fix bug in leaky_relu for tf_version <= 1.5

Author: mindest

tests/common.py

@@ -14,7 +14,7 @@
 from tf2onnx import constants, logging, utils
 
 __all__ = ["TestConfig", "get_test_config", "unittest_main", "check_onnxruntime_backend",
-           "check_tf_min_version", "skip_tf_versions", "check_onnxruntime_min_version",
+           "check_tf_min_version", "check_tf_max_version", "skip_tf_versions", "check_onnxruntime_min_version",
            "check_opset_min_version", "check_target", "skip_caffe2_backend", "skip_onnxruntime_backend",
            "skip_opset", "check_onnxruntime_incompatibility", "validate_const_node",
            "group_nodes_by_type", "test_ms_domain", "check_node_domain"]

tests/test_backend.py

@@ -799,7 +799,9 @@ def test_relu(self):
 
     @skip_caffe2_backend("fails on caffe2 with dim issue")
     @check_onnxruntime_incompatibility("Mul")
+    @check_tf_min_version("1.6")
     def test_leaky_relu(self):
+        # starting from tf 1.6, leaky_relu supports `feature` x of int type
         x_types = [np.float32, np.int32, np.int64]
         for x_type in x_types:
             x_val = 1000 * np.random.random_sample([1000, 100]).astype(x_type)
@@ -810,6 +812,19 @@ def test_leaky_relu(self):
                 self._run_test_case([_OUTPUT], {_INPUT: x_val})
                 tf.reset_default_graph()
 
+    @skip_caffe2_backend("fails on caffe2 with dim issue")
+    @check_onnxruntime_incompatibility("Mul")
+    @check_tf_max_version("1.5")
+    def test_leaky_relu_old(self):
+        # for tf_version <= 1.5, leaky_relu requires `feature` x to be of type `float32`
+        x_val = 1000 * np.random.random_sample([1000, 100]).astype(np.float32)
+        for alpha in [0.1, -0.1, 1.0, -1.0]:
+            x = tf.placeholder(x_val.dtype, [None] * x_val.ndim, name=_TFINPUT)
+            x_ = tf.nn.leaky_relu(x, alpha)
+            _ = tf.identity(x_, name=_TFOUTPUT)
+            self._run_test_case([_OUTPUT], {_INPUT: x_val})
+            tf.reset_default_graph()
+
     @check_onnxruntime_incompatibility("Elu")
     def test_elu(self):
         x_val = np.array([0.5, 1.0, -0.5, -1.0], dtype=np.float32).reshape((2, 2))