Make the gfile package available when importing tensorflow.

Update programs that were importing both 'tensorflow' and 'gfile' to use
'gfile' from the tensorflow import.
Change: 114249943

6 files changed, 27 insertions, 31 deletions

diff --git a/tensorflow/models/image/cifar10/cifar10_eval.py b/tensorflow/models/image/cifar10/cifar10_eval.py
index 6dc1db7248..9ba89e4e1e 100644
--- a/tensorflow/models/image/cifar10/cifar10_eval.py
+++ b/tensorflow/models/image/cifar10/cifar10_eval.py
@@ -39,7 +39,7 @@ import math
 import time
 
 import tensorflow.python.platform
-from tensorflow.python.platform import gfile
+
 import numpy as np
 import tensorflow as tf
 
@@ -151,9 +151,9 @@ def evaluate():
 
 def main(argv=None):  # pylint: disable=unused-argument
   cifar10.maybe_download_and_extract()
-  if gfile.Exists(FLAGS.eval_dir):
-    gfile.DeleteRecursively(FLAGS.eval_dir)
-  gfile.MakeDirs(FLAGS.eval_dir)
+  if tf.gfile.Exists(FLAGS.eval_dir):
+    tf.gfile.DeleteRecursively(FLAGS.eval_dir)
+  tf.gfile.MakeDirs(FLAGS.eval_dir)
   evaluate()
 
 
diff --git a/tensorflow/models/image/cifar10/cifar10_input.py b/tensorflow/models/image/cifar10/cifar10_input.py
index f7d7083d73..d5e12c08b9 100644
--- a/tensorflow/models/image/cifar10/cifar10_input.py
+++ b/tensorflow/models/image/cifar10/cifar10_input.py
@@ -25,8 +25,6 @@ import tensorflow.python.platform
 from six.moves import xrange  # pylint: disable=redefined-builtin
 import tensorflow as tf
 
-from tensorflow.python.platform import gfile
-
 # Process images of this size. Note that this differs from the original CIFAR
 # image size of 32 x 32. If one alters this number, then the entire model
 # architecture will change and any model would need to be retrained.
@@ -144,7 +142,7 @@ def distorted_inputs(data_dir, batch_size):
   filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i)
                for i in xrange(1, 6)]
   for f in filenames:
-    if not gfile.Exists(f):
+    if not tf.gfile.Exists(f):
       raise ValueError('Failed to find file: ' + f)
 
   # Create a queue that produces the filenames to read.
@@ -209,7 +207,7 @@ def inputs(eval_data, data_dir, batch_size):
     num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_EVAL
 
   for f in filenames:
-    if not gfile.Exists(f):
+    if not tf.gfile.Exists(f):
       raise ValueError('Failed to find file: ' + f)
 
   # Create a queue that produces the filenames to read.
diff --git a/tensorflow/models/image/cifar10/cifar10_multi_gpu_train.py b/tensorflow/models/image/cifar10/cifar10_multi_gpu_train.py
index f594b86627..b7c07435af 100644
--- a/tensorflow/models/image/cifar10/cifar10_multi_gpu_train.py
+++ b/tensorflow/models/image/cifar10/cifar10_multi_gpu_train.py
@@ -46,7 +46,7 @@ import time
 
 # pylint: disable=unused-import,g-bad-import-order
 import tensorflow.python.platform
-from tensorflow.python.platform import gfile
+
 import numpy as np
 from six.moves import xrange  # pylint: disable=redefined-builtin
 import tensorflow as tf
@@ -275,9 +275,9 @@ def train():
 
 def main(argv=None):  # pylint: disable=unused-argument
   cifar10.maybe_download_and_extract()
-  if gfile.Exists(FLAGS.train_dir):
-    gfile.DeleteRecursively(FLAGS.train_dir)
-  gfile.MakeDirs(FLAGS.train_dir)
+  if tf.gfile.Exists(FLAGS.train_dir):
+    tf.gfile.DeleteRecursively(FLAGS.train_dir)
+  tf.gfile.MakeDirs(FLAGS.train_dir)
   train()
 
 
diff --git a/tensorflow/models/image/cifar10/cifar10_train.py b/tensorflow/models/image/cifar10/cifar10_train.py
index fb2ef56e1e..1882f256bd 100644
--- a/tensorflow/models/image/cifar10/cifar10_train.py
+++ b/tensorflow/models/image/cifar10/cifar10_train.py
@@ -41,7 +41,6 @@ import os.path
 import time
 
 import tensorflow.python.platform
-from tensorflow.python.platform import gfile
 
 import numpy as np
 from six.moves import xrange  # pylint: disable=redefined-builtin
@@ -128,9 +127,9 @@ def train():
 
 def main(argv=None):  # pylint: disable=unused-argument
   cifar10.maybe_download_and_extract()
-  if gfile.Exists(FLAGS.train_dir):
-    gfile.DeleteRecursively(FLAGS.train_dir)
-  gfile.MakeDirs(FLAGS.train_dir)
+  if tf.gfile.Exists(FLAGS.train_dir):
+    tf.gfile.DeleteRecursively(FLAGS.train_dir)
+  tf.gfile.MakeDirs(FLAGS.train_dir)
   train()
 
 
diff --git a/tensorflow/models/image/imagenet/classify_image.py b/tensorflow/models/image/imagenet/classify_image.py
index 2459f8a633..838cc568ba 100644
--- a/tensorflow/models/image/imagenet/classify_image.py
+++ b/tensorflow/models/image/imagenet/classify_image.py
@@ -47,8 +47,6 @@ import numpy as np
 import tensorflow as tf
 # pylint: enable=unused-import,g-bad-import-order
 
-from tensorflow.python.platform import gfile
-
 FLAGS = tf.app.flags.FLAGS
 
 # classify_image_graph_def.pb:
@@ -96,13 +94,13 @@ class NodeLookup(object):
     Returns:
       dict from integer node ID to human-readable string.
     """
-    if not gfile.Exists(uid_lookup_path):
+    if not tf.gfile.Exists(uid_lookup_path):
       tf.logging.fatal('File does not exist %s', uid_lookup_path)
-    if not gfile.Exists(label_lookup_path):
+    if not tf.gfile.Exists(label_lookup_path):
       tf.logging.fatal('File does not exist %s', label_lookup_path)
 
     # Loads mapping from string UID to human-readable string
-    proto_as_ascii_lines = gfile.GFile(uid_lookup_path).readlines()
+    proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()
     uid_to_human = {}
     p = re.compile(r'[n\d]*[ \S,]*')
     for line in proto_as_ascii_lines:
@@ -113,7 +111,7 @@ class NodeLookup(object):
 
     # Loads mapping from string UID to integer node ID.
     node_id_to_uid = {}
-    proto_as_ascii = gfile.GFile(label_lookup_path).readlines()
+    proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()
     for line in proto_as_ascii:
       if line.startswith('  target_class:'):
         target_class = int(line.split(': ')[1])
@@ -138,9 +136,9 @@ class NodeLookup(object):
 
 
 def create_graph():
-  """"Creates a graph from saved GraphDef file and returns a saver."""
+  """Creates a graph from saved GraphDef file and returns a saver."""
   # Creates graph from saved graph_def.pb.
-  with gfile.FastGFile(os.path.join(
+  with tf.gfile.FastGFile(os.path.join(
       FLAGS.model_dir, 'classify_image_graph_def.pb'), 'rb') as f:
     graph_def = tf.GraphDef()
     graph_def.ParseFromString(f.read())
@@ -156,9 +154,9 @@ def run_inference_on_image(image):
   Returns:
     Nothing
   """
-  if not gfile.Exists(image):
+  if not tf.gfile.Exists(image):
     tf.logging.fatal('File does not exist %s', image)
-  image_data = gfile.FastGFile(image, 'rb').read()
+  image_data = tf.gfile.FastGFile(image, 'rb').read()
 
   # Creates graph from saved GraphDef.
   create_graph()
diff --git a/tensorflow/models/image/mnist/convolutional.py b/tensorflow/models/image/mnist/convolutional.py
index edceb2a1ec..c0dfcc7979 100644
--- a/tensorflow/models/image/mnist/convolutional.py
+++ b/tensorflow/models/image/mnist/convolutional.py
@@ -55,13 +55,14 @@ FLAGS = tf.app.flags.FLAGS
 
 def maybe_download(filename):
   """Download the data from Yann's website, unless it's already here."""
-  if not os.path.exists(WORK_DIRECTORY):
-    os.mkdir(WORK_DIRECTORY)
+  if not tf.gfile.Exists(WORK_DIRECTORY):
+    tf.gfile.MakeDirs(WORK_DIRECTORY)
   filepath = os.path.join(WORK_DIRECTORY, filename)
-  if not os.path.exists(filepath):
+  if not tf.gfile.Exists(filepath):
     filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)
-    statinfo = os.stat(filepath)
-    print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
+    with tf.gfile.GFile(filepath) as f:
+      size = f.Size()
+    print('Successfully downloaded', filename, size, 'bytes.')
   return filepath