": text_encoder.TextEncoder(),
+        "targets": text_encoder.ClassLabelEncoder(["neg", "pos"]),
     }
 
   def example_reading_spec(self):