1 files changed, 40 insertions, 24 deletions
diff --git a/tensorflow/python/keras/backend.py b/tensorflow/python/keras/backend.py
index 13f52fbae7..7509ef9c59 100644
--- a/tensorflow/python/keras/backend.py
+++ b/tensorflow/python/keras/backend.py
@@ -2338,7 +2338,8 @@ def permute_dimensions(x, pattern):
 
 
 @tf_export('keras.backend.resize_images')
-def resize_images(x, height_factor, width_factor, data_format):
+def resize_images(x, height_factor, width_factor, data_format,
+                  interpolation='nearest'):
   """Resizes the images contained in a 4D tensor.
 
   Arguments:
@@ -2346,40 +2347,55 @@ def resize_images(x, height_factor, width_factor, data_format):
       height_factor: Positive integer.
       width_factor: Positive integer.
       data_format: One of `"channels_first"`, `"channels_last"`.
+      interpolation: A string, one of `nearest` or `bilinear`.
 
   Returns:
       A tensor.
 
   Raises:
-      ValueError: if `data_format` is neither
-          `channels_last` or `channels_first`.
+      ValueError: in case of incorrect value for
+        `data_format` or `interpolation`.
   """
   if data_format == 'channels_first':
-    original_shape = int_shape(x)
-    new_shape = array_ops.shape(x)[2:]
-    new_shape *= constant_op.constant(
-        np.array([height_factor, width_factor]).astype('int32'))
+    rows, cols = 2, 3
+  elif data_format == 'channels_last':
+    rows, cols = 1, 2
+  else:
+    raise ValueError('Invalid `data_format` argument: %s' % (data_format,))
+
+  original_shape = int_shape(x)
+  new_shape = array_ops.shape(x)[rows:cols + 1]
+  new_shape *= constant_op.constant(
+      np.array([height_factor, width_factor], dtype='int32'))
+
+  if data_format == 'channels_first':
     x = permute_dimensions(x, [0, 2, 3, 1])
+  if interpolation == 'nearest':
     x = image_ops.resize_nearest_neighbor(x, new_shape)
+  elif interpolation == 'bilinear':
+    x = image_ops.resize_bilinear(x, new_shape)
+  else:
+    raise ValueError('interpolation should be one '
+                     'of "nearest" or "bilinear".')
+  if data_format == 'channels_first':
     x = permute_dimensions(x, [0, 3, 1, 2])
-    x.set_shape((None, None, original_shape[2] * height_factor
-                 if original_shape[2] is not None else None,
-                 original_shape[3] * width_factor
-                 if original_shape[3] is not None else None))
-    return x
-  elif data_format == 'channels_last':
-    original_shape = int_shape(x)
-    new_shape = array_ops.shape(x)[1:3]
-    new_shape *= constant_op.constant(
-        np.array([height_factor, width_factor]).astype('int32'))
-    x = image_ops.resize_nearest_neighbor(x, new_shape)
-    x.set_shape((None, original_shape[1] * height_factor
-                 if original_shape[1] is not None else None,
-                 original_shape[2] * width_factor
-                 if original_shape[2] is not None else None, None))
-    return x
+
+  if original_shape[rows] is None:
+    new_height = None
   else:
-    raise ValueError('Invalid data_format: ' + str(data_format))
+    new_height = original_shape[rows] * height_factor
+
+  if original_shape[cols] is None:
+    new_width = None
+  else:
+    new_width = original_shape[cols] * width_factor
+
+  if data_format == 'channels_first':
+    output_shape = (None, None, new_height, new_width)
+  else:
+    output_shape = (None, new_height, new_width, None)
+  x.set_shape(output_shape)
+  return x
 
 
 @tf_export('keras.backend.resize_volumes')