* Add nGraph function serialization.
* Turn of execption throwing on failed visitation.
* CNNNetworkNgrapImpl serialize also support fallback to v7 serialization.
* Add error message for legacy IR not implemented case.
* Store tests models in files.
* Add tests with multiple layers.
* Style aligned to IE rules.
* Add visit_attributes to ExecutionNode.
* Layer version XML atribute implementation.
* Fix opset atribute creation for ExecutionGraph. Refactoring.
* Add missing header.
* Move opset collecting to private scope.
* Add missing header.
* Add test wit multiple oututs. Fix found issues: constant name, result
outputs.
* Move serialization to transformation library.
* Add versioning to serialization transformation.
* Add functional tests with ONNX importer path.
* Add nodes unique name checking and correction.
* Add checks for unsuported cases: dynamic shapes & GenericIE node
* General refactoring.
* Add comment describing type name translations.
* Add serialization deterministicity tests.
It's needed to ensure that subsequent calls to serialize() on the same function are giving the same results.
* Serialization in CNNNetworkNGraphImpl::serialize executed via pass::Manager.
Co-authored-by: Gleb Kazantaev <gleb.nnstu@gmail.com>
* NGRAPH_CHECK messages refactored.
* Performance and const correctness refactoring.
* Style formatting applied.
* Code simplifaction.
* Serialize transformation documentation refactoring.
* Changed compare_function() to throw on functions with multiple outputs.
Before this check was implemented with assert which means it was working
only in debug builds. Now it is working also in release build.
* Adjust tests to new compare_functions() behaviour.
* Replace cmakes add_definitions with more modern target_compile_definitions
Co-authored-by: Gleb Kazantaev <gleb.nnstu@gmail.com>
${CMAKE_CURRENT_SOURCE_DIR}/ie_rtti.cpp
${CMAKE_CURRENT_SOURCE_DIR}/precision_utils.cpp
${CMAKE_CURRENT_SOURCE_DIR}/shape_infer/ie_built_in_holder.cpp
- ${CMAKE_CURRENT_SOURCE_DIR}/network_serializer.cpp
- ${CMAKE_CURRENT_SOURCE_DIR}/network_serializer.hpp
${CMAKE_CURRENT_SOURCE_DIR}/system_allocator.cpp
${CMAKE_CURRENT_SOURCE_DIR}/system_allocator.hpp)
#include <transformations/utils/utils.hpp>
#include <transformations/smart_reshape/set_batch_size.hpp>
#include <transformations/smart_reshape/smart_reshape.hpp>
+#include "transformations/serialize.hpp"
#include <legacy/transformations/convert_opset1_to_legacy/convert_one_hot_to_one_hot_ie.hpp>
#include <legacy/ie_ngraph_utils.hpp>
#include "exec_graph_info.hpp"
#include "ie_itt.hpp"
-#include "network_serializer.hpp"
#include "generic_ie.hpp"
#include "shape_infer/ie_built_in_holder.hpp"
}
}
-StatusCode CNNNetworkNGraphImpl::serialize(const std::string& xmlPath, const std::string& binPath,
+StatusCode CNNNetworkNGraphImpl::serialize(const std::string& xmlPath,
+ const std::string& binPath,
ResponseDesc* resp) const noexcept {
- auto network = cnnNetwork;
- if (!network) {
- // TODO: once Serialization::SerializeV10 supports true IR v10
- // remove this conversion and WA for execution graph
- try {
- bool isExecutionGraph = true;
- for (const auto & op : _ngraph_function->get_ops()) {
- auto & rtInfo = op->get_rt_info();
- if (rtInfo.find(ExecGraphInfoSerialization::PERF_COUNTER) == rtInfo.end()) {
- isExecutionGraph = false;
- break;
- }
- }
- if (isExecutionGraph) {
- Serialization::SerializeV10(xmlPath, binPath, (InferenceEngine::ICNNNetwork&)*this);
- return OK;
- }
-
+ try {
+ if (getFunction()) {
+ ngraph::pass::Manager manager;
+ manager.register_pass<ngraph::pass::Serialize>(xmlPath, binPath);
+ manager.run_passes(_ngraph_function);
+ } else {
#ifdef ENABLE_V7_SERIALIZE
- network = std::make_shared<details::CNNNetworkImpl>(*this);
+ auto network = std::make_shared<details::CNNNetworkImpl>(*this);
+ return network->serialize(xmlPath, binPath, resp);
+#else
+ return DescriptionBuffer(NOT_IMPLEMENTED, resp)
+ << "The serialization of legacy IR is not implemented";
#endif
- } catch (const InferenceEngineException& e) {
- return DescriptionBuffer(GENERAL_ERROR, resp) << e.what();
- } catch (const std::exception& e) {
- return DescriptionBuffer(UNEXPECTED, resp) << e.what();
- } catch (...) {
- return DescriptionBuffer(UNEXPECTED, resp);
}
+ } catch (const InferenceEngineException& e) {
+ return DescriptionBuffer(GENERAL_ERROR, resp) << e.what();
+ } catch (const std::exception& e) {
+ return DescriptionBuffer(UNEXPECTED, resp) << e.what();
+ } catch (...) {
+ return DescriptionBuffer(UNEXPECTED, resp);
}
-
-#ifdef ENABLE_V7_SERIALIZE
- return network->serialize(xmlPath, binPath, resp);
-#else
- return DescriptionBuffer(NOT_IMPLEMENTED, resp) << "The serialize for IR v10 is not implemented";
-#endif
+ return OK;
}
StatusCode CNNNetworkNGraphImpl::setBatchSize(size_t size, ResponseDesc* responseDesc) noexcept {
+++ /dev/null
-// Copyright (C) 2018-2020 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "network_serializer.hpp"
-
-#include <map>
-#include <deque>
-#include <string>
-#include <vector>
-
-#include "exec_graph_info.hpp"
-#include "xml_parse_utils.h"
-#include <legacy/ie_ngraph_utils.hpp>
-#include <ngraph/variant.hpp>
-#include <ngraph/function.hpp>
-
-namespace InferenceEngine {
-namespace Serialization {
-
-namespace {
-
-void FillXmlDocWithExecutionNGraph(const InferenceEngine::ICNNNetwork& network,
- pugi::xml_document& doc) {
- std::shared_ptr<const ngraph::Function> function = network.getFunction();
- if (function == nullptr) {
- THROW_IE_EXCEPTION << network.getName() << " does not represent ngraph::Function";
- }
-
- std::vector<std::shared_ptr<ngraph::Node>> ordered = function->get_ordered_ops();
- pugi::xml_node netXml = doc.append_child("net");
- netXml.append_attribute("name").set_value(network.getName().c_str());
-
- pugi::xml_node layers = netXml.append_child("layers");
- std::unordered_map<std::shared_ptr<ngraph::Node>, size_t> matching;
-
- for (size_t i = 0; i < ordered.size(); ++i) {
- matching[ordered[i]] = i;
- const std::shared_ptr<ngraph::Node> node = ordered[i];
- auto params = node->get_rt_info();
-
- auto layerTypeVariant = params.find(ExecGraphInfoSerialization::LAYER_TYPE);
- if (layerTypeVariant == params.end()) {
- THROW_IE_EXCEPTION << node->get_friendly_name() << " does not define "
- << ExecGraphInfoSerialization::LAYER_TYPE << " attribute.";
- }
- using VariantString = ngraph::VariantImpl<std::string>;
- auto layerTypeValueStr = std::dynamic_pointer_cast<VariantString>(layerTypeVariant->second);
- IE_ASSERT(layerTypeValueStr != nullptr);
- params.erase(layerTypeVariant);
-
- pugi::xml_node layer = layers.append_child("layer");
- layer.append_attribute("name").set_value(node->get_friendly_name().c_str());
- layer.append_attribute("type").set_value(layerTypeValueStr->get().c_str());
- layer.append_attribute("id").set_value(i);
-
- if (!params.empty()) {
- pugi::xml_node data = layer.append_child("data");
-
- for (const auto& it : params) {
- if (auto strValue = std::dynamic_pointer_cast<VariantString>(it.second))
- data.append_attribute(it.first.c_str()).set_value(strValue->get().c_str());
- }
- }
-
- if (node->get_input_size() > 0) {
- pugi::xml_node input = layer.append_child("input");
-
- for (size_t iport = 0; iport < node->get_input_size(); iport++) {
- const ngraph::Shape & dims = node->get_input_shape(iport);
- pugi::xml_node port = input.append_child("port");
-
- port.append_attribute("id").set_value(iport);
- for (auto dim : dims) {
- port.append_child("dim").text().set(dim);
- }
- }
- }
- if (node->get_output_size() > 0 &&
- // ngraph::op::Result still have single output while we should not print it
- !std::dynamic_pointer_cast<ngraph::op::Result>(node)) {
- pugi::xml_node output = layer.append_child("output");
-
- for (size_t oport = 0; oport < node->get_output_size(); oport++) {
- pugi::xml_node port = output.append_child("port");
- Precision outputPrecision = details::convertPrecision(node->get_output_element_type(oport));
-
- port.append_attribute("id").set_value(node->get_input_size() + oport);
- port.append_attribute("precision").set_value(outputPrecision.name());
-
- for (const auto dim : node->get_output_shape(oport)) {
- port.append_child("dim").text().set(dim);
- }
- }
- }
- }
-
- pugi::xml_node edges = netXml.append_child("edges");
-
- for (const auto& ord : ordered) {
- const std::shared_ptr<ngraph::Node> parentNode = ord;
-
- if (parentNode->get_output_size() > 0) {
- auto itFrom = matching.find(parentNode);
- if (itFrom == matching.end()) {
- THROW_IE_EXCEPTION << "Internal error, cannot find " << parentNode->get_friendly_name()
- << " in matching container during serialization of IR";
- }
- for (size_t oport = 0; oport < parentNode->get_output_size(); oport++) {
- ngraph::Output<ngraph::Node> parentPort = parentNode->output(oport);
- for (const auto& childPort : parentPort.get_target_inputs()) {
- ngraph::Node * childNode = childPort.get_node();
- for (int iport = 0; iport < childNode->get_input_size(); iport++) {
- if (childNode->input_value(iport).get_node() == parentPort.get_node()) {
- auto itTo = matching.find(childNode->shared_from_this());
- if (itTo == matching.end()) {
- THROW_IE_EXCEPTION << "Broken edge form layer "
- << parentNode->get_friendly_name() << " to layer "
- << childNode->get_friendly_name()
- << "during serialization of IR";
- }
- pugi::xml_node edge = edges.append_child("edge");
- edge.append_attribute("from-layer").set_value(itFrom->second);
- edge.append_attribute("from-port").set_value(oport + parentNode->get_input_size());
-
- edge.append_attribute("to-layer").set_value(itTo->second);
- edge.append_attribute("to-port").set_value(iport);
- }
- }
- }
- }
- }
- }
-}
-
-} // namespace
-
-void SerializeV10(const std::string& xmlPath, const std::string& binPath,
- const InferenceEngine::ICNNNetwork& network) {
- if (auto function = network.getFunction()) {
- // A flag for serializing executable graph information (not complete IR)
- bool execGraphInfoSerialization = true;
-
- // go over all operations and check whether performance stat is set
- for (const auto & op : function->get_ops()) {
- auto & rtInfo = op->get_rt_info();
- if (rtInfo.find(ExecGraphInfoSerialization::PERF_COUNTER) == rtInfo.end()) {
- execGraphInfoSerialization = false;
- break;
- }
- }
-
- if (execGraphInfoSerialization) {
- pugi::xml_document doc;
- FillXmlDocWithExecutionNGraph(network, doc);
-
- if (!doc.save_file(xmlPath.c_str())) {
- THROW_IE_EXCEPTION << "File '" << xmlPath << "' was not serialized";
- }
- } else {
- THROW_IE_EXCEPTION << "Serialization to IR v10 is not implemented in Inference Engine";
- }
- } else {
- THROW_IE_EXCEPTION << "Serialization to IR v7 is removed from Inference Engine";
- }
-}
-} // namespace Serialization
-} // namespace InferenceEngine
+++ /dev/null
-// Copyright (C) 2018-2020 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#pragma once
-
-#include <ie_icnn_network.hpp>
-#include <string>
-#include <vector>
-
-namespace InferenceEngine {
-namespace Serialization {
-
-/**
- * @brief Serializes a network into IE IR v10 XML file and binary weights file
- * @param xmlPath Path to XML file
- * @param binPath Path to BIN file
- * @param network network to be serialized
- */
-void SerializeV10(const std::string& xmlPath, const std::string& binPath,
- const InferenceEngine::ICNNNetwork& network);
-
-} // namespace Serialization
-} // namespace InferenceEngine
return cloned;
}
+
+ bool visit_attributes(ngraph::AttributeVisitor&) override {
+ return true;
+ }
};
} // namespace ExecGraphInfoSerialization
)
target_link_libraries(${TARGET_NAME} PUBLIC ${NGRAPH_LIBRARIES}
- PRIVATE openvino::itt ngraph::builder)
+ PRIVATE openvino::itt ngraph::builder pugixml)
target_include_directories(${TARGET_NAME} PUBLIC ${PUBLIC_HEADERS_DIR}
PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}/src")
--- /dev/null
+// Copyright (C) 2018-2020 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+
+#pragma once
+
+#include <string>
+
+#include "ngraph/pass/pass.hpp"
+#include "transformations_visibility.hpp"
+
+namespace ngraph {
+namespace pass {
+
+class TRANSFORMATIONS_API Serialize;
+
+} // namespace pass
+} // namespace ngraph
+
+/**
+ * @ingroup ie_transformation_common_api
+ * @brief Serialize transformation converts ngraph::Function into IR files
+ * @attention
+ * - dynamic shapes are not supported
+ * - GenericIE operation type (experimental opset) is not supported
+ * - order of generated layers in xml file is ngraph specific (given by
+ * get_ordered_ops()); MO generates file with different order, but they are
+ * logically equivalent
+ */
+class ngraph::pass::Serialize : public ngraph::pass::FunctionPass {
+public:
+ enum class Version { IR_V10 };
+ NGRAPH_RTTI_DECLARATION;
+ bool run_on_function(std::shared_ptr<ngraph::Function> f) override;
+
+ Serialize(const std::string& xmlPath, const std::string& binPath,
+ Version version = Version::IR_V10)
+ : m_xmlPath{xmlPath}, m_binPath{binPath}, m_version{version} {}
+
+private:
+ const std::string m_xmlPath;
+ const std::string m_binPath;
+ const Version m_version;
+};
--- /dev/null
+// Copyright (C) 2020 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <array>
+#include <fstream>
+#include <unordered_map>
+#include <unordered_set>
+
+#include "ngraph/ops.hpp"
+#include "ngraph/opsets/opset.hpp"
+#include "pugixml.hpp"
+#include "transformations/serialize.hpp"
+
+using namespace ngraph;
+
+NGRAPH_RTTI_DEFINITION(ngraph::pass::Serialize, "Serialize", 0);
+
+namespace { // helpers
+template <typename T, typename A>
+std::string joinVec(std::vector<T, A> const& vec,
+ std::string const& glue = std::string(",")) {
+ if (vec.empty()) return "";
+ std::stringstream oss;
+ oss << vec[0];
+ for (size_t i = 1; i < vec.size(); i++) oss << glue << vec[i];
+ return oss.str();
+}
+} // namespace
+
+namespace { // implementation details
+struct Edge {
+ int from_layer = 0;
+ int from_port = 0;
+ int to_layer = 0;
+ int to_port = 0;
+};
+
+struct ConstantAtributes {
+ int size = 0;
+ int offset = 0;
+};
+
+class XmlVisitor : public ngraph::AttributeVisitor {
+ pugi::xml_node m_data;
+
+ template <typename T>
+ std::string create_atribute_list(
+ ngraph::ValueAccessor<std::vector<T>>& adapter) {
+ return joinVec(adapter.get(), std::string(","));
+ }
+
+public:
+ XmlVisitor(pugi::xml_node& data) : m_data(data) {}
+
+ void on_adapter(const std::string& name,
+ ngraph::ValueAccessor<void>& adapter) override {
+#if 0 // TODO: remove when Constant will support VisitorAPI
+ m_data.append_attribute(name.c_str());
+#endif
+ }
+ void on_adapter(const std::string& name,
+ ngraph::ValueAccessor<bool>& adapter) override {
+ m_data.append_attribute(name.c_str()).set_value(adapter.get());
+ }
+ void on_adapter(const std::string& name,
+ ngraph::ValueAccessor<std::string>& adapter) override {
+ m_data.append_attribute(name.c_str()).set_value(adapter.get().c_str());
+ }
+ void on_adapter(const std::string& name,
+ ngraph::ValueAccessor<int64_t>& adapter) override {
+ m_data.append_attribute(name.c_str()).set_value(adapter.get());
+ }
+ void on_adapter(const std::string& name,
+ ngraph::ValueAccessor<double>& adapter) override {
+ m_data.append_attribute(name.c_str()).set_value(adapter.get());
+ }
+ void on_adapter(
+ const std::string& name,
+ ngraph::ValueAccessor<std::vector<int64_t>>& adapter) override {
+ m_data.append_attribute(name.c_str())
+ .set_value(create_atribute_list(adapter).c_str());
+ }
+ void on_adapter(
+ const std::string& name,
+ ngraph::ValueAccessor<std::vector<uint64_t>>& adapter) override {
+ m_data.append_attribute(name.c_str())
+ .set_value(create_atribute_list(adapter).c_str());
+ }
+ void on_adapter(
+ const std::string& name,
+ ngraph::ValueAccessor<std::vector<float>>& adapter) override {
+ m_data.append_attribute(name.c_str())
+ .set_value(create_atribute_list(adapter).c_str());
+ }
+ void on_adapter(
+ const std::string& name,
+ ngraph::ValueAccessor<std::vector<std::string>>& adapter) override {
+ m_data.append_attribute(name.c_str())
+ .set_value(create_atribute_list(adapter).c_str());
+ }
+};
+
+const std::unordered_map<ngraph::Node*, int> create_layer_ids(
+ const ngraph::Function& f) {
+ std::unordered_map<ngraph::Node*, int> layer_ids;
+ int id = 0;
+ for (const auto& node : f.get_ordered_ops()) {
+ layer_ids[node.get()] = id++;
+ }
+ return layer_ids;
+}
+
+const std::vector<Edge> create_edge_mapping(
+ const std::unordered_map<ngraph::Node*, int>& layer_ids,
+ const ngraph::Function& f) {
+ std::vector<Edge> edges;
+ for (const auto& node : f.get_ordered_ops()) {
+ if (ngraph::op::is_parameter(node)) {
+ continue;
+ }
+
+ for (const auto& i : node->inputs()) {
+ auto source_output = i.get_source_output();
+ auto source_node = source_output.get_node();
+ auto current_node = i.get_node();
+
+ NGRAPH_CHECK(layer_ids.find(source_node) != layer_ids.end(),
+ "Internal error");
+ NGRAPH_CHECK(layer_ids.find(current_node) != layer_ids.end(),
+ "Internal error");
+
+ Edge e{};
+ e.from_layer = layer_ids.find(source_node)->second;
+ e.from_port =
+ source_node->get_input_size() + source_output.get_index();
+ e.to_layer = layer_ids.find(current_node)->second;
+ e.to_port = i.get_index();
+ edges.push_back(e);
+ }
+ }
+ std::sort(begin(edges), end(edges),
+ [](const Edge& a, const Edge& b) -> bool {
+ return a.from_layer < b.from_layer;
+ });
+ return edges;
+}
+
+// TODO: refactor to Vistor API when Constant will be supporting it
+ConstantAtributes dump_constant_data(std::vector<uint8_t>& bin,
+ const ngraph::op::Constant& c) {
+ NGRAPH_CHECK(c.get_output_partial_shape(0.).is_static(),
+ "Unsupported dynamic output shape in ", c);
+
+ ConstantAtributes attr;
+ const uint8_t* p = reinterpret_cast<const uint8_t*>(c.get_data_ptr());
+ attr.size = ngraph::shape_size(c.get_shape()) * c.get_element_type().size();
+ attr.offset = bin.size();
+ bin.insert(end(bin), p, p + attr.size);
+ return attr;
+}
+
+std::string get_opset_name(const ngraph::Node* n) {
+ auto opsets = std::array<std::reference_wrapper<const ngraph::OpSet>, 5>{
+ ngraph::get_opset1(), ngraph::get_opset2(), ngraph::get_opset3(),
+ ngraph::get_opset4(), ngraph::get_opset5()};
+
+ // return the oldest opset name where node type is present
+ for (int idx = 0; idx < opsets.size(); idx++) {
+ if (opsets[idx].get().contains_op_type(n)) {
+ return "opset" + std::to_string(idx + 1);
+ }
+ }
+ return "experimental";
+}
+
+// Here operation type names are translated from ngraph convention to IR
+// convention. Most of them are the same, but there are exceptions, e.g
+// Constant (ngraph name) and Const (IR name). If there will be more
+// discrepancies discoverd, translations needs to be added here.
+std::string get_type_name(const ngraph::Node* n) {
+ std::string name = n->get_type_name();
+ NGRAPH_CHECK(name != "GenericIE", "Unsupported type in ", n);
+
+ const std::unordered_map<std::string, std::string> translator = {
+ {"Constant", "Const"}};
+ if (translator.count(name) > 0) {
+ name = translator.at(name);
+ }
+ return name;
+}
+
+std::string get_output_precision_name(ngraph::Output<Node>& o) {
+ auto elem_type = o.get_element_type();
+ switch (elem_type) {
+ case ::ngraph::element::Type_t::undefined:
+ return "UNSPECIFIED";
+ case ::ngraph::element::Type_t::f16:
+ return "FP16";
+ case ::ngraph::element::Type_t::f32:
+ return "FP32";
+ case ::ngraph::element::Type_t::bf16:
+ return "BF16";
+ case ::ngraph::element::Type_t::i8:
+ return "I8";
+ case ::ngraph::element::Type_t::i16:
+ return "I16";
+ case ::ngraph::element::Type_t::i32:
+ return "I32";
+ case ::ngraph::element::Type_t::i64:
+ return "I64";
+ case ::ngraph::element::Type_t::u8:
+ return "U8";
+ case ::ngraph::element::Type_t::u16:
+ return "U16";
+ case ::ngraph::element::Type_t::u32:
+ return "U32";
+ case ::ngraph::element::Type_t::u64:
+ return "U64";
+ case ::ngraph::element::Type_t::u1:
+ return "BIN";
+ case ::ngraph::element::Type_t::boolean:
+ return "BOOL";
+ default:
+ NGRAPH_CHECK(false, "Unsupported precision in ", o);
+ return "";
+ }
+}
+
+std::string generate_unique_name(
+ const std::unordered_set<std::string>& unique_names, std::string base_name,
+ int suffix) {
+ std::string new_name = base_name + std::to_string(suffix);
+ if (unique_names.find(new_name) == unique_names.end()) {
+ return new_name;
+ } else {
+ suffix++;
+ return generate_unique_name(unique_names, base_name, suffix);
+ }
+}
+
+// TODO: remove when CNNNetwork will be supporting not-unique names
+std::string get_node_unique_name(std::unordered_set<std::string>& unique_names,
+ const ngraph::Node* n) {
+ std::string name = n->get_friendly_name();
+ if (unique_names.find(name) != unique_names.end()) {
+ name = generate_unique_name(unique_names, name, 0);
+ }
+ unique_names.insert(name);
+ return name;
+}
+
+void ngfunction_2_irv10(pugi::xml_document& doc, std::vector<uint8_t>& bin,
+ const ngraph::Function& f) {
+ pugi::xml_node netXml = doc.append_child("net");
+ netXml.append_attribute("name").set_value(f.get_friendly_name().c_str());
+ netXml.append_attribute("version").set_value("10");
+ pugi::xml_node layers = netXml.append_child("layers");
+
+ const std::unordered_map<ngraph::Node*, int> layer_ids =
+ create_layer_ids(f);
+ std::unordered_set<std::string> unique_names;
+
+ for (const auto& n : f.get_ordered_ops()) {
+ ngraph::Node* node = n.get();
+
+ NGRAPH_CHECK(layer_ids.find(node) != layer_ids.end(), "Internal error");
+ // <layers>
+ pugi::xml_node layer = layers.append_child("layer");
+ layer.append_attribute("id").set_value(layer_ids.find(node)->second);
+ layer.append_attribute("name").set_value(
+ get_node_unique_name(unique_names, node).c_str());
+ layer.append_attribute("type").set_value(get_type_name(node).c_str());
+ layer.append_attribute("version").set_value(
+ get_opset_name(node).c_str());
+
+ // <layers/data>
+ pugi::xml_node data = layer.append_child("data");
+
+ // <layers/data> general atributes
+ XmlVisitor visitor{data};
+ NGRAPH_CHECK(node->visit_attributes(visitor),
+ "Visitor API is not supported in ", node);
+
+ // <layers/data> constant atributes (special case)
+ if (auto constant = dynamic_cast<ngraph::op::Constant*>(node)) {
+ ConstantAtributes attr = dump_constant_data(bin, *constant);
+ data.append_attribute("offset").set_value(attr.offset);
+ data.append_attribute("size").set_value(attr.size);
+ }
+
+ int port_id = 0;
+ // <layers/input>
+ if (node->get_input_size() > 0) {
+ pugi::xml_node input = layer.append_child("input");
+ for (auto i : node->inputs()) {
+ NGRAPH_CHECK(i.get_partial_shape().is_static(),
+ "Unsupported dynamic input shape in ", node);
+
+ pugi::xml_node port = input.append_child("port");
+ port.append_attribute("id").set_value(port_id++);
+ for (auto d : i.get_shape()) {
+ pugi::xml_node dim = port.append_child("dim");
+ dim.append_child(pugi::xml_node_type::node_pcdata)
+ .set_value(std::to_string(d).c_str());
+ }
+ }
+ }
+ // <layers/output>
+ if ((node->get_output_size() > 0) && !ngraph::op::is_output(node)) {
+ pugi::xml_node output = layer.append_child("output");
+ for (auto o : node->outputs()) {
+ NGRAPH_CHECK(o.get_partial_shape().is_static(),
+ "Unsupported dynamic output shape in ", node);
+
+ pugi::xml_node port = output.append_child("port");
+ port.append_attribute("id").set_value(port_id++);
+ port.append_attribute("precision")
+ .set_value(get_output_precision_name(o).c_str());
+ for (auto d : o.get_shape()) {
+ pugi::xml_node dim = port.append_child("dim");
+ dim.append_child(pugi::xml_node_type::node_pcdata)
+ .set_value(std::to_string(d).c_str());
+ }
+ }
+ }
+ }
+ // <edges>
+ const std::vector<Edge> edge_mapping = create_edge_mapping(layer_ids, f);
+ pugi::xml_node edges = netXml.append_child("edges");
+ for (auto e : edge_mapping) {
+ pugi::xml_node edge = edges.append_child("edge");
+ edge.append_attribute("from-layer").set_value(e.from_layer);
+ edge.append_attribute("from-port").set_value(e.from_port);
+ edge.append_attribute("to-layer").set_value(e.to_layer);
+ edge.append_attribute("to-port").set_value(e.to_port);
+ }
+}
+
+} // namespace
+
+// ! [function_pass:serialize_cpp]
+// serialize.cpp
+bool pass::Serialize::run_on_function(std::shared_ptr<ngraph::Function> f) {
+ // prepare data
+ pugi::xml_document xml_doc;
+ std::vector<uint8_t> constants;
+ switch (m_version) {
+ case Version::IR_V10:
+ ngfunction_2_irv10(xml_doc, constants, *f);
+ break;
+ default:
+ NGRAPH_UNREACHABLE("Unsupported version");
+ break;
+ }
+
+ // create xml file
+ std::ofstream xml_file(m_xmlPath, std::ios::out);
+ xml_doc.save(xml_file);
+
+ // create bin file
+ std::ofstream bin_file(m_binPath, std::ios::out | std::ios::binary);
+ bin_file.write(reinterpret_cast<const char*>(constants.data()),
+ constants.size() * sizeof(constants[0]));
+
+ // Return false because we didn't change nGraph Function
+ return false;
+}
+// ! [function_pass:serialize_cpp]
# FLAGS "/we4996 /W4 /WX"
# PLUGIN_API)
endif()
+
+
+# ir serialization functional tests variables
+target_compile_definitions(${TARGET_NAME} PRIVATE IR_SERIALIZATION_MODELS_PATH="${CMAKE_CURRENT_SOURCE_DIR}/ir_serialization/models/")
--- /dev/null
+// Copyright (C) 2020 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <fstream>
+
+#include "common_test_utils/ngraph_test_utils.hpp"
+#include "gtest/gtest.h"
+#include "ie_core.hpp"
+
+#ifndef IR_SERIALIZATION_MODELS_PATH // should be already defined by cmake
+#define IR_SERIALIZATION_MODELS_PATH ""
+#endif
+
+class SerializationDeterministicityTest : public ::testing::Test {
+protected:
+ std::string test_name =
+ ::testing::UnitTest::GetInstance()->current_test_info()->name();
+ std::string m_out_xml_path_1 = test_name + "1" + ".xml";
+ std::string m_out_bin_path_1 = test_name + "1" + ".bin";
+ std::string m_out_xml_path_2 = test_name + "2" + ".xml";
+ std::string m_out_bin_path_2 = test_name + "2" + ".bin";
+
+ void TearDown() override {
+ std::remove(m_out_xml_path_1.c_str());
+ std::remove(m_out_xml_path_2.c_str());
+ std::remove(m_out_bin_path_1.c_str());
+ std::remove(m_out_bin_path_2.c_str());
+ }
+
+ bool files_equal(std::ifstream& f1, std::ifstream& f2) {
+ if (!f1.good()) return false;
+ if (!f2.good()) return false;
+
+ while (!f1.eof() && !f2.eof()) {
+ if (f1.get() != f2.get()) {
+ return false;
+ }
+ }
+
+ if (f1.eof() != f2.eof()) {
+ return false;
+ }
+
+ return true;
+ }
+};
+
+TEST_F(SerializationDeterministicityTest, BasicModel) {
+ const std::string model = IR_SERIALIZATION_MODELS_PATH "add_abc.prototxt";
+
+ InferenceEngine::Core ie;
+ auto expected = ie.ReadNetwork(model);
+ expected.serialize(m_out_xml_path_1, m_out_bin_path_1);
+ expected.serialize(m_out_xml_path_2, m_out_bin_path_2);
+
+ std::ifstream xml_1(m_out_xml_path_1, std::ios::in | std::ios::binary);
+ std::ifstream bin_1(m_out_bin_path_1, std::ios::in | std::ios::binary);
+ std::ifstream xml_2(m_out_xml_path_2, std::ios::in | std::ios::binary);
+ std::ifstream bin_2(m_out_bin_path_2, std::ios::in | std::ios::binary);
+
+ ASSERT_TRUE(files_equal(xml_1, xml_2));
+ ASSERT_TRUE(files_equal(bin_1, bin_2));
+}
+
+TEST_F(SerializationDeterministicityTest, ModelWithMultipleOutputs) {
+ const std::string model =
+ IR_SERIALIZATION_MODELS_PATH "split_equal_parts_2d.xml";
+ const std::string weights =
+ IR_SERIALIZATION_MODELS_PATH "split_equal_parts_2d.bin";
+
+ InferenceEngine::Core ie;
+ auto expected = ie.ReadNetwork(model, weights);
+ expected.serialize(m_out_xml_path_1, m_out_bin_path_1);
+ expected.serialize(m_out_xml_path_2, m_out_bin_path_2);
+
+ std::ifstream xml_1(m_out_xml_path_1, std::ios::in | std::ios::binary);
+ std::ifstream bin_1(m_out_bin_path_1, std::ios::in | std::ios::binary);
+ std::ifstream xml_2(m_out_xml_path_2, std::ios::in | std::ios::binary);
+ std::ifstream bin_2(m_out_bin_path_2, std::ios::in | std::ios::binary);
+
+ ASSERT_TRUE(files_equal(xml_1, xml_2));
+ ASSERT_TRUE(files_equal(bin_1, bin_2));
+}
+
+TEST_F(SerializationDeterministicityTest, ModelWithMultipleLayers) {
+ const std::string model =
+ IR_SERIALIZATION_MODELS_PATH "addmul_abc.prototxt";
+
+ InferenceEngine::Core ie;
+ auto expected = ie.ReadNetwork(model);
+ expected.serialize(m_out_xml_path_1, m_out_bin_path_1);
+ expected.serialize(m_out_xml_path_2, m_out_bin_path_2);
+
+ std::ifstream xml_1(m_out_xml_path_1, std::ios::in | std::ios::binary);
+ std::ifstream bin_1(m_out_bin_path_1, std::ios::in | std::ios::binary);
+ std::ifstream xml_2(m_out_xml_path_2, std::ios::in | std::ios::binary);
+ std::ifstream bin_2(m_out_bin_path_2, std::ios::in | std::ios::binary);
+
+ ASSERT_TRUE(files_equal(xml_1, xml_2));
+ ASSERT_TRUE(files_equal(bin_1, bin_2));
+}
+
+TEST_F(SerializationDeterministicityTest, ModelWithConstants) {
+ const std::string model =
+ IR_SERIALIZATION_MODELS_PATH "add_abc_initializers.xml";
+ const std::string weights =
+ IR_SERIALIZATION_MODELS_PATH "add_abc_initializers.bin";
+
+ InferenceEngine::Core ie;
+ auto expected = ie.ReadNetwork(model, weights);
+ expected.serialize(m_out_xml_path_1, m_out_bin_path_1);
+ expected.serialize(m_out_xml_path_2, m_out_bin_path_2);
+
+ std::ifstream xml_1(m_out_xml_path_1, std::ios::in | std::ios::binary);
+ std::ifstream bin_1(m_out_bin_path_1, std::ios::in | std::ios::binary);
+ std::ifstream xml_2(m_out_xml_path_2, std::ios::in | std::ios::binary);
+ std::ifstream bin_2(m_out_bin_path_2, std::ios::in | std::ios::binary);
+
+ ASSERT_TRUE(files_equal(xml_1, xml_2));
+ ASSERT_TRUE(files_equal(bin_1, bin_2));
+}
--- /dev/null
+ir_version: 3
+producer_name: "nGraph ONNX Importer"
+graph {
+ node {
+ input: "A"
+ input: "B"
+ output: "X"
+ name: "add_node1"
+ op_type: "Add"
+ }
+ node {
+ input: "X"
+ input: "C"
+ output: "Y"
+ name: "add_node2"
+ op_type: "Add"
+ }
+ name: "test_graph"
+ input {
+ name: "A"
+ type {
+ tensor_type {
+ elem_type: 1
+ shape {
+ dim {
+ dim_value: 1
+ }
+ }
+ }
+ }
+ }
+ input {
+ name: "B"
+ type {
+ tensor_type {
+ elem_type: 1
+ shape {
+ dim {
+ dim_value: 1
+ }
+ }
+ }
+ }
+ }
+ input {
+ name: "C"
+ type {
+ tensor_type {
+ elem_type: 1
+ shape {
+ dim {
+ dim_value: 1
+ }
+ }
+ }
+ }
+ }
+ output {
+ name: "Y"
+ type {
+ tensor_type {
+ elem_type: 1
+ shape {
+ dim {
+ dim_value: 1
+ }
+ }
+ }
+ }
+ }
+}
+opset_import {
+ version: 4
+}
--- /dev/null
+<?xml version="1.0" ?>
+<net name="add_abc" version="10">
+ <layers>
+ <layer id="0" name="A" type="Parameter" version="opset1">
+ <data element_type="f32" shape="1"/>
+ <output>
+ <port id="0" precision="FP32">
+ <dim>1</dim>
+ </port>
+ </output>
+ </layer>
+ <layer id="1" name="B" type="Parameter" version="opset1">
+ <data element_type="f32" shape="1"/>
+ <output>
+ <port id="0" precision="FP32">
+ <dim>1</dim>
+ </port>
+ </output>
+ </layer>
+ <layer id="2" name="add_node1" type="Add" version="opset1">
+ <input>
+ <port id="0">
+ <dim>1</dim>
+ </port>
+ <port id="1">
+ <dim>1</dim>
+ </port>
+ </input>
+ <output>
+ <port id="2" precision="FP32">
+ <dim>1</dim>
+ </port>
+ </output>
+ </layer>
+ <layer id="3" name="C" type="Parameter" version="opset1">
+ <data element_type="f32" shape="1"/>
+ <output>
+ <port id="0" precision="FP32">
+ <dim>1</dim>
+ </port>
+ </output>
+ </layer>
+ <layer id="4" name="Y" type="Add" version="opset1">
+ <input>
+ <port id="0">
+ <dim>1</dim>
+ </port>
+ <port id="1">
+ <dim>1</dim>
+ </port>
+ </input>
+ <output>
+ <port id="2" precision="FP32">
+ <dim>1</dim>
+ </port>
+ </output>
+ </layer>
+ <layer id="5" name="Y/sink_port_0" type="Result" version="opset1">
+ <input>
+ <port id="0">
+ <dim>1</dim>
+ </port>
+ </input>
+ </layer>
+ </layers>
+ <edges>
+ <edge from-layer="0" from-port="0" to-layer="2" to-port="0"/>
+ <edge from-layer="1" from-port="0" to-layer="2" to-port="1"/>
+ <edge from-layer="2" from-port="2" to-layer="4" to-port="0"/>
+ <edge from-layer="3" from-port="0" to-layer="4" to-port="1"/>
+ <edge from-layer="4" from-port="2" to-layer="5" to-port="0"/>
+ </edges>
+ <meta_data>
+ <MO_version value="unknown version"/>
+ <cli_parameters>
+ <caffe_parser_path value="DIR"/>
+ <data_type value="float"/>
+ <disable_nhwc_to_nchw value="False"/>
+ <disable_omitting_optional value="False"/>
+ <disable_resnet_optimization value="False"/>
+ <disable_weights_compression value="False"/>
+ <enable_concat_optimization value="False"/>
+ <enable_flattening_nested_params value="False"/>
+ <enable_ssd_gluoncv value="False"/>
+ <extensions value="DIR"/>
+ <framework value="onnx"/>
+ <freeze_placeholder_with_value value="{}"/>
+ <generate_deprecated_IR_V7 value="False"/>
+ <input_model value="DIR/add_abc.onnx"/>
+ <input_model_is_text value="False"/>
+ <k value="DIR/CustomLayersMapping.xml"/>
+ <keep_shape_ops value="True"/>
+ <legacy_mxnet_model value="False"/>
+ <log_level value="ERROR"/>
+ <mean_scale_values value="{}"/>
+ <mean_values value="()"/>
+ <model_name value="add_abc"/>
+ <output_dir value="DIR"/>
+ <placeholder_data_types value="{}"/>
+ <progress value="False"/>
+ <remove_memory value="False"/>
+ <remove_output_softmax value="False"/>
+ <reverse_input_channels value="False"/>
+ <save_params_from_nd value="False"/>
+ <scale_values value="()"/>
+ <silent value="False"/>
+ <static_shape value="False"/>
+ <stream_output value="False"/>
+ <unset unset_cli_parameters="batch, counts, disable_fusing, disable_gfusing, finegrain_fusing, input, input_checkpoint, input_meta_graph, input_proto, input_shape, input_symbol, mean_file, mean_file_offsets, move_to_preprocess, nd_prefix_name, output, placeholder_shapes, pretrained_model_name, saved_model_dir, saved_model_tags, scale, tensorboard_logdir, tensorflow_custom_layer_libraries, tensorflow_custom_operations_config_update, tensorflow_object_detection_api_pipeline_config, tensorflow_use_custom_operations_config, transformations_config"/>
+ </cli_parameters>
+ </meta_data>
+</net>
--- /dev/null
+ir_version: 3
+producer_name: "nGraph ONNX Importer"
+graph {
+ node {
+ output: "B"
+ op_type: "Constant"
+ attribute {
+ name: "value"
+ t {
+ dims: 2
+ dims: 2
+ data_type: 1
+ float_data: 1
+ float_data: 2
+ float_data: 3
+ float_data: 4
+ name: "const_tensor"
+ }
+ type: TENSOR
+ }
+ }
+ node {
+ input: "A"
+ input: "B"
+ output: "X"
+ name: "add_node1"
+ op_type: "Add"
+ }
+ node {
+ input: "X"
+ input: "C"
+ output: "Y"
+ name: "add_node2"
+ op_type: "Add"
+ }
+ name: "test_graph"
+ initializer {
+ dims: 2
+ dims: 2
+ data_type: 1
+ name: "A"
+ raw_data: "\000\000\200?\000\000\000@\000\000@@\000\000\200@"
+ }
+ input {
+ name: "A"
+ type {
+ tensor_type {
+ elem_type: 1
+ shape {
+ dim {
+ dim_value: 2
+ }
+ dim {
+ dim_value: 2
+ }
+ }
+ }
+ }
+ }
+ input {
+ name: "C"
+ type {
+ tensor_type {
+ elem_type: 1
+ shape {
+ dim {
+ dim_value: 2
+ }
+ dim {
+ dim_value: 2
+ }
+ }
+ }
+ }
+ }
+ output {
+ name: "Y"
+ type {
+ tensor_type {
+ elem_type: 1
+ shape {
+ dim {
+ dim_value: 2
+ }
+ dim {
+ dim_value: 2
+ }
+ }
+ }
+ }
+ }
+}
+opset_import {
+ version: 4
+}
--- /dev/null
+<?xml version="1.0" ?>
+<net name="add_abc_const" version="10">
+ <layers>
+ <layer id="0" name="add_node1/Output_0/Data__const" type="Const" version="opset1">
+ <data element_type="f32" offset="0" shape="2,2" size="16"/>
+ <output>
+ <port id="1" precision="FP32">
+ <dim>2</dim>
+ <dim>2</dim>
+ </port>
+ </output>
+ </layer>
+ <layer id="1" name="C" type="Parameter" version="opset1">
+ <data element_type="f32" shape="2,2"/>
+ <output>
+ <port id="0" precision="FP32">
+ <dim>2</dim>
+ <dim>2</dim>
+ </port>
+ </output>
+ </layer>
+ <layer id="2" name="Y" type="Add" version="opset1">
+ <input>
+ <port id="0">
+ <dim>2</dim>
+ <dim>2</dim>
+ </port>
+ <port id="1">
+ <dim>2</dim>
+ <dim>2</dim>
+ </port>
+ </input>
+ <output>
+ <port id="2" precision="FP32">
+ <dim>2</dim>
+ <dim>2</dim>
+ </port>
+ </output>
+ </layer>
+ <layer id="3" name="Y/sink_port_0" type="Result" version="opset1">
+ <input>
+ <port id="0">
+ <dim>2</dim>
+ <dim>2</dim>
+ </port>
+ </input>
+ </layer>
+ </layers>
+ <edges>
+ <edge from-layer="0" from-port="1" to-layer="2" to-port="0"/>
+ <edge from-layer="1" from-port="0" to-layer="2" to-port="1"/>
+ <edge from-layer="2" from-port="2" to-layer="3" to-port="0"/>
+ </edges>
+ <meta_data>
+ <MO_version value="unknown version"/>
+ <cli_parameters>
+ <caffe_parser_path value="DIR"/>
+ <data_type value="float"/>
+ <disable_nhwc_to_nchw value="False"/>
+ <disable_omitting_optional value="False"/>
+ <disable_resnet_optimization value="False"/>
+ <disable_weights_compression value="False"/>
+ <enable_concat_optimization value="False"/>
+ <enable_flattening_nested_params value="False"/>
+ <enable_ssd_gluoncv value="False"/>
+ <extensions value="DIR"/>
+ <framework value="onnx"/>
+ <freeze_placeholder_with_value value="{}"/>
+ <generate_deprecated_IR_V7 value="False"/>
+ <input_model value="DIR/add_abc_const.onnx"/>
+ <input_model_is_text value="False"/>
+ <k value="DIR/CustomLayersMapping.xml"/>
+ <keep_shape_ops value="True"/>
+ <legacy_mxnet_model value="False"/>
+ <log_level value="ERROR"/>
+ <mean_scale_values value="{}"/>
+ <mean_values value="()"/>
+ <model_name value="add_abc_const"/>
+ <output_dir value="DIR"/>
+ <placeholder_data_types value="{}"/>
+ <progress value="False"/>
+ <remove_memory value="False"/>
+ <remove_output_softmax value="False"/>
+ <reverse_input_channels value="False"/>
+ <save_params_from_nd value="False"/>
+ <scale_values value="()"/>
+ <silent value="False"/>
+ <static_shape value="False"/>
+ <stream_output value="False"/>
+ <unset unset_cli_parameters="batch, counts, disable_fusing, disable_gfusing, finegrain_fusing, input, input_checkpoint, input_meta_graph, input_proto, input_shape, input_symbol, mean_file, mean_file_offsets, move_to_preprocess, nd_prefix_name, output, placeholder_shapes, pretrained_model_name, saved_model_dir, saved_model_tags, scale, tensorboard_logdir, tensorflow_custom_layer_libraries, tensorflow_custom_operations_config_update, tensorflow_object_detection_api_pipeline_config, tensorflow_use_custom_operations_config, transformations_config"/>
+ </cli_parameters>
+ </meta_data>
+</net>
--- /dev/null
+ir_version: 3
+producer_name: "nGraph ONNX Importer"
+graph {
+ node {
+ input: "A"
+ input: "B"
+ output: "X1"
+ name: "add_node1"
+ op_type: "Add"
+ }
+
+ node {
+ input: "A"
+ input: "B"
+ output: "X2"
+ name: "add_node2"
+ op_type: "Mul"
+ }
+
+ node {
+ input: "X1"
+ input: "X2"
+ output: "Z1"
+ name: "add_node3"
+ op_type: "Add"
+ }
+
+ node {
+ input: "Z1"
+ input: "C"
+ output: "Z2"
+ name: "add_node4"
+ op_type: "Add"
+ }
+
+ node {
+ input: "Z2"
+ input: "C"
+ output: "Y"
+ name: "add_node5"
+ op_type: "Add"
+ }
+
+ name: "test_graph"
+ input {
+ name: "A"
+ type {
+ tensor_type {
+ elem_type: 1
+ shape {
+ dim {
+ dim_value: 1
+ }
+ }
+ }
+ }
+ }
+ input {
+ name: "B"
+ type {
+ tensor_type {
+ elem_type: 1
+ shape {
+ dim {
+ dim_value: 1
+ }
+ }
+ }
+ }
+ }
+ input {
+ name: "C"
+ type {
+ tensor_type {
+ elem_type: 1
+ shape {
+ dim {
+ dim_value: 1
+ }
+ }
+ }
+ }
+ }
+ output {
+ name: "Y"
+ type {
+ tensor_type {
+ elem_type: 1
+ shape {
+ dim {
+ dim_value: 1
+ }
+ }
+ }
+ }
+ }
+}
+opset_import {
+ version: 4
+}
--- /dev/null
+<?xml version="1.0" ?>
+<net name="addmul_abc" version="10">
+ <layers>
+ <layer id="0" name="A" type="Parameter" version="opset1">
+ <data element_type="f32" shape="1"/>
+ <output>
+ <port id="0" precision="FP32">
+ <dim>1</dim>
+ </port>
+ </output>
+ </layer>
+ <layer id="1" name="B" type="Parameter" version="opset1">
+ <data element_type="f32" shape="1"/>
+ <output>
+ <port id="0" precision="FP32">
+ <dim>1</dim>
+ </port>
+ </output>
+ </layer>
+ <layer id="2" name="add_node1" type="Add" version="opset1">
+ <input>
+ <port id="0">
+ <dim>1</dim>
+ </port>
+ <port id="1">
+ <dim>1</dim>
+ </port>
+ </input>
+ <output>
+ <port id="2" precision="FP32">
+ <dim>1</dim>
+ </port>
+ </output>
+ </layer>
+ <layer id="3" name="add_node2" type="Multiply" version="opset1">
+ <input>
+ <port id="0">
+ <dim>1</dim>
+ </port>
+ <port id="1">
+ <dim>1</dim>
+ </port>
+ </input>
+ <output>
+ <port id="2" precision="FP32">
+ <dim>1</dim>
+ </port>
+ </output>
+ </layer>
+ <layer id="4" name="add_node3" type="Add" version="opset1">
+ <input>
+ <port id="0">
+ <dim>1</dim>
+ </port>
+ <port id="1">
+ <dim>1</dim>
+ </port>
+ </input>
+ <output>
+ <port id="2" precision="FP32">
+ <dim>1</dim>
+ </port>
+ </output>
+ </layer>
+ <layer id="5" name="C" type="Parameter" version="opset1">
+ <data element_type="f32" shape="1"/>
+ <output>
+ <port id="0" precision="FP32">
+ <dim>1</dim>
+ </port>
+ </output>
+ </layer>
+ <layer id="6" name="add_node4" type="Add" version="opset1">
+ <input>
+ <port id="0">
+ <dim>1</dim>
+ </port>
+ <port id="1">
+ <dim>1</dim>
+ </port>
+ </input>
+ <output>
+ <port id="2" precision="FP32">
+ <dim>1</dim>
+ </port>
+ </output>
+ </layer>
+ <layer id="7" name="Y" type="Add" version="opset1">
+ <input>
+ <port id="0">
+ <dim>1</dim>
+ </port>
+ <port id="1">
+ <dim>1</dim>
+ </port>
+ </input>
+ <output>
+ <port id="2" precision="FP32">
+ <dim>1</dim>
+ </port>
+ </output>
+ </layer>
+ <layer id="8" name="Y/sink_port_0" type="Result" version="opset1">
+ <input>
+ <port id="0">
+ <dim>1</dim>
+ </port>
+ </input>
+ </layer>
+ </layers>
+ <edges>
+ <edge from-layer="0" from-port="0" to-layer="2" to-port="0"/>
+ <edge from-layer="1" from-port="0" to-layer="2" to-port="1"/>
+ <edge from-layer="0" from-port="0" to-layer="3" to-port="0"/>
+ <edge from-layer="1" from-port="0" to-layer="3" to-port="1"/>
+ <edge from-layer="2" from-port="2" to-layer="4" to-port="0"/>
+ <edge from-layer="3" from-port="2" to-layer="4" to-port="1"/>
+ <edge from-layer="4" from-port="2" to-layer="6" to-port="0"/>
+ <edge from-layer="5" from-port="0" to-layer="6" to-port="1"/>
+ <edge from-layer="6" from-port="2" to-layer="7" to-port="0"/>
+ <edge from-layer="5" from-port="0" to-layer="7" to-port="1"/>
+ <edge from-layer="7" from-port="2" to-layer="8" to-port="0"/>
+ </edges>
+ <meta_data>
+ <MO_version value="unknown version"/>
+ <cli_parameters>
+ <caffe_parser_path value="DIR"/>
+ <data_type value="float"/>
+ <disable_nhwc_to_nchw value="False"/>
+ <disable_omitting_optional value="False"/>
+ <disable_resnet_optimization value="False"/>
+ <disable_weights_compression value="False"/>
+ <enable_concat_optimization value="False"/>
+ <enable_flattening_nested_params value="False"/>
+ <enable_ssd_gluoncv value="False"/>
+ <extensions value="DIR"/>
+ <framework value="onnx"/>
+ <freeze_placeholder_with_value value="{}"/>
+ <generate_deprecated_IR_V7 value="False"/>
+ <input_model value="DIR/addmul_abc.onnx"/>
+ <input_model_is_text value="False"/>
+ <k value="DIR/CustomLayersMapping.xml"/>
+ <keep_shape_ops value="True"/>
+ <legacy_mxnet_model value="False"/>
+ <log_level value="ERROR"/>
+ <mean_scale_values value="{}"/>
+ <mean_values value="()"/>
+ <model_name value="addmul_abc"/>
+ <output_dir value="DIR"/>
+ <placeholder_data_types value="{}"/>
+ <progress value="False"/>
+ <remove_memory value="False"/>
+ <remove_output_softmax value="False"/>
+ <reverse_input_channels value="False"/>
+ <save_params_from_nd value="False"/>
+ <scale_values value="()"/>
+ <silent value="False"/>
+ <static_shape value="False"/>
+ <stream_output value="False"/>
+ <unset unset_cli_parameters="batch, counts, disable_fusing, disable_gfusing, finegrain_fusing, input, input_checkpoint, input_meta_graph, input_proto, input_shape, input_symbol, mean_file, mean_file_offsets, move_to_preprocess, nd_prefix_name, output, placeholder_shapes, pretrained_model_name, saved_model_dir, saved_model_tags, scale, tensorboard_logdir, tensorflow_custom_layer_libraries, tensorflow_custom_operations_config_update, tensorflow_object_detection_api_pipeline_config, tensorflow_use_custom_operations_config, transformations_config"/>
+ </cli_parameters>
+ </meta_data>
+</net>
--- /dev/null
+ir_version: 3
+producer_name: "nGraph ONNX Importer"
+graph {
+ node {
+ input: "input"
+ output: "output_1"
+ output: "output_2"
+ op_type: "Split"
+ attribute {
+ name: "axis"
+ i: 1
+ type: INT
+ }
+ }
+ name: "test_split_equal_parts_2d"
+ input {
+ name: "input"
+ type {
+ tensor_type {
+ elem_type: 1
+ shape {
+ dim {
+ dim_value: 2
+ }
+ dim {
+ dim_value: 6
+ }
+ }
+ }
+ }
+ }
+ output {
+ name: "output_1"
+ type {
+ tensor_type {
+ elem_type: 1
+ shape {
+ dim {
+ dim_value: 2
+ }
+ dim {
+ dim_value: 3
+ }
+ }
+ }
+ }
+ }
+ output {
+ name: "output_2"
+ type {
+ tensor_type {
+ elem_type: 1
+ shape {
+ dim {
+ dim_value: 2
+ }
+ dim {
+ dim_value: 3
+ }
+ }
+ }
+ }
+ }
+}
+opset_import {
+ version: 8
+}
--- /dev/null
+<?xml version="1.0" ?>
+<net name="split_equal_parts_2d" version="10">
+ <layers>
+ <layer id="0" name="input" type="Parameter" version="opset1">
+ <data element_type="f32" shape="2,6"/>
+ <output>
+ <port id="0" precision="FP32">
+ <dim>2</dim>
+ <dim>6</dim>
+ </port>
+ </output>
+ </layer>
+ <layer id="1" name="output_14/Split_input_port_1/value/Output_0/Data__const" type="Const" version="opset1">
+ <data element_type="i64" offset="0" shape="" size="8"/>
+ <output>
+ <port id="1" precision="I64"/>
+ </output>
+ </layer>
+ <layer id="2" name="output_14/Split" type="Split" version="opset1">
+ <data num_splits="2"/>
+ <input>
+ <port id="0">
+ <dim>2</dim>
+ <dim>6</dim>
+ </port>
+ <port id="1"/>
+ </input>
+ <output>
+ <port id="2" precision="FP32">
+ <dim>2</dim>
+ <dim>3</dim>
+ </port>
+ <port id="3" precision="FP32">
+ <dim>2</dim>
+ <dim>3</dim>
+ </port>
+ </output>
+ </layer>
+ <layer id="3" name="67_input_port_1/value69_const" type="Const" version="opset1">
+ <data element_type="f32" offset="8" shape="" size="4"/>
+ <output>
+ <port id="1" precision="FP32"/>
+ </output>
+ </layer>
+ <layer id="4" name="output_1" type="Add" version="opset1">
+ <input>
+ <port id="0">
+ <dim>2</dim>
+ <dim>3</dim>
+ </port>
+ <port id="1"/>
+ </input>
+ <output>
+ <port id="2" precision="FP32">
+ <dim>2</dim>
+ <dim>3</dim>
+ </port>
+ </output>
+ </layer>
+ <layer id="5" name="output_1/sink_port_0" type="Result" version="opset1">
+ <input>
+ <port id="0">
+ <dim>2</dim>
+ <dim>3</dim>
+ </port>
+ </input>
+ </layer>
+ <layer id="6" name="71_input_port_1/value73_const" type="Const" version="opset1">
+ <data element_type="f32" offset="8" shape="" size="4"/>
+ <output>
+ <port id="1" precision="FP32"/>
+ </output>
+ </layer>
+ <layer id="7" name="output_2" type="Add" version="opset1">
+ <input>
+ <port id="0">
+ <dim>2</dim>
+ <dim>3</dim>
+ </port>
+ <port id="1"/>
+ </input>
+ <output>
+ <port id="2" precision="FP32">
+ <dim>2</dim>
+ <dim>3</dim>
+ </port>
+ </output>
+ </layer>
+ <layer id="8" name="output_2/sink_port_0" type="Result" version="opset1">
+ <input>
+ <port id="0">
+ <dim>2</dim>
+ <dim>3</dim>
+ </port>
+ </input>
+ </layer>
+ </layers>
+ <edges>
+ <edge from-layer="0" from-port="0" to-layer="2" to-port="0"/>
+ <edge from-layer="1" from-port="1" to-layer="2" to-port="1"/>
+ <edge from-layer="2" from-port="2" to-layer="4" to-port="0"/>
+ <edge from-layer="3" from-port="1" to-layer="4" to-port="1"/>
+ <edge from-layer="4" from-port="2" to-layer="5" to-port="0"/>
+ <edge from-layer="2" from-port="3" to-layer="7" to-port="0"/>
+ <edge from-layer="6" from-port="1" to-layer="7" to-port="1"/>
+ <edge from-layer="7" from-port="2" to-layer="8" to-port="0"/>
+ </edges>
+ <meta_data>
+ <MO_version value="unknown version"/>
+ <cli_parameters>
+ <caffe_parser_path value="DIR"/>
+ <data_type value="float"/>
+ <disable_nhwc_to_nchw value="False"/>
+ <disable_omitting_optional value="False"/>
+ <disable_resnet_optimization value="False"/>
+ <disable_weights_compression value="False"/>
+ <enable_concat_optimization value="False"/>
+ <enable_flattening_nested_params value="False"/>
+ <enable_ssd_gluoncv value="False"/>
+ <extensions value="DIR"/>
+ <framework value="onnx"/>
+ <freeze_placeholder_with_value value="{}"/>
+ <generate_deprecated_IR_V7 value="False"/>
+ <input_model value="DIR/split_equal_parts_2d.onnx"/>
+ <input_model_is_text value="False"/>
+ <k value="DIR/CustomLayersMapping.xml"/>
+ <keep_shape_ops value="True"/>
+ <legacy_mxnet_model value="False"/>
+ <log_level value="ERROR"/>
+ <mean_scale_values value="{}"/>
+ <mean_values value="()"/>
+ <model_name value="split_equal_parts_2d"/>
+ <output_dir value="DIR"/>
+ <placeholder_data_types value="{}"/>
+ <progress value="False"/>
+ <remove_memory value="False"/>
+ <remove_output_softmax value="False"/>
+ <reverse_input_channels value="False"/>
+ <save_params_from_nd value="False"/>
+ <scale_values value="()"/>
+ <silent value="False"/>
+ <static_shape value="False"/>
+ <stream_output value="False"/>
+ <unset unset_cli_parameters="batch, counts, disable_fusing, disable_gfusing, finegrain_fusing, input, input_checkpoint, input_meta_graph, input_proto, input_shape, input_symbol, mean_file, mean_file_offsets, move_to_preprocess, nd_prefix_name, output, placeholder_shapes, pretrained_model_name, saved_model_dir, saved_model_tags, scale, tensorboard_logdir, tensorflow_custom_layer_libraries, tensorflow_custom_operations_config_update, tensorflow_object_detection_api_pipeline_config, tensorflow_use_custom_operations_config, transformations_config"/>
+ </cli_parameters>
+ </meta_data>
+</net>
--- /dev/null
+// Copyright (C) 2020 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <fstream>
+
+#include "common_test_utils/ngraph_test_utils.hpp"
+#include "gtest/gtest.h"
+#include "ie_core.hpp"
+
+#ifndef IR_SERIALIZATION_MODELS_PATH // should be already defined by cmake
+#define IR_SERIALIZATION_MODELS_PATH ""
+#endif
+
+class SerializationTest : public ::testing::Test {
+protected:
+ std::string test_name =
+ ::testing::UnitTest::GetInstance()->current_test_info()->name();
+ std::string m_out_xml_path = test_name + ".xml";
+ std::string m_out_bin_path = test_name + ".bin";
+
+ void TearDown() override {
+ std::remove(m_out_xml_path.c_str());
+ std::remove(m_out_bin_path.c_str());
+ }
+};
+
+TEST_F(SerializationTest, BasicModel_MO) {
+ const std::string model = IR_SERIALIZATION_MODELS_PATH "add_abc.xml";
+ const std::string weights = IR_SERIALIZATION_MODELS_PATH "add_abc.bin";
+
+ InferenceEngine::Core ie;
+ auto expected = ie.ReadNetwork(model, weights);
+ expected.serialize(m_out_xml_path, m_out_bin_path);
+ auto result = ie.ReadNetwork(m_out_xml_path, m_out_bin_path);
+
+ bool success;
+ std::string message;
+ std::tie(success, message) =
+ compare_functions(result.getFunction(), expected.getFunction());
+
+ ASSERT_TRUE(success) << message;
+}
+
+TEST_F(SerializationTest, BasicModel_ONNXImporter) {
+ const std::string model = IR_SERIALIZATION_MODELS_PATH "add_abc.prototxt";
+
+ InferenceEngine::Core ie;
+ auto expected = ie.ReadNetwork(model);
+ expected.serialize(m_out_xml_path, m_out_bin_path);
+ auto result = ie.ReadNetwork(m_out_xml_path, m_out_bin_path);
+
+ bool success;
+ std::string message;
+ std::tie(success, message) =
+ compare_functions(result.getFunction(), expected.getFunction());
+
+ ASSERT_TRUE(success) << message;
+}
+
+TEST_F(SerializationTest, ModelWithMultipleOutputs_MO) {
+ const std::string model =
+ IR_SERIALIZATION_MODELS_PATH "split_equal_parts_2d.xml";
+ const std::string weights =
+ IR_SERIALIZATION_MODELS_PATH "split_equal_parts_2d.bin";
+
+ InferenceEngine::Core ie;
+ auto expected = ie.ReadNetwork(model, weights);
+ expected.serialize(m_out_xml_path, m_out_bin_path);
+ auto result = ie.ReadNetwork(m_out_xml_path, m_out_bin_path);
+
+ // Compare function does not support models with multiple outputs
+ bool success;
+ std::string message;
+ std::tie(success, message) =
+ compare_functions(result.getFunction(), expected.getFunction());
+
+ ASSERT_FALSE(success) << message;
+}
+
+TEST_F(SerializationTest, ModelWithMultipleOutputs_ONNXImporter) {
+ const std::string model =
+ IR_SERIALIZATION_MODELS_PATH "split_equal_parts_2d.prototxt";
+
+ InferenceEngine::Core ie;
+ auto expected = ie.ReadNetwork(model);
+ expected.serialize(m_out_xml_path, m_out_bin_path);
+ auto result = ie.ReadNetwork(m_out_xml_path, m_out_bin_path);
+
+ // Compare function does not support models with multiple outputs
+ bool success;
+ std::string message;
+ std::tie(success, message) =
+ compare_functions(result.getFunction(), expected.getFunction());
+
+ ASSERT_FALSE(success) << message;
+}
+
+TEST_F(SerializationTest, ModelWithMultipleLayers_MO) {
+ const std::string model = IR_SERIALIZATION_MODELS_PATH "addmul_abc.xml";
+ const std::string weights = IR_SERIALIZATION_MODELS_PATH "addmul_abc.bin";
+
+ InferenceEngine::Core ie;
+ auto expected = ie.ReadNetwork(model, weights);
+ expected.serialize(m_out_xml_path, m_out_bin_path);
+ auto result = ie.ReadNetwork(m_out_xml_path, m_out_bin_path);
+
+ bool success;
+ std::string message;
+ std::tie(success, message) =
+ compare_functions(result.getFunction(), expected.getFunction());
+
+ ASSERT_TRUE(success) << message;
+}
+
+TEST_F(SerializationTest, ModelWithMultipleLayers_ONNXImporter) {
+ const std::string model =
+ IR_SERIALIZATION_MODELS_PATH "addmul_abc.prototxt";
+
+ InferenceEngine::Core ie;
+ auto expected = ie.ReadNetwork(model);
+ expected.serialize(m_out_xml_path, m_out_bin_path);
+ auto result = ie.ReadNetwork(m_out_xml_path, m_out_bin_path);
+
+ bool success;
+ std::string message;
+ std::tie(success, message) =
+ compare_functions(result.getFunction(), expected.getFunction());
+
+ ASSERT_TRUE(success) << message;
+}
+
+TEST_F(SerializationTest, ModelWithConstants_MO) {
+ const std::string model =
+ IR_SERIALIZATION_MODELS_PATH "add_abc_initializers.xml";
+ const std::string weights =
+ IR_SERIALIZATION_MODELS_PATH "add_abc_initializers.bin";
+
+ InferenceEngine::Core ie;
+ auto expected = ie.ReadNetwork(model, weights);
+ expected.serialize(m_out_xml_path, m_out_bin_path);
+ auto result = ie.ReadNetwork(m_out_xml_path, m_out_bin_path);
+
+ bool success;
+ std::string message;
+ std::tie(success, message) =
+ compare_functions(result.getFunction(), expected.getFunction());
+
+ ASSERT_TRUE(success) << message;
+}
+
+TEST_F(SerializationTest, ModelWithConstants_ONNXImporter) {
+ const std::string model =
+ IR_SERIALIZATION_MODELS_PATH "add_abc_initializers.prototxt";
+
+ InferenceEngine::Core ie;
+ auto expected = ie.ReadNetwork(model);
+ expected.serialize(m_out_xml_path, m_out_bin_path);
+ auto result = ie.ReadNetwork(m_out_xml_path, m_out_bin_path);
+
+ bool success;
+ std::string message;
+ std::tie(success, message) =
+ compare_functions(result.getFunction(), expected.getFunction());
+
+ ASSERT_TRUE(success) << message;
+}
--- /dev/null
+// Copyright (C) 2020 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <fstream>
+
+#include "gtest/gtest.h"
+#include "ie_core.hpp"
+#include "ngraph/ngraph.hpp"
+#include "transformations/serialize.hpp"
+
+#ifndef IR_SERIALIZATION_MODELS_PATH // should be already defined by cmake
+#define IR_SERIALIZATION_MODELS_PATH ""
+#endif
+
+class SerializationTransformationTest : public ::testing::Test {
+protected:
+ std::string test_name =
+ ::testing::UnitTest::GetInstance()->current_test_info()->name();
+ std::string m_out_xml_path = test_name + ".xml";
+ std::string m_out_bin_path = test_name + ".bin";
+ std::shared_ptr<ngraph::Function> m_function;
+
+ void SetUp() override {
+ const std::string model = IR_SERIALIZATION_MODELS_PATH "add_abc.xml";
+ const std::string weights = IR_SERIALIZATION_MODELS_PATH "add_abc.bin";
+ InferenceEngine::Core ie;
+ m_function = ie.ReadNetwork(model, weights).getFunction();
+ }
+
+ void TearDown() override {
+ std::remove(m_out_xml_path.c_str());
+ std::remove(m_out_bin_path.c_str());
+ }
+};
+
+TEST_F(SerializationTransformationTest, DirectInstantiation) {
+ ngraph::pass::Serialize transform{m_out_xml_path, m_out_bin_path};
+ transform.run_on_function(m_function);
+
+ std::ifstream xml(m_out_xml_path);
+ std::ifstream bin(m_out_bin_path);
+ ASSERT_TRUE(xml.good());
+ ASSERT_TRUE(bin.good());
+}
+
+TEST_F(SerializationTransformationTest, PassManagerInstantiation) {
+ ngraph::pass::Manager manager;
+ manager.register_pass<ngraph::pass::Serialize>(m_out_xml_path,
+ m_out_bin_path);
+ manager.run_passes(m_function);
+
+ std::ifstream xml(m_out_xml_path);
+ std::ifstream bin(m_out_bin_path);
+ ASSERT_TRUE(xml.good());
+ ASSERT_TRUE(bin.good());
+}
projects / platform / upstream / dldt.git / commitdiff