Merge pull request #29 from rsepassi/push

Push 1.0.6

README.md

@@ -26,6 +26,20 @@ issues](https://github.com/tensorflow/tensor2tensor/issues).
 And chat with us and other users on
 [Gitter](https://gitter.im/tensor2tensor/Lobby).
 
+### Contents
+
+* [Walkthrough](#walkthrough)
+* [Installation](#installation)
+* [Features](#features)
+* [T2T Overview](#t2t-overview)
+  * [Datasets](#datasets)
+  * [Problems and Modalities](#problems-and-modalities)
+  * [Models](#models)
+  * [Hyperparameter Sets](#hyperparameter-sets)
+  * [Trainer](#trainer)
+* [Adding your own components](#adding-your-own-components)
+* [Adding a dataset](#adding-a-dataset)
+
 ---
 
 ## Walkthrough

setup.py

@@ -5,7 +5,7 @@
 
 setup(
     name='tensor2tensor',
-    version='1.0.5',
+    version='1.0.6',
     description='Tensor2Tensor',
     author='Google Inc.',
     author_email='[email protected]',

tensor2tensor/data_generators/generator_utils.py

@@ -127,15 +127,15 @@ def generate_files(generator,
 
 
 def download_report_hook(count, block_size, total_size):
-  """Report hook for download progress
+  """Report hook for download progress.
 
   Args:
     count: current block number
     block_size: block size
     total_size: total size
   """
-  percent = int(count*block_size*100/total_size)
-  print("\r%d%%" % percent + ' completed', end='\r')
+  percent = int(count * block_size * 100 / total_size)
+  print("\r%d%%" % percent + " completed", end="\r")
 
 
 def maybe_download(directory, filename, url):
@@ -155,11 +155,12 @@ def maybe_download(directory, filename, url):
   filepath = os.path.join(directory, filename)
   if not tf.gfile.Exists(filepath):
     tf.logging.info("Downloading %s to %s" % (url, filepath))
-    filepath, _ = urllib.urlretrieve(url, filepath,
-                                     reporthook=download_report_hook)
-
+    inprogress_filepath = filepath + ".incomplete"
+    inprogress_filepath, _ = urllib.urlretrieve(url, inprogress_filepath,
+                                                reporthook=download_report_hook)
     # Print newline to clear the carriage return from the download progress
     print()
+    tf.gfile.Rename(inprogress_filepath, filepath)
     statinfo = os.stat(filepath)
     tf.logging.info("Succesfully downloaded %s, %s bytes." % (filename,
                                                               statinfo.st_size))

tensor2tensor/models/common_layers.py

@@ -1079,6 +1079,7 @@ def conv_hidden_relu(inputs,
                      hidden_size,
                      output_size,
                      kernel_size=(1, 1),
+                     second_kernel_size=(1, 1),
                      summaries=True,
                      dropout=0.0,
                      **kwargs):
@@ -1090,7 +1091,8 @@ def conv_hidden_relu(inputs,
       inputs = tf.expand_dims(inputs, 2)
     else:
       is_3d = False
-    h = conv(
+    conv_f1 = conv if kernel_size == (1, 1) else separable_conv
+    h = conv_f1(
         inputs,
         hidden_size,
         kernel_size,
@@ -1103,7 +1105,8 @@ def conv_hidden_relu(inputs,
       tf.summary.histogram("hidden_density_logit",
                            relu_density_logit(
                                h, list(range(inputs.shape.ndims - 1))))
-    ret = conv(h, output_size, (1, 1), name="conv2", **kwargs)
+    conv_f2 = conv if second_kernel_size == (1, 1) else separable_conv
+    ret = conv_f2(h, output_size, second_kernel_size, name="conv2", **kwargs)
     if is_3d:
       ret = tf.squeeze(ret, 2)
     return ret

tensor2tensor/models/transformer.py

@@ -255,6 +255,15 @@ def transformer_ffn_layer(x, hparams):
         hparams.filter_size,
         hparams.num_heads,
         hparams.attention_dropout)
+  elif hparams.ffn_layer == "conv_hidden_relu_with_sepconv":
+    return common_layers.conv_hidden_relu(
+        x,
+        hparams.filter_size,
+        hparams.hidden_size,
+        kernel_size=(3, 1),
+        second_kernel_size=(31, 1),
+        padding="LEFT",
+        dropout=hparams.relu_dropout)
   else:
     assert hparams.ffn_layer == "none"
     return x
@@ -342,7 +351,6 @@ def transformer_parsing_base():
   hparams.learning_rate_warmup_steps = 16000
   hparams.hidden_size = 1024
   hparams.learning_rate = 0.05
-  hparams.residual_dropout = 0.1
   hparams.shared_embedding_and_softmax_weights = int(False)
   return hparams
 

tensor2tensor/utils/data_reader.py

@@ -100,14 +100,16 @@ def examples_queue(data_sources,
   with tf.name_scope("examples_queue"):
     # Read serialized examples using slim parallel_reader.
     num_epochs = None if training else 1
+    data_files = tf.contrib.slim.parallel_reader.get_data_files(data_sources)
+    num_readers = min(4 if training else 1, len(data_files))
     _, example_serialized = tf.contrib.slim.parallel_reader.parallel_read(
         data_sources,
         tf.TFRecordReader,
         num_epochs=num_epochs,
         shuffle=training,
         capacity=2 * capacity,
         min_after_dequeue=capacity,
-        num_readers=4 if training else 1)
+        num_readers=num_readers)
 
     if data_items_to_decoders is None:
       data_items_to_decoders = {