add unittest

Author: lucienwang1009

tests/test_backend.py

@@ -925,6 +925,22 @@ def test_leaky_relu_int(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")
+    def test_leaky_relu_with_dependency(self):
+        x_val = 1000 * np.random.random_sample([1000, 100]).astype(np.float32)
+        x = tf.placeholder(x_val.dtype, [None] * x_val.ndim, name=_TFINPUT)
+        # simulate leaky_relu
+        alpha = tf.constant(0.5)
+        y = alpha * x
+        x_ = tf.maximum(y, x)
+        dependency = y - 1
+
+        _ = tf.identity(x_, name=_TFOUTPUT)
+        _ = tf.identity(dependency, name=_TFOUTPUT1)
+        self._run_test_case([_OUTPUT, _OUTPUT1], {_INPUT: x_val})
+        tf.reset_default_graph()
+
     @skip_caffe2_backend("fails on caffe2 with dim issue")
     @check_onnxruntime_incompatibility("Mul")
     def test_leaky_relu_float(self):

tests/test_loops.py

@@ -8,9 +8,10 @@
 from __future__ import print_function
 from __future__ import unicode_literals
 
+import unittest
+
 import numpy as np
 import tensorflow as tf
-import unittest
 
 from backend_test_base import Tf2OnnxBackendTestBase
 from common import unittest_main, check_tf_min_version

tf2onnx/graph.py

@@ -1183,16 +1183,12 @@ def delete_unused_nodes(self, outputs_name):
 
     def delete_nodes_without_dependency(self, to_delete):
         """Delete nodes in `to_delete` without third-party dependency."""
+        delete_set = set(to_delete)
         for n in to_delete:
-            can_delete = True
+            out_consumers = set()
             for out in n.output:
-                if not can_delete:
-                    break
-                for consumer in self.find_output_consumers(out):
-                    if consumer not in to_delete:
-                        can_delete = False
-                        break
-            if can_delete:
+                out_consumers |= set(self.find_output_consumers(out))
+            if out_consumers.issubset(delete_set):
                 self.remove_node(n.name)