Merge pull request #734 from jignparm/jignparm/fix_optimizer

Fix optimizer for Transpose

Author: guschmue

tests/test_optimizers.py

@@ -7,8 +7,6 @@
 from __future__ import print_function
 from __future__ import unicode_literals
 
-import unittest
-
 import numpy as np
 from onnx import helper, TensorProto, OperatorSetIdProto
 from tf2onnx import utils
@@ -1099,10 +1097,9 @@ def test_transpose_back_to_back_non_const(self):
 
         model_proto = self.make_model(graph, producer_name="onnx-tests")
         self.run_transpose_compare(["res"], {"u": np.random.randn(5, 5, 5, 5).astype(np.float32)},
-                                   model_proto, remaining_transpose_num=2)
+                                   model_proto, remaining_transpose_num=1)
 
-    # @check_opset_min_version(9, "string type tensor")
-    @unittest.skip("FIXME: disabled because of crash on linux/ortnightly")
+    @check_opset_min_version(9, "string type tensor")
     def test_cast_back_to_back_non_const_mixed_types(self):
         node0 = helper.make_node("Cast", ["u"], ["v"], to=11, name="cast_0")  # double
         node1 = helper.make_node("Cast", ["v"], ["w"], to=6, name="cast_1")  # int32
@@ -1113,11 +1110,13 @@ def test_cast_back_to_back_non_const_mixed_types(self):
         node5 = helper.make_node("Cast", ["w2"], ["res2"], to=7, name="cast_5")  # int64
 
         node6 = helper.make_node("Cast", ["x"], ["x2"], to=9, name="cast_6")  # bool
-        node7 = helper.make_node("Cast", ["x2"], ["x3"], to=8, name="cast_7")  # string
-        node8 = helper.make_node("Cast", ["x3"], ["res3"], to=3, name="cast_8")  # int8
+        # TODO: uncomment below after fix
+        # https://github.com/microsoft/onnxruntime/issues/2338
+        # node7 = helper.make_node("Cast", ["x2"], ["x3"], to=8, name="cast_7")  # string
+        node8 = helper.make_node("Cast", ["x2"], ["res3"], to=3, name="cast_8")  # int8
 
         graph = helper.make_graph(
-            [node0, node1, node2, node3, node4, node5, node6, node7, node8],
+            [node0, node1, node2, node3, node4, node5, node6, node8],
             "test-cast-back-to-back-non-const",
             [helper.make_tensor_value_info("u", TensorProto.FLOAT, (1, 2, 3))],
             [helper.make_tensor_value_info("res", TensorProto.INT64, (1, 2, 3)),

tf2onnx/optimizer/back_to_back_optimizer.py

@@ -121,9 +121,8 @@ def _optimize_cast(g, node, consumer_nodes):
             g.remove_node(node.name)
         return q2
 
-    # TODO: reactivate after fixing interference with transpose_optimizer
     @staticmethod
-    @_register_func("_Transpose")
+    @_register_func("Transpose")
     def _optimize_transpose(g, node, consumer_nodes):
         t1 = list(node.get_attr('perm').ints)
         q2 = []
@@ -132,13 +131,16 @@ def _optimize_transpose(g, node, consumer_nodes):
             t2 = list(node2.get_attr('perm').ints)
             new_perm = [t1[i] for i in t2]
             # check if node2 can be removed. otherwise only update
-            if new_perm == list(range(len(t2))) \
-                    and not set(node2.output) & set(g.outputs):
+            if new_perm == list(range(len(t2))):
                 # both nodes can be deleted
+                shape = g.get_shape(node2.output[0])
+                dtype = g.get_dtype(node2.output[0])
                 node2_consumers = g.find_output_consumers(node2.output[0])
-                for consumer in node2_consumers:
-                    consumer.input[0] = node.input[0]
+                g.replace_all_inputs(node2_consumers, node2.output[0], node.input[0])
                 g.remove_node(node2.name)
+                if set(node2.output) & set(g.outputs):
+                    g.make_node("Identity", [node.input[0]],
+                                outputs=node2.output, shapes=[shape], dtypes=[dtype])
             else:
                 node2.set_attr('perm', [t1[i] for i in t2])
                 q2.append(node2.output[0])