Made the lines shorted in the resize implementation of tensorflow.

keras/backend/tensorflow_backend.py

@@ -1962,7 +1962,11 @@ def permute_dimensions(x, pattern):
     return tf.transpose(x, perm=pattern)
 
 
-def resize_images(x, height_factor, width_factor, data_format, interpolation='nearest'):
+def resize_images(x,
+                  height_factor,
+                  width_factor,
+                  data_format,
+                  interpolation='nearest'):
     """Resizes the images contained in a 4D tensor.
 
     # Arguments
@@ -1978,37 +1982,40 @@ def resize_images(x, height_factor, width_factor, data_format, interpolation='ne
     # Raises
         ValueError: if `data_format` is neither `"channels_last"` or `"channels_first"`.
     """
+    if data_format == 'channels_first':
+        rows, cols = 2, 3
+    else:
+        rows, cols = 1, 2
+
     original_shape = int_shape(x)
+    new_shape = tf.shape(x)[rows:cols + 1]
+    new_shape *= tf.constant(np.array([height_factor, width_factor], dtype='int32'))
+
     if data_format == 'channels_first':
-        new_shape = tf.shape(x)[2:]
-        new_shape *= tf.constant(np.array([height_factor, width_factor]).astype('int32'))
         x = permute_dimensions(x, [0, 2, 3, 1])
-        if interpolation == 'nearest':
-            x = tf.image.resize_nearest_neighbor(x, new_shape)
-        elif interpolation == 'bilinear':
-            x = tf.image.resize_bilinear(x, new_shape)
-        else:
-            raise ValueError('interpolation should be one '
-                             'of "nearest" or "bilinear".')
+    if interpolation == 'nearest':
+        x = tf.image.resize_nearest_neighbor(x, new_shape)
+    elif interpolation == 'bilinear':
+        x = tf.image.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':
-        new_shape = tf.shape(x)[1:3]
-        new_shape *= tf.constant(np.array([height_factor, width_factor]).astype('int32'))
-        if interpolation == 'nearest':
-            x = tf.image.resize_nearest_neighbor(x, new_shape)
-        elif interpolation == 'bilinear':
-            x = tf.image.resize_bilinear(x, new_shape)
-        else:
-            raise ValueError('interpolation should be one '
-                             'of "nearest" or "bilinear".')
-        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('Unknown 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
+
+    output_shape = (None, new_height, new_width, None)
+    x.set_shape(transpose_shape(output_shape, data_format, spatial_axes=(1, 2)))
+    return x
 
 
 def resize_volumes(x, depth_factor, height_factor, width_factor, data_format):