[WIP] Improve performance by adding forward indexes, second version.

tests/backend_test_base.py

@@ -20,6 +20,7 @@
 import numpy as np
 import tensorflow as tf
 from tensorflow.python.ops import variables as variables_lib
+import onnx
 from common import get_test_config
 from tf2onnx import utils
 from tf2onnx.tfonnx import process_tf_graph
@@ -63,12 +64,21 @@ def run_onnxcaffe2(self, onnx_graph, inputs):
     def run_onnxruntime(self, model_path, inputs, output_names):
         """Run test against onnxruntime backend."""
         import onnxruntime as rt
+        try:
+            from onnxruntime.capi.onnxruntime_pybind11_state import Fail
+        except ImportError:
+            Fail = RuntimeError
         opt = rt.SessionOptions()
         # in case of issues with the runtime, one can enable more logging
         # opt.log_severity_level = 0
         # opt.log_verbosity_level = 255
         # opt.enable_profiling = True
-        m = rt.InferenceSession(model_path, opt)
+        try:
+            m = rt.InferenceSession(model_path, opt)
+        except Fail as e:
+            with open(model_path, 'rb') as f:
+                onx = onnx.load(f)
+            raise AssertionError("Unable to load model '{}'\n{}".format(model_path, onx)) from e
         results = m.run(output_names, inputs)
         return results
 

tests/test_optimizers.py

@@ -912,7 +912,6 @@ def test_duplicated_duplicated_attributes(self):
                                                 op_type="ReduceSum", remaining_op_num=2)
 
     def _check_initializer_num(self, graph_proto, num):
-        print(len(graph_proto.initializer))
         return num == len(graph_proto.initializer)
 
     def test_duplicated_duplicated_constant(self):

tf2onnx/constants.py

@@ -15,7 +15,7 @@
 MICROSOFT_DOMAIN = "com.microsoft"
 
 # Default opset version for onnx domain
-PREFERRED_OPSET = 8
+PREFERRED_OPSET = 11
 
 # Default opset for custom ops
 TENSORFLOW_OPSET = helper.make_opsetid("ai.onnx.converters.tensorflow", 1)

tf2onnx/custom_opsets/ms.py

@@ -85,8 +85,8 @@ def version_1(cls, ctx, node, **kwargs):
         # set node's attrs, Note: output_padding, group are left default.
         conv_dims_attr(node, "dilations")
         # set node's inputs from (output_shape, filter, input_tensor) to (input_tensor, filter, pads, Bias)
-        node.input[0] = node.input[2]
-        node.input[2] = pads.output[0]
+        ctx.replace_input(node, node.input[0], node.input[2], 0)
+        ctx.replace_input(node, node.input[2], pads.output[0], 2)
         conv_convert_inputs(ctx, node, with_kernel=True)
         node.attr.pop("data_format")
         node.attr.pop("padding")