Implement function retrieval APIs; Add documentation for functions

CMakeLists.txt

@@ -30,6 +30,8 @@ list(APPEND CMAKE_MODULE_PATH ${ONNX_ROOT}/cmake/Modules)
 if(NOT MSVC)
   set(CMAKE_C_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -O0")
   set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -O0")
+  set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -pthread")
+  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread")
   if(ONNX_COVERAGE)
     set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fprofile-arcs -ftest-coverage")
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fprofile-arcs -ftest-coverage")
@@ -361,6 +363,7 @@ if (MSVC)
         /wd4800 # disable warning type' : forcing value to bool 'true' or 'false' (performance warning)
         /wd4503 # identifier' : decorated name length exceeded, name was truncated
         /wd4146 # unary minus operator applied to unsigned type, result still unsigned
+        /wd4244 # conversion from 'protobuf::int64' to 'int'
         ${EXTRA_FLAGS}
     )
     if(${ONNX_USE_MSVC_STATIC_RUNTIME})

cmake/unittest.cmake

@@ -19,10 +19,10 @@ set(${UT_NAME}_libs
     ${googletest_STATIC_LIBRARIES}
 )
 
-list(APPEND ${UT_NAME}_libs ${PROTOBUF_LIBRARIES})
-list(APPEND ${UT_NAME}_libs onnx_proto)
 add_whole_archive_flag(onnx tmp)
 list(APPEND ${UT_NAME}_libs ${tmp})
+list(APPEND ${UT_NAME}_libs onnx_proto)
+list(APPEND ${UT_NAME}_libs ${PROTOBUF_LIBRARIES})
 
 file(GLOB_RECURSE ${UT_NAME}_src
     "${ONNX_ROOT}/onnx/test/c++/*.cc"

docs/Functions.md

@@ -0,0 +1,36 @@
+## Functions
+*This file is automatically generated from the
+            [def files](/onnx/defs) via [this script](/onnx/defs/gen_doc.py).
+            Do not modify directly and instead edit function definitions.*
+## ai.onnx (default)
+  * <sub>experimental</sub><a href="#FuncMeanVarianceNormalization">FuncMeanVarianceNormalization</a>
+
+
+
+### <sub>experimental</sub> <a name="FuncMeanVarianceNormalization"></a><a name="funcmeanvariancenormalization">**FuncMeanVarianceNormalization**</a>
+
+  A MeanVarianceNormalization Function: Perform mean variance normalization on the input tensor X using formula: <br/> ``` (X-EX)/sqrt(E(X-EX)^2) ``` <br/><br/><b>INPUT: </b>X(float/float16/double) with shape [N,C,W,H] or N-D shape <br/><br/><b>ATTRIBUTE: </b><br/>&nbsp;&nbsp;&nbsp;&nbsp;<tt>axes: </tt>will be passed to ReducedMean Ops. Use [0,2,3] (without C axis for N-D cases) for for calculating means and variances along channels. Two variables with the same C-coordinate are associated with the same mean and variance. Use [0,1,2,3] (with C axis) to calculate global mean and global variance with all variables sharing the same mean/variance.<br/>&nbsp;&nbsp;&nbsp;&nbsp;(The KeepDims attribute in ReducedMean is set to true for calculation)<br/><br/><b>OUTPUT: </b>X_MVN(float/float16/double) with shape [N,C,W,H] or the input N-D shape <br/>
+
+#### Version
+
+This version of the function has been available since version 8 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt>X; </dt>
+<br/></dl>
+
+#### Outputs
+
+<dl>
+<dt>X_MVN; </dt>
+<br/></dl>
+
+#### Attributes
+
+<dl>
+<dt>axes;<br/></dt>
+</dl>
+
+

docs/FunctionsChangelog.md

@@ -0,0 +1,32 @@
+## Function Changelog
+*This file is automatically generated from the
+            [def files](/onnx/defs) via [this script](/onnx/defs/gen_doc.py).
+            Do not modify directly and instead edit function definitions.*
+# ai.onnx (default)
+## Version 8 of domain ai.onnx (default)
+### <a name="FuncMeanVarianceNormalization-8"></a>**FuncMeanVarianceNormalization-8**</a>
+
+  A MeanVarianceNormalization Function: Perform mean variance normalization on the input tensor X using formula: <br/> ``` (X-EX)/sqrt(E(X-EX)^2) ``` <br/><br/><b>INPUT: </b>X(float/float16/double) with shape [N,C,W,H] or N-D shape <br/><br/><b>ATTRIBUTE: </b><br/>&nbsp;&nbsp;&nbsp;&nbsp;<tt>axes: </tt>will be passed to ReducedMean Ops. Use [0,2,3] (without C axis for N-D cases) for for calculating means and variances along channels. Two variables with the same C-coordinate are associated with the same mean and variance. Use [0,1,2,3] (with C axis) to calculate global mean and global variance with all variables sharing the same mean/variance.<br/>&nbsp;&nbsp;&nbsp;&nbsp;(The KeepDims attribute in ReducedMean is set to true for calculation)<br/><br/><b>OUTPUT: </b>X_MVN(float/float16/double) with shape [N,C,W,H] or the input N-D shape <br/>
+
+#### Version
+
+This version of the function has been available since version 8 of the default ONNX operator set.
+
+#### Inputs
+
+<dl>
+<dt>X; </dt>
+<br/></dl>
+
+#### Outputs
+
+<dl>
+<dt>X_MVN; </dt>
+<br/></dl>
+
+#### Attributes
+
+<dl>
+<dt>axes;<br/></dt>
+</dl>
+

onnx/backend/test/case/node/mvn.py

@@ -0,0 +1,43 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import numpy as np  # type: ignore
+
+import onnx
+from ..base import Base
+from . import expect
+
+
+class MVN(Base):
+
+    @staticmethod
+    def export():  # type: () -> None
+        node = onnx.helper.make_node(
+            'FuncMeanVarianceNormalization',
+            inputs=['X'],
+            outputs=['Y'],
+            axes=[0, 2, 3]
+        )
+
+        input_data = np.array([[[[0.8439683], [0.5665144], [0.05836735]],
+            [[0.02916367], [0.12964272], [0.5060197]],
+            [[0.79538304], [0.9411346], [0.9546573]]],
+            [[[0.17730942], [0.46192095], [0.26480448]],
+            [[0.6746842], [0.01665257], [0.62473077]],
+            [[0.9240844], [0.9722341], [0.11965699]]],
+            [[[0.41356155], [0.9129373], [0.59330076]],
+            [[0.81929934], [0.7862604], [0.11799799]],
+            [[0.69248444], [0.54119414], [0.07513223]]]], dtype=np.float32)
+
+        # Calculate expected output data
+        data_mean = np.mean(input_data, axis=(0, 2, 3), keepdims=1)
+        data_mean_squared = np.power(data_mean, 2)
+        data_squared = np.power(input_data, 2)
+        data_squared_mean = np.mean(data_squared, axis=(0, 2, 3), keepdims=1)
+        std = np.sqrt(data_squared_mean - data_mean_squared)
+        expected_output = (input_data - data_mean) / (std + 1e-9)
+
+        expect(node, inputs=[input_data], outputs=[expected_output],
+               name='test_mvn')

onnx/common/model_helpers.cc

@@ -0,0 +1,39 @@
+// Copyright (c) Facebook Inc. and Microsoft Corporation.
+// Licensed under the MIT license.
+
+#include "onnx/common/model_helpers.h"
+#include "onnx/checker.h"
+#include "onnx/defs/schema.h"
+#include "onnx/string_utils.h"
+
+namespace ONNX_NAMESPACE {
+using namespace Common;
+
+Common::Status BuildNode(
+    NodeProto* node,
+    const std::string& name,
+    const std::string& domain,
+    const std::string& doc_string,
+    const std::string& op_type,
+    std::vector<std::string> const& inputs,
+    std::vector<std::string> const& outputs) {
+  if (node == NULL) {
+    return Status(
+        Common::CHECKER,
+        Common::INVALID_ARGUMENT,
+        "node_proto should not be nullptr.");
+  }
+  node->set_name(name);
+  node->set_domain(domain);
+  node->set_doc_string(doc_string);
+  node->set_op_type(op_type);
+  for (auto& input : inputs) {
+    node->add_input(input);
+  }
+  for (auto& output : outputs) {
+    node->add_output(output);
+  }
+
+  return Status::OK();
+}
+} // namespace ONNX_NAMESPACE

onnx/common/model_helpers.h

@@ -0,0 +1,24 @@
+// Copyright (c) Facebook Inc. and Microsoft Corporation.
+// Licensed under the MIT license.
+
+#pragma once
+
+#include <string>
+#include <vector>
+#include "onnx/common/status.h"
+#include "onnx/onnx-operators_pb.h"
+
+namespace ONNX_NAMESPACE {
+
+// Helper function for register nodes in
+// a FunctionProto. Attributes need to be
+// registered separately.
+Common::Status BuildNode(
+    NodeProto* node,
+    const std::string& name,
+    const std::string& domain,
+    const std::string& doc_string,
+    const std::string& op_type,
+    std::vector<std::string> const& inputs,
+    std::vector<std::string> const& outputs);
+} // namespace ONNX_NAMESPACE

onnx/cpp2py_export.cc

@@ -5,6 +5,7 @@
 #include <unordered_map>
 
 #include "onnx/checker.h"
+#include "onnx/defs/function.h"
 #include "onnx/defs/schema.h"
 #include "onnx/optimizer/optimize.h"
 #include "onnx/py_utils.h"
@@ -96,6 +97,57 @@ PYBIND11_MODULE(onnx_cpp2py_export, onnx_cpp2py_export) {
       .value("COMMON", OpSchema::SupportType::COMMON)
       .value("EXPERIMENTAL", OpSchema::SupportType::EXPERIMENTAL);
 
+  py::class_<FunctionProto> function_proto(defs, "FunctionProto");
+  function_proto.def_property_readonly("name", &FunctionProto::name)
+      .def_property_readonly("doc_string", &FunctionProto::doc_string)
+      .def_property_readonly("since_version", &FunctionProto::since_version)
+      .def_property_readonly(
+          "inputs",
+          [](FunctionProto* fp) -> std::vector<std::string> {
+            std::vector<std::string> _stl_vec;
+            _stl_vec.assign(fp->input().begin(), fp->input().end());
+            return _stl_vec;
+          })
+      .def_property_readonly(
+          "outputs",
+          [](FunctionProto* fp) -> std::vector<std::string> {
+            std::vector<std::string> _stl_vec;
+            _stl_vec.assign(fp->output().begin(), fp->output().end());
+            return _stl_vec;
+          })
+      .def_property_readonly(
+          "attribute",
+          [](FunctionProto* fp) -> std::vector<std::string> {
+            std::vector<std::string> _stl_vec;
+            _stl_vec.assign(fp->attribute().begin(), fp->attribute().end());
+            return _stl_vec;
+          })
+      .def_property_readonly(
+          "nodes", [](FunctionProto* fp) -> std::vector<NodeProto> {
+            std::vector<NodeProto> _stl_vec;
+            _stl_vec.assign(fp->node().begin(), fp->node().end());
+            return _stl_vec;
+          });
+
+  py::class_<NodeProto> node_proto(function_proto, "NodeProto");
+  node_proto.def_property_readonly("name", &NodeProto::name)
+      .def_property_readonly("doc_string", &NodeProto::doc_string)
+      .def_property_readonly("domain", &NodeProto::domain)
+      .def_property_readonly("op_type", &NodeProto::op_type)
+      .def_property_readonly(
+          "inputs",
+          [](NodeProto* np) -> std::vector<std::string> {
+            std::vector<std::string> _stl_vec;
+            _stl_vec.assign(np->input().begin(), np->input().end());
+            return _stl_vec;
+          })
+      .def_property_readonly(
+          "outputs", [](NodeProto* np) -> std::vector<std::string> {
+            std::vector<std::string> _stl_vec;
+            _stl_vec.assign(np->output().begin(), np->output().end());
+            return _stl_vec;
+          });
+
   defs.def(
       "has_schema",
       [](const std::string& op_type, const std::string& domain) -> bool {
@@ -146,6 +198,32 @@ PYBIND11_MODULE(onnx_cpp2py_export, onnx_cpp2py_export) {
     return OpSchemaRegistry::get_all_schemas_with_history();
   });
 
+  defs.def(
+      "get_all_functions",
+      [](const std::string& domain)
+          -> std::unordered_map<std::string, std::vector<FunctionProto>> {
+        // Eliminate unsupported stl containers and smart pointers for Pybind
+        // and return a dict[Text, [FunctionProto]] datatype to Python API
+        std::multimap<std::string, std::unique_ptr<FunctionProto>> temp_ptr_map;
+        std::unordered_map<std::string, std::vector<FunctionProto>> temp_map;
+        FunctionBuilderRegistry& function_registry =
+            FunctionBuilderRegistry::OnnxInstance();
+        Common::Status status =
+            function_registry.GetFunctions(domain, &temp_ptr_map);
+        for (auto iter = temp_ptr_map.begin(); iter != temp_ptr_map.end();
+             ++iter)
+          if (!temp_map.count(iter->first)) {
+            std::vector<FunctionProto> tmp_vec;
+            tmp_vec.emplace_back(*iter->second);
+            temp_map.insert(
+                std::unordered_map<std::string, std::vector<FunctionProto>>::
+                    value_type(iter->first, tmp_vec));
+          } else {
+            temp_map.at(iter->first).emplace_back(*iter->second);
+          }
+        return temp_map;
+      });
+
   // Submodule `checker`
   auto checker = onnx_cpp2py_export.def_submodule("checker");
   checker.doc() = "Checker submodule";

onnx/defs/__init__.py

@@ -32,3 +32,10 @@ def _Attribute_default_value(self):  # type: ignore
 
 
 OpSchema.Attribute.default_value = _Attribute_default_value  # type: ignore
+
+
+FunctionProto = C.FunctionProto  # type: ignore
+
+
+def get_functions(domain=ONNX_DOMAIN):  # type: ignore
+    return C.get_all_functions(domain)  # type: ignore