Use public API for name_scope and convert_to_tensor

Author: seanpmorgan

tensorflow_addons/image/dense_image_warp.py

@@ -21,7 +21,6 @@
 import tensorflow as tf
 
 
-@tf.function
 def interpolate_bilinear(grid, query_points, indexing="ij", name=None):
     """Similar to Matlab's interp2 function.
 
@@ -48,31 +47,33 @@ def interpolate_bilinear(grid, query_points, indexing="ij", name=None):
     with tf.name_scope(name or "interpolate_bilinear"):
         grid = tf.convert_to_tensor(grid)
         query_points = tf.convert_to_tensor(query_points)
-        shape = grid.get_shape().as_list()
-        if len(shape) != 4:
+
+        if len(grid.shape) != 4:
             msg = "Grid must be 4 dimensional. Received size: "
-            raise ValueError(msg + str(grid.get_shape()))
+            raise ValueError(msg + str(grid.shape))
+
+        if len(query_points.shape) != 3:
+            raise ValueError("Query points must be 3 dimensional.")
+
+        grid_shape = tf.shape(grid)
+        query_shape = tf.shape(query_points)
 
-        batch_size, height, width, channels = (tf.shape(grid)[0],
-                                               tf.shape(grid)[1],
-                                               tf.shape(grid)[2],
-                                               tf.shape(grid)[3])
+        batch_size, height, width, channels = (grid_shape[0],
+                                               grid_shape[1],
+                                               grid_shape[2],
+                                               grid_shape[3])
 
         shape = [batch_size, height, width, channels]
+        num_queries = query_shape[1]
+
         query_type = query_points.dtype
         grid_type = grid.dtype
 
         tf.debugging.assert_equal(
-            len(query_points.get_shape()),
-            3,
-            message="Query points must be 3 dimensional.")
-        tf.debugging.assert_equal(
-            tf.shape(query_points)[2],
+            query_shape[2],
             2,
             message="Query points must be size 2 in dim 2.")
 
-        num_queries = tf.shape(query_points)[1]
-
         tf.debugging.assert_greater_equal(
             height, 2, message="Grid height must be at least 2."),
         tf.debugging.assert_greater_equal(

tensorflow_addons/image/dense_image_warp_test.py

@@ -134,7 +134,7 @@ def _check_zero_flow_correctness(self, shape, image_type, flow_type):
 
         self.assertAllClose(rand_image, interp)
 
-    # TODO: run in both graph and eager modes
+    @test_utils.run_in_graph_and_eager_modes
     def test_zero_flows(self):
         """Apply _check_zero_flow_correctness() for a few sizes and types."""
         shapes_to_try = [[3, 4, 5, 6], [1, 2, 2, 1]]