Automated rollback of change 129807750

Change: 130017620

3 files changed, 11 insertions, 48 deletions

diff --git a/tensorflow/examples/image_retraining/BUILD b/tensorflow/examples/image_retraining/BUILD
index b1f83f76d5..4cf6adecb9 100644
--- a/tensorflow/examples/image_retraining/BUILD
+++ b/tensorflow/examples/image_retraining/BUILD
@@ -21,14 +21,9 @@ py_test(
     name = "retrain_test",
     size = "small",
     srcs = [
-        "label_image.py",
         "retrain.py",
         "retrain_test.py",
     ],
-    data = [
-        ":data/labels.txt",
-        "//tensorflow/examples/label_image:data/grace_hopper.jpg",
-    ],
     srcs_version = "PY2AND3",
     deps = [
         "//tensorflow:tensorflow_py",
@@ -40,9 +35,7 @@ py_test(
 filegroup(
     name = "all_files",
     srcs = glob(
-        [
-            "**/*",
-        ],
+        ["**/*"],
         exclude = [
             "**/METADATA",
             "**/OWNERS",
diff --git a/tensorflow/examples/image_retraining/retrain.py b/tensorflow/examples/image_retraining/retrain.py
index 8ca3c570d7..6a3024d5bc 100644
--- a/tensorflow/examples/image_retraining/retrain.py
+++ b/tensorflow/examples/image_retraining/retrain.py
@@ -79,6 +79,8 @@ import tensorflow as tf
 
 from tensorflow.python.framework import graph_util
 from tensorflow.python.framework import tensor_shape
+from tensorflow.python.platform import gfile
+
 
 FLAGS = tf.app.flags.FLAGS
 
@@ -178,7 +180,7 @@ def create_image_lists(image_dir, testing_percentage, validation_percentage):
     A dictionary containing an entry for each label subfolder, with images split
     into training, testing, and validation sets within each label.
   """
-  if not tf.gfile.Exists(image_dir):
+  if not gfile.Exists(image_dir):
     print("Image directory '" + image_dir + "' not found.")
     return None
   result = {}
@@ -301,7 +303,7 @@ def create_inception_graph():
   with tf.Session() as sess:
     model_filename = os.path.join(
         FLAGS.model_dir, 'classify_image_graph_def.pb')
-    with tf.gfile.FastGFile(model_filename, 'rb') as f:
+    with gfile.FastGFile(model_filename, 'rb') as f:
       graph_def = tf.GraphDef()
       graph_def.ParseFromString(f.read())
       bottleneck_tensor, jpeg_data_tensor, resized_input_tensor = (
@@ -404,9 +406,9 @@ def get_or_create_bottleneck(sess, image_lists, label_name, index, image_dir,
     print('Creating bottleneck at ' + bottleneck_path)
     image_path = get_image_path(image_lists, label_name, index, image_dir,
                                 category)
-    if not tf.gfile.Exists(image_path):
+    if not gfile.Exists(image_path):
       tf.logging.fatal('File does not exist %s', image_path)
-    image_data = tf.gfile.FastGFile(image_path, 'rb').read()
+    image_data = gfile.FastGFile(image_path, 'rb').read()
     bottleneck_values = run_bottleneck_on_image(sess, image_data,
                                                 jpeg_data_tensor,
                                                 bottleneck_tensor)
@@ -535,9 +537,9 @@ def get_random_distorted_bottlenecks(
     image_index = random.randrange(65536)
     image_path = get_image_path(image_lists, label_name, image_index, image_dir,
                                 category)
-    if not tf.gfile.Exists(image_path):
+    if not gfile.Exists(image_path):
       tf.logging.fatal('File does not exist %s', image_path)
-    jpeg_data = tf.gfile.FastGFile(image_path, 'rb').read()
+    jpeg_data = gfile.FastGFile(image_path, 'rb').read()
     # Note that we materialize the distorted_image_data as a numpy array before
     # sending running inference on the image. This involves 2 memory copies and
     # might be optimized in other implementations.
@@ -876,9 +878,9 @@ def main(_):
   # Write out the trained graph and labels with the weights stored as constants.
   output_graph_def = graph_util.convert_variables_to_constants(
       sess, graph.as_graph_def(), [FLAGS.final_tensor_name])
-  with tf.gfile.FastGFile(FLAGS.output_graph, 'wb') as f:
+  with gfile.FastGFile(FLAGS.output_graph, 'wb') as f:
     f.write(output_graph_def.SerializeToString())
-  with tf.gfile.FastGFile(FLAGS.output_labels, 'w') as f:
+  with gfile.FastGFile(FLAGS.output_labels, 'w') as f:
     f.write('\n'.join(image_lists.keys()) + '\n')
 
 
diff --git a/tensorflow/examples/image_retraining/retrain_test.py b/tensorflow/examples/image_retraining/retrain_test.py
index fb9acea3eb..072998ae60 100644
--- a/tensorflow/examples/image_retraining/retrain_test.py
+++ b/tensorflow/examples/image_retraining/retrain_test.py
@@ -19,9 +19,7 @@ from __future__ import division
 from __future__ import print_function
 
 import tensorflow as tf
-import os
 
-from tensorflow.examples.image_retraining import label_image
 from tensorflow.examples.image_retraining import retrain
 from tensorflow.python.framework import test_util
 
@@ -82,35 +80,5 @@ class ImageRetrainingTest(test_util.TensorFlowTestCase):
       gt = tf.placeholder(tf.float32, [1], name='gt')
       self.assertIsNotNone(retrain.add_evaluation_step(final, gt))
 
-  def testLabelImage(self):
-
-    image_filename = ('../label_image/data/grace_hopper.jpg')
-
-    # Load some default data
-    label_path = os.path.join(tf.resource_loader.get_data_files_path(),
-                              'data/labels.txt')
-    labels = label_image.load_labels(label_path)
-    self.assertEqual(len(labels), 3)
-
-    image_path = os.path.join(tf.resource_loader.get_data_files_path(),
-                              image_filename)
-
-    image = label_image.load_image(image_path)
-    self.assertEqual(len(image), 61306)
-
-    # Create trivial graph; note that the two nodes don't meet
-    with tf.Graph().as_default():
-      jpeg = tf.constant(image)
-      # Input node that doesn't lead anywhere.
-      tf.image.decode_jpeg(jpeg, name='DecodeJpeg')
-
-      # Output node, that always outputs a constant.
-      final = tf.constant([[10, 30, 5]], name='final')
-
-      # As label_image outputs via print, we assume that
-      # if it returns, everything is OK.
-      result = label_image.run_graph(image, labels, jpeg, final)
-      self.assertEqual(result, 0)
-
 if __name__ == '__main__':
   tf.test.main()