From 3a9de9057913928c9489ed7eb6ba8e72b8457f82 Mon Sep 17 00:00:00 2001
From: "Joshua Z. Zhang" <cheungchih@gmail.com>
Date: Mon, 20 May 2019 10:07:32 -0700
Subject: [PATCH] [Relay][ONNX] fix #3134 converter where initializers were not
 registered as nodes (#3143)

---
 python/tvm/relay/frontend/onnx.py | 17 ++++++++++++++++-
 1 file changed, 16 insertions(+), 1 deletion(-)

diff --git a/python/tvm/relay/frontend/onnx.py b/python/tvm/relay/frontend/onnx.py
index 470f4197c908..c70f5aba39fe 100644
--- a/python/tvm/relay/frontend/onnx.py
+++ b/python/tvm/relay/frontend/onnx.py
@@ -934,7 +934,7 @@ def __init__(self, shape, dtype):
         self._renames = {}
         self._num_input = 0
         self._num_param = 0
-        self._shape = shape
+        self._shape = shape if shape else {}
         self._dtype = dtype
 
     def from_onnx(self, graph, opset):
@@ -966,6 +966,9 @@ def from_onnx(self, graph, opset):
             if not init_tensor.name.strip():
                 raise ValueError("Tensor's name is required.")
             self._params[init_tensor.name] = self._parse_array(init_tensor)
+            self._nodes[init_tensor.name] = new_var(init_tensor.name,
+                                                    shape=self._params[init_tensor.name].shape,
+                                                    dtype=self._params[init_tensor.name].dtype)
         for i in graph.input:
             # from onnx v0.2, GraphProto.input has type ValueInfoProto,
             #  and the name is 'i.name'
@@ -1179,6 +1182,18 @@ def from_onnx(model,
     params : dict of str to tvm.NDArray
         The parameter dict to be used by relay
     """
+    try:
+        import onnx
+        if hasattr(onnx.checker, 'check_model'):
+            # try use onnx's own model checker before converting any model
+            try:
+                onnx.checker.check_model(model)
+            except onnx.onnx_cpp2py_export.checker.ValidationError as e:
+                import warnings
+                # the checker is a bit violent about errors, so simply print warnings here
+                warnings.warn(str(e))
+    except ImportError:
+        pass
     g = GraphProto(shape, dtype)
     graph = model.graph
     try: