Fixing for TopKV2 shape propagation in Toco.

PiperOrigin-RevId: 207917321

1 files changed, 14 insertions, 5 deletions

diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py
index 73c05e21b6..1dfd086f5a 100644
--- a/tensorflow/contrib/lite/testing/generate_examples.py
+++ b/tensorflow/contrib/lite/testing/generate_examples.py
@@ -1670,7 +1670,7 @@ def make_shape_tests(zip_path):
   }]
 
   def build_graph(parameters):
-    """Build the topk op testing graph."""
+    """Build the shape op testing graph."""
     # Note that we intentionally leave out the shape from the input placeholder
     # to prevent the Shape operation from being optimized out during conversion.
     input_value = tf.placeholder(dtype=parameters["input_dtype"], name="input")
@@ -2318,6 +2318,7 @@ def make_topk_tests(zip_path):
   test_parameters = [{
       "input_dtype": [tf.float32, tf.int32],
       "input_shape": [[10], [5, 20]],
+      "input_k": [None, 1, 3],
   }]
 
   def build_graph(parameters):
@@ -2326,15 +2327,23 @@ def make_topk_tests(zip_path):
         dtype=parameters["input_dtype"],
         name="input",
         shape=parameters["input_shape"])
-    k = tf.constant(3, name="k")
+    if parameters["input_k"] is not None:
+      k = tf.placeholder(dtype=tf.int32, name="input_k", shape=[])
+    else:
+      k = tf.constant(3, name="k")
     out = tf.nn.top_k(input_value, k)
-    return [input_value], [out[1]]
+    return [input_value, k], [out[1]]
 
   def build_inputs(parameters, sess, inputs, outputs):
     input_value = create_tensor_data(parameters["input_dtype"],
                                      parameters["input_shape"])
-    return [input_value], sess.run(
-        outputs, feed_dict=dict(zip(inputs, [input_value])))
+    if parameters["input_k"] is not None:
+      k = np.array(parameters["input_k"], dtype=np.int32)
+      return [input_value, k], sess.run(
+          outputs, feed_dict=dict(zip(inputs, [input_value, k])))
+    else:
+      return [input_value], sess.run(
+          outputs, feed_dict=dict(zip(inputs, [input_value])))
 
   make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs)