Merge changes from github.

Change: 133096559

1 files changed, 10 insertions, 4 deletions

diff --git a/tensorflow/examples/image_retraining/retrain.py b/tensorflow/examples/image_retraining/retrain.py
index f226de0ce3..7812117e5d 100644
--- a/tensorflow/examples/image_retraining/retrain.py
+++ b/tensorflow/examples/image_retraining/retrain.py
@@ -165,6 +165,7 @@ MODEL_INPUT_HEIGHT = 299
 MODEL_INPUT_DEPTH = 3
 JPEG_DATA_TENSOR_NAME = 'DecodeJpeg/contents:0'
 RESIZED_INPUT_TENSOR_NAME = 'ResizeBilinear:0'
+MAX_NUM_IMAGES_PER_CLASS = 2 ** 27 - 1  # ~134M
 
 
 def create_image_lists(image_dir, testing_percentage, validation_percentage):
@@ -208,6 +209,9 @@ def create_image_lists(image_dir, testing_percentage, validation_percentage):
       continue
     if len(file_list) < 20:
       print('WARNING: Folder has less than 20 images, which may cause issues.')
+    elif len(file_list) > MAX_NUM_IMAGES_PER_CLASS:
+      print('WARNING: Folder {} has more than {} images. Some images will '
+            'never be selected.'.format(dir_name, MAX_NUM_IMAGES_PER_CLASS))
     label_name = re.sub(r'[^a-z0-9]+', ' ', dir_name.lower())
     training_images = []
     testing_images = []
@@ -228,7 +232,9 @@ def create_image_lists(image_dir, testing_percentage, validation_percentage):
       # itself, so we do a hash of that and then use that to generate a
       # probability value that we use to assign it.
       hash_name_hashed = hashlib.sha1(compat.as_bytes(hash_name)).hexdigest()
-      percentage_hash = (int(hash_name_hashed, 16) % (65536)) * (100 / 65535.0)
+      percentage_hash = ((int(hash_name_hashed, 16) %
+                          (MAX_NUM_IMAGES_PER_CLASS + 1)) *
+                         (100.0 / MAX_NUM_IMAGES_PER_CLASS))
       if percentage_hash < validation_percentage:
         validation_images.append(base_name)
       elif percentage_hash < (testing_percentage + validation_percentage):
@@ -323,7 +329,7 @@ def run_bottleneck_on_image(sess, image_data, image_data_tensor,
 
   Args:
     sess: Current active TensorFlow Session.
-    image_data: Numpy array of image data.
+    image_data: String of raw JPEG data.
     image_data_tensor: Input data layer in the graph.
     bottleneck_tensor: Layer before the final softmax.
 
@@ -525,7 +531,7 @@ def get_random_cached_bottlenecks(sess, image_lists, how_many, category,
   for unused_i in range(how_many):
     label_index = random.randrange(class_count)
     label_name = list(image_lists.keys())[label_index]
-    image_index = random.randrange(65536)
+    image_index = random.randrange(MAX_NUM_IMAGES_PER_CLASS + 1)
     bottleneck = get_or_create_bottleneck(sess, image_lists, label_name,
                                           image_index, image_dir, category,
                                           bottleneck_dir, jpeg_data_tensor,
@@ -570,7 +576,7 @@ def get_random_distorted_bottlenecks(
   for unused_i in range(how_many):
     label_index = random.randrange(class_count)
     label_name = list(image_lists.keys())[label_index]
-    image_index = random.randrange(65536)
+    image_index = random.randrange(MAX_NUM_IMAGES_PER_CLASS + 1)
     image_path = get_image_path(image_lists, label_name, image_index, image_dir,
                                 category)
     if not gfile.Exists(image_path):