diff --git a/onnxconverter_common/onnx2py.py b/onnxconverter_common/onnx2py.py
new file mode 100644
index 0000000..1deefeb
--- /dev/null
+++ b/onnxconverter_common/onnx2py.py
@@ -0,0 +1,192 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for
+# license information.
+###########################################################################
+
+"""
+Converts onnx model into model.py file for easy editing. Resulting model.py file uses onnx.helper library to
+recreate the original onnx model. Constant tensors with more than 10 elements are saved into .npy
+files in location model/const#_tensor_name.npy
+
+Example usage:
+python -m onnxconverter_common.onnx2py my_model.onnx my_model.py
+"""
+
+import sys
+import onnx
+import collections
+from onnx import helper, numpy_helper, TensorProto
+import numpy as np
+import os
+
+from .pytracing import TracingObject
+
+needed_types = set()
+const_dir = None
+const_counter = None
+
+np_traced = TracingObject("np")
+helper_traced = TracingObject("helper")
+numpy_helper_traced = TracingObject("numpy_helper")
+TensorProtoTraced = TracingObject("TensorProto")
+
+
+def convert_tensor_type(i):
+    return getattr(TensorProtoTraced, TensorProto.DataType.Name(i))
+
+
+def convert_field(field):
+    global needed_types
+    if isinstance(field, (int, str, float, bytes)):
+        return field
+    elif isinstance(field, onnx.GraphProto):
+        return convert_graph(field)
+    elif isinstance(field, onnx.ModelProto):
+        return convert_model(field)
+    elif isinstance(field, onnx.NodeProto):
+        return convert_node(field)
+    elif isinstance(field, onnx.TensorProto):
+        return convert_tensor(field)
+    elif isinstance(field, onnx.ValueInfoProto):
+        return convert_value_info(field)
+    elif isinstance(field, onnx.OperatorSetIdProto):
+        return convert_operatorsetid(field)
+    elif isinstance(field, collections.abc.Iterable):
+        return list(convert_field(x) for x in field)
+    else:
+        # Missing handler needs to be added
+        t = str(type(field))
+        needed_types.add(t)
+        return field
+
+
+def convert_value_info(val_info):
+    name = val_info.name
+    elem_type = convert_tensor_type(val_info.type.tensor_type.elem_type)
+    kwargs = collections.OrderedDict()
+
+    def convert_shape_dim(d):
+        if d.HasField("dim_value"):
+            return d.dim_value
+        if d.HasField("dim_param"):
+            return d.dim_param
+        return None
+
+    def convert_shape_denotation(d):
+        if d.HasField("denotation"):
+            return d.denotation
+        return None
+
+    kwargs["shape"] = [convert_shape_dim(d) for d in val_info.type.tensor_type.shape.dim]
+    if any(d.HasField("denotation") for d in val_info.type.tensor_type.shape.dim):
+        kwargs["shape_denotation"] = [convert_shape_denotation(d) for d in val_info.type.tensor_type.shape.dim]
+
+    if val_info.HasField("doc_string"):
+        kwargs["doc_string"].doc_string
+
+    return helper_traced.make_tensor_value_info(name, elem_type, **kwargs)
+
+
+def convert_operatorsetid(opsetid):
+    domain = opsetid.domain
+    version = opsetid.version
+    return helper_traced.make_operatorsetid(domain, version)
+
+
+def convert_tensor(tensor):
+    global const_dir, const_counter
+    np_data = numpy_helper.to_array(tensor)
+    if np.product(np_data.shape) <= 10:
+        return numpy_helper_traced.from_array(np_data, name=tensor.name)
+    os.makedirs(const_dir, exist_ok=True)
+    name = "const" + str(const_counter)
+    if tensor.name:
+        name = name + "_" + tensor.name
+    for c in '~"#%&*:<>?/\\{|}':
+        name = name.replace(c, '_')
+    const_path = "%s/%s.npy" % (const_dir, name)
+    np.save(const_path, np_data)
+    const_counter += 1
+    return numpy_helper_traced.from_array(np_traced.load(const_path), name=tensor.name)
+
+
+def convert_node(node):
+    fields = {f[0].name: f[1] for f in node.ListFields()}
+    attributes = fields.pop("attribute", [])
+    attrs = {a.name: convert_field(helper.get_attribute_value(a)) for a in attributes}
+    fields = {f: convert_field(v) for f, v in fields.items()}
+    op_type = fields.pop("op_type")
+    if op_type == "Cast" and "to" in attrs:
+        attrs["to"] = convert_tensor_type(attrs["to"])
+    inputs = fields.pop("input", [])
+    outputs = fields.pop("output", [])
+    return helper_traced.make_node(op_type, inputs=inputs, outputs=outputs, **fields, **attrs)
+
+
+def convert_graph(graph):
+    fields = {f[0].name: convert_field(f[1]) for f in graph.ListFields()}
+    nodes = fields.pop("node", [])
+    name = fields.pop("name")
+    inputs = fields.pop("input", [])
+    outputs = fields.pop("output", [])
+    return helper_traced.make_graph(nodes, name=name, inputs=inputs, outputs=outputs, **fields)
+
+
+def convert_model(model):
+    fields = {f[0].name: convert_field(f[1]) for f in model.ListFields()}
+    graph = fields.pop("graph")
+    opset_imports = fields.pop("opset_import", [])
+    return helper_traced.make_model(graph, opset_imports=opset_imports, **fields)
+
+
+def clear_directory(path):
+    for f in os.listdir(path):
+        if f.endswith(".npy"):
+            os.remove(os.path.join(path, f))
+    try:
+        # Delete if empty
+        os.rmdir(path)
+    except OSError:
+        pass
+
+
+class MissingHandlerException(Exception):
+    pass
+
+
+def convert(model, out_path):
+    global needed_types, const_dir, const_counter
+    needed_types = set()
+    if out_path.endswith(".py"):
+        out_path = out_path[:-3]
+    if os.path.exists(out_path):
+        clear_directory(out_path)
+    const_dir = out_path
+    const_counter = 0
+
+    model_trace = convert_model(model)
+    code = "from onnx import helper, numpy_helper, TensorProto\n"
+    code += "import numpy as np\n"
+    code += "\n" + "model = " + repr(model_trace) + "\n"
+    with open(out_path + ".py", "wt") as file:
+        file.write(code)
+    if needed_types:
+        raise MissingHandlerException("Missing handler for types: %s" % list(needed_types))
+
+
+def main():
+    _, in_path, out_path = sys.argv
+    if not out_path.endswith(".py"):
+        out_path = out_path + ".py"
+
+    model = onnx.load(in_path)
+    try:
+        convert(model, out_path)
+    except MissingHandlerException as e:
+        print("ERROR:", e)
+
+    print("Model saved to", out_path)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/onnxconverter_common/pytracing.py b/onnxconverter_common/pytracing.py
new file mode 100644
index 0000000..c0a7d16
--- /dev/null
+++ b/onnxconverter_common/pytracing.py
@@ -0,0 +1,52 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for
+# license information.
+###########################################################################
+
+import numpy as np
+
+
+def indent(s):
+    return "\n".join("    " + line for line in s.split("\n"))
+
+
+class TracingObject:
+    """
+    Used by onnx2py to mock a module like numpy or onnx.helper and record calls on that module
+    Ex:
+        np = TracingObject("np")
+        x = np.array(np.product([1, 2, 3]), np.int32)
+        assert repr(x) == "np.array(np.product([1, 2, 3]), np.int32)"
+    """
+    def __init__(self, trace):
+        self._trace = trace
+
+    @staticmethod
+    def from_repr(o):
+        return TracingObject(TracingObject.get_repr(o))
+
+    @staticmethod
+    def get_repr(x):
+        if isinstance(x, np.ndarray):
+            return "np.array(%r, dtype=np.%s)" % (x.tolist(), x.dtype)
+        if not isinstance(x, list):
+            return repr(x)
+        ls = [TracingObject.get_repr(o) for o in x]
+        code = "[" + ", ".join(ls) + "]"
+        if len(code) <= 200:
+            return code
+        return "[\n" + "".join(indent(s) + ",\n" for s in ls) + "]"
+
+    def __getattr__(self, attr):
+        return TracingObject(self._trace + "." + attr)
+
+    def __call__(self, *args, **kwargs):
+        arg_s = [TracingObject.get_repr(o) for o in args]
+        arg_s += [k + "=" + TracingObject.get_repr(o) for k, o in kwargs.items()]
+        trace = self._trace + "(" + ", ".join(arg_s) + ")"
+        if len(trace) <= 200:
+            return TracingObject(trace)
+        return TracingObject(self._trace + "(\n" + "".join(indent(s) + ",\n" for s in arg_s) + ")")
+
+    def __repr__(self):
+        return self._trace
diff --git a/tests/test_onnx2py.py b/tests/test_onnx2py.py
new file mode 100644
index 0000000..d247cb8
--- /dev/null
+++ b/tests/test_onnx2py.py
@@ -0,0 +1,48 @@
+import unittest
+import numpy as np
+import os
+import onnx
+import onnxruntime as _ort
+import sys
+
+from onnxconverter_common.onnx2py import convert, clear_directory
+
+working_path = os.path.abspath(os.path.dirname(__file__))
+tmp_path = os.path.join(working_path, 'temp')
+data_path = os.path.join(working_path, 'data')
+
+class Oonnx2PyTests(unittest.TestCase):
+    def tearDown(self):
+        for f in os.listdir(tmp_path):
+            if f.endswith(".py"):
+                os.remove(os.path.join(tmp_path, f))
+                folder_path = os.path.join(tmp_path, f[:-3])
+                if os.path.exists(folder_path):
+                    clear_directory(folder_path)
+
+    @unittest.skipIf(sys.version_info < (3, 6), "Requires onnx > 1.3.0")
+    def test_onnx2py(self):
+        global model
+        model_name = 'test_model_1_no_opt'
+        onnx_model = onnx.load(os.path.join(data_path, model_name + '.onnx'))
+        sess1 = _ort.InferenceSession(onnx_model.SerializeToString())
+        np.random.seed(42)
+        data = np.random.random_sample(size=(1, 1, 512)).astype(np.float32)
+        expected = sess1.run(["conv1d_1"], {"input_1": data})
+
+        os.makedirs(tmp_path, exist_ok=True)
+        out_path = os.path.join(tmp_path, model_name + '.py')
+        convert(onnx_model, out_path)
+        self.assertTrue(os.path.exists(out_path))
+        local_map = {}
+        with open(out_path, "rt") as f:
+            # Creates model called 'model'
+            exec(f.read(), None, local_map)
+        model = local_map["model"]
+        sess2 = _ort.InferenceSession(model.SerializeToString())
+        actual = sess2.run(["conv1d_1"], {"input_1": data})
+
+        np.testing.assert_allclose(expected[0], actual[0])
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/tests/test_pytracing.py b/tests/test_pytracing.py
new file mode 100644
index 0000000..9dab0be
--- /dev/null
+++ b/tests/test_pytracing.py
@@ -0,0 +1,21 @@
+import unittest
+import numpy as np
+
+from onnxconverter_common.pytracing import TracingObject
+
+class TracingTests(unittest.TestCase):
+    def test_tracing_simple(self):
+        tracer = TracingObject("x")
+        actual = repr(tracer.func_call([1, 'A', 3, tracer], tracer))
+        expected = "x.func_call([1, 'A', 3, x], x)"
+        self.assertEqual(actual, expected)
+
+    def test_tracing_numpy(self):
+        tracer = TracingObject("helper")
+        x = np.array([1, 2, 3], dtype=np.int32)
+        actual = repr(tracer.from_numpy(x))
+        expected = "helper.from_numpy(np.array([1, 2, 3], dtype=np.int32))"
+        self.assertEqual(actual, expected)
+
+if __name__ == '__main__':
+    unittest.main()