Remove ops from Node Converter- part 2 (#4181)
* remove NodeConverter for Interpolate and ScaleShiftIE * add SpecificCreators for Interpolate and ScaleShiftIE * add commented lines to check if tests will pass * remove comments to check if tests will pass * checking if tests are avaiable for ResampleV2 * remove NodeConvertor for ExecutionNode * remove nodeCovnertor for ResampleV2 and add SLT for serialization * remove shufflechannels op from node converter * enable visitor api for resamplev2 * remove nodeconverter class * add missing newline * remove interpolate sslt- sporadic fails * remove unnecessary interpolate specific creator * Add interpolate specific creator Co-authored-by: blesniewski <bartosz.lesniewski@intel.com>
This commit is contained in:
Bartek Szmelczynski
GitHub
parent
91f05008db
commit
78c045b7ae
@ -67,6 +67,8 @@ public:
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void validate_and_infer_types() override;
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bool visit_attributes(AttributeVisitor &visitor) override;
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std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
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ResampleIEAttrs get_attrs() { return m_attrs; }
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@ -22,6 +22,7 @@ public:
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const Output<Node>& weights,
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const Output<Node>& bias,
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const element::Type output_type = element::undefined);
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bool visit_attributes(AttributeVisitor& visitor) override;
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void validate_and_infer_types() override;
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@ -1616,6 +1616,14 @@ InferenceEngine::details::CNNLayerCreator::CNNLayerCreator(const std::shared_ptr
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return res;
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});
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addSpecificCreator({"Interpolate"}, [](const std::shared_ptr<::ngraph::Node>& node,
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const std::map<std::string, std::string>& params) -> CNNLayerPtr {
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LayerParams attrs = {node->get_friendly_name(), "Interpolate", details::convertPrecision(node->get_output_element_type(0))};
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auto res = std::make_shared<InferenceEngine::CNNLayer>(attrs);
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res->params = params;
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return res;
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});
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addSpecificCreator({"CropIE"}, [](const std::shared_ptr<::ngraph::Node> &node,
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const std::map<std::string, std::string> ¶ms) -> CNNLayerPtr {
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LayerParams attrs = {node->get_friendly_name(), "Crop", details::convertPrecision(node->get_output_element_type(0))};
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@ -1624,6 +1632,70 @@ InferenceEngine::details::CNNLayerCreator::CNNLayerCreator(const std::shared_ptr
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return res;
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});
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addSpecificCreator({"ScaleShiftIE"}, [](const std::shared_ptr<::ngraph::Node>& node,
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const std::map<std::string, std::string>& params) -> CNNLayerPtr {
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LayerParams attrs = {node->get_friendly_name(), "ScaleShift", details::convertPrecision(node->get_output_element_type(0))};
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auto res = std::make_shared<InferenceEngine::ScaleShiftLayer>(attrs);
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res->params = params;
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const auto weightsNode = node->input_value(1).get_node_shared_ptr();
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InferenceEngine::details::addBlob(weightsNode, res, InferenceEngine::details::weights);
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const auto biasNode = node->input_value(2).get_node_shared_ptr();
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InferenceEngine::details::addBlob(biasNode, res, InferenceEngine::details::biases);
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return res;
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});
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addSpecificCreator({"ExecutionNode"}, [](const std::shared_ptr<::ngraph::Node>& node,
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const std::map<std::string, std::string>& params) -> CNNLayerPtr {
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auto& rtInfo = node->get_rt_info();
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if (rtInfo.count(ExecGraphInfoSerialization::LAYER_TYPE) == 0) {
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THROW_IE_EXCEPTION << "No " << ExecGraphInfoSerialization::LAYER_TYPE
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<< " attribute is set in " << node->get_friendly_name() << " node";
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}
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auto getStringValue = [] (const std::shared_ptr<ngraph::Variant> & variant) {
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auto castedVariant = std::dynamic_pointer_cast<ngraph::VariantImpl<std::string>>(variant);
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IE_ASSERT(castedVariant != nullptr);
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return castedVariant->get();
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};
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LayerParams attrs = {node->get_friendly_name(),
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getStringValue(rtInfo[ExecGraphInfoSerialization::LAYER_TYPE]),
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details::convertPrecision(node->get_output_element_type(0))};
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rtInfo.erase(ExecGraphInfoSerialization::LAYER_TYPE);
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auto res = std::make_shared<InferenceEngine::CNNLayer>(attrs);
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res->params = params;
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for (const auto & kvp : rtInfo) {
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auto castedVariant = std::dynamic_pointer_cast<ngraph::VariantImpl<std::string>>(kvp.second);
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// skip RT info which holds fusedNames, etc
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if (castedVariant)
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res->params[kvp.first] = getStringValue(castedVariant);
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}
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return res;
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});
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addSpecificCreator({"ResampleV2"}, [](const std::shared_ptr<::ngraph::Node>& node,
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const std::map<std::string, std::string>& params) -> CNNLayerPtr {
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LayerParams attrs = {node->get_friendly_name(), "Resample", details::convertPrecision(node->get_output_element_type(0))};
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auto res = std::make_shared<InferenceEngine::CNNLayer>(attrs);
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res->params = params;
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res->params["antialias"] = res->getBoolStrParamAsIntStr("antialias");
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if (res->params["type"] == "nearest") {
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res->params["type"] = "caffe.ResampleParameter.NEAREST";
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} else if (res->params["type"] == "cubic") {
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res->params["type"] = "caffe.ResampleParameter.CUBIC";
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} else if (res->params["type"] == "area") {
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res->params["type"] = "caffe.ResampleParameter.AREA";
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} else if (res->params["type"] == "linear") {
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res->params["type"] = "caffe.ResampleParameter.LINEAR";
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}
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return res;
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});
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addSpecificCreator({"FullyConnected"}, [](const std::shared_ptr<::ngraph::Node> &node,
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const std::map<std::string, std::string> ¶ms) -> CNNLayerPtr {
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LayerParams attrs = {node->get_friendly_name(), "FullyConnected", details::convertPrecision(node->get_output_element_type(0))};
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@ -1673,6 +1745,14 @@ InferenceEngine::details::CNNLayerCreator::CNNLayerCreator(const std::shared_ptr
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return res;
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});
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addSpecificCreator({"ShuffleChannels"}, [](const std::shared_ptr<::ngraph::Node>& node,
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const std::map<std::string, std::string>& params) -> CNNLayerPtr {
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LayerParams attrs = {node->get_friendly_name(), "ShuffleChannels", details::convertPrecision(node->get_output_element_type(0))};
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auto res = std::make_shared<InferenceEngine::ShuffleChannelsLayer>(attrs);
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res->params = params;
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return res;
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});
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addSpecificCreator({"PowerIE"}, [](const std::shared_ptr<::ngraph::Node> &node,
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const std::map<std::string, std::string> ¶ms) -> CNNLayerPtr {
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LayerParams attrs = {node->get_friendly_name(), "Power", details::convertPrecision(node->get_output_element_type(0))};
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@ -1711,24 +1791,11 @@ void convertFunctionToICNNNetwork(const std::shared_ptr<const ::ngraph::Function
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this->node = node;
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}
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};
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static const std::vector<std::shared_ptr<Builder::INodeConverter>> convertors = {
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std::make_shared<Builder::NodeConverter<::ngraph::op::ResampleV2>>(),
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std::make_shared<Builder::NodeConverter<::ngraph::op::ScaleShiftIE>>(),
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std::make_shared<Builder::NodeConverter<::ngraph::op::ShuffleChannels>>(),
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std::make_shared<Builder::NodeConverter<::ngraph::op::v4::Interpolate>>(),
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std::make_shared<Builder::NodeConverter<::ExecGraphInfoSerialization::ExecutionNode>>(),
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};
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CNNLayerPtr result;
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for (auto &convertor : convertors) {
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if (!convertor->canCreate(node)) continue;
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result = convertor->createLayer(node);
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break;
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}
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if (!result) {
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CNNLayerCreator visitor(node);
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if (node->visit_attributes(visitor)) result = visitor.create();
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CNNLayerCreator visitor(node);
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if (node->visit_attributes(visitor)) {
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result = visitor.create();
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}
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if (!result)
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@ -1,264 +0,0 @@
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// Copyright (C) 2018-2020 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#include <limits>
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#include <cmath>
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#include <set>
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#include <sstream>
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#include <utility>
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#include "legacy/ngraph_ops/fully_connected.hpp"
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#include "legacy/ngraph_ops/interp.hpp"
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#include <transformations/rt_info/primitives_priority_attribute.hpp>
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#include "legacy/ngraph_ops/scaleshift.hpp"
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#include "exec_graph_info.hpp"
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#include <cnn_network_ngraph_impl.hpp>
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#include <precision_utils.h>
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#include <cpp/ie_cnn_network.h>
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#include <ngraph/ngraph.hpp>
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#include <ngraph/variant.hpp>
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#include <ngraph/opsets/opset5.hpp>
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#include <legacy/convert_function_to_cnn_network.hpp>
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#include "legacy/graph_transformer.h"
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#include "legacy/graph_tools.hpp"
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#include "legacy/net_pass.h"
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#include <legacy/cnn_network_impl.hpp>
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#include <ie_cnn_layer_builder_ngraph.h>
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namespace InferenceEngine {
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namespace Builder {
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template <>
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std::string asString<double>(const double& value) {
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std::ostringstream sStrm;
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sStrm.precision(std::numeric_limits<double>::digits10);
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sStrm << std::fixed << value;
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std::string result = sStrm.str();
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auto pos = result.find_last_not_of("0");
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if (pos != std::string::npos) result.erase(pos + 1);
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pos = result.find_last_not_of(".");
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if (pos != std::string::npos) result.erase(pos + 1);
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return result;
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}
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template <>
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std::string asString<float>(const float& value) {
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return asString(static_cast<double>(value));
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}
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template <>
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CNNLayer::Ptr NodeConverter<ngraph::op::ScaleShiftIE>::createLayer(const std::shared_ptr<ngraph::Node>& layer) const {
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LayerParams params = {layer->get_friendly_name(), "ScaleShift",
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details::convertPrecision(layer->get_output_element_type(0))};
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auto res = std::make_shared<InferenceEngine::ScaleShiftLayer>(params);
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const auto weightsNode = layer->input_value(1).get_node_shared_ptr();
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InferenceEngine::details::addBlob(weightsNode, res, InferenceEngine::details::weights);
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const auto biasNode = layer->input_value(2).get_node_shared_ptr();
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InferenceEngine::details::addBlob(biasNode, res, InferenceEngine::details::biases);
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return res;
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}
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template <>
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CNNLayer::Ptr NodeConverter<ngraph::op::ShuffleChannels>::createLayer(const std::shared_ptr<ngraph::Node>& layer) const {
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LayerParams params = {layer->get_friendly_name(), "ShuffleChannels", details::convertPrecision(layer->get_output_element_type(0))};
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auto res = std::make_shared<InferenceEngine::ShuffleChannelsLayer>(params);
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auto castedLayer = ngraph::as_type_ptr<ngraph::op::ShuffleChannels>(layer);
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if (castedLayer == nullptr) THROW_IE_EXCEPTION << "Cannot get " << params.type << " layer " << params.name;
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res->params["axis"] = std::to_string(castedLayer->get_axis());
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res->params["group"] = std::to_string(castedLayer->get_group());
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return res;
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}
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template <>
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CNNLayer::Ptr NodeConverter<ngraph::op::ResampleV2>::createLayer(const std::shared_ptr<ngraph::Node>& layer) const {
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LayerParams params = {layer->get_friendly_name(), "Resample", details::convertPrecision(layer->get_output_element_type(0))};
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auto res = std::make_shared<InferenceEngine::CNNLayer>(params);
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auto castedLayer = ngraph::as_type_ptr<ngraph::op::ResampleV2>(layer);
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if (castedLayer == nullptr)
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THROW_IE_EXCEPTION << "Cannot get " << params.type << " layer " << params.name;
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auto attrs = castedLayer->get_attrs();
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res->params["antialias"] = attrs.antialias ? "1" : "0";
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if (attrs.mode == "nearest") {
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res->params["type"] = "caffe.ResampleParameter.NEAREST";
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} else if (attrs.mode == "cubic") {
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res->params["type"] = "caffe.ResampleParameter.CUBIC";
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} else if (attrs.mode == "area") {
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res->params["type"] = "caffe.ResampleParameter.AREA";
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} else if (attrs.mode == "linear") {
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res->params["type"] = "caffe.ResampleParameter.LINEAR";
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}
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res->params["factor"] = asString(attrs.factor);
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return res;
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}
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template <>
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CNNLayer::Ptr NodeConverter<ngraph::op::v4::Interpolate>::createLayer(const std::shared_ptr<ngraph::Node>& layer) const {
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LayerParams params = {layer->get_friendly_name(), "Interpolate",
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details::convertPrecision(layer->get_output_element_type(0))};
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auto castedLayer = ngraph::as_type_ptr<ngraph::op::v4::Interpolate>(layer);
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if (castedLayer == nullptr) THROW_IE_EXCEPTION << "Cannot get " << params.type << " layer " << params.name;
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auto attrs = castedLayer->get_attrs();
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auto res = std::make_shared<InferenceEngine::CNNLayer>(params);
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switch (attrs.mode) {
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case ::ngraph::op::v4::Interpolate::InterpolateMode::nearest: {
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res->params["mode"] = "nearest";
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break;
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}
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case ::ngraph::op::v4::Interpolate::InterpolateMode::linear: {
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res->params["mode"] = "linear";
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break;
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}
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case ::ngraph::op::v4::Interpolate::InterpolateMode::linear_onnx: {
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res->params["mode"] = "linear_onnx";
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break;
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}
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case ::ngraph::op::v4::Interpolate::InterpolateMode::cubic: {
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res->params["mode"] = "cubic";
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break;
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}
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default:
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THROW_IE_EXCEPTION << "Unsupported mode for Interpolate op";
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break;
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}
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switch (attrs.shape_calculation_mode) {
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case ::ngraph::op::v4::Interpolate::ShapeCalcMode::sizes: {
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res->params["shape_calculation_mode"] = "sizes";
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break;
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}
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case ::ngraph::op::v4::Interpolate::ShapeCalcMode::scales: {
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res->params["shape_calculation_mode"] = "scales";
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break;
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}
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default:
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THROW_IE_EXCEPTION << "Unsupported shape_calculation_mode for Interpolate op";
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break;
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}
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switch (attrs.coordinate_transformation_mode) {
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case ::ngraph::op::v4::Interpolate::CoordinateTransformMode::half_pixel: {
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res->params["coordinate_transformation_mode"] = "half_pixel";
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break;
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}
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case ::ngraph::op::v4::Interpolate::CoordinateTransformMode::pytorch_half_pixel: {
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res->params["coordinate_transformation_mode"] = "pytorch_half_pixel";
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break;
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}
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case ::ngraph::op::v4::Interpolate::CoordinateTransformMode::asymmetric: {
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res->params["coordinate_transformation_mode"] = "asymmetric";
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break;
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}
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case ::ngraph::op::v4::Interpolate::CoordinateTransformMode::tf_half_pixel_for_nn: {
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res->params["coordinate_transformation_mode"] = "tf_half_pixel_for_nn";
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break;
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}
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case ::ngraph::op::v4::Interpolate::CoordinateTransformMode::align_corners: {
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res->params["coordinate_transformation_mode"] = "align_corners";
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break;
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}
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default:
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res->params["coordinate_transformation_mode"] = "half_pixel";
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break;
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}
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switch (attrs.nearest_mode) {
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case ::ngraph::op::v4::Interpolate::NearestMode::round_prefer_floor: {
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res->params["nearest_mode"] = "round_prefer_floor";
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break;
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}
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case ::ngraph::op::v4::Interpolate::NearestMode::round_prefer_ceil: {
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res->params["nearest_mode"] = "round_prefer_ceil";
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break;
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}
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case ::ngraph::op::v4::Interpolate::NearestMode::floor: {
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res->params["nearest_mode"] = "floor";
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break;
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}
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case ::ngraph::op::v4::Interpolate::NearestMode::ceil: {
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res->params["nearest_mode"] = "ceil";
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break;
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}
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case ::ngraph::op::v4::Interpolate::NearestMode::simple: {
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res->params["nearest_mode"] = "simple";
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break;
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}
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default:
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res->params["nearest_mode"] = "round_prefer_floor";
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break;
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}
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res->params["antialias"] = attrs.antialias ? "True" : "False";
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std::string value;
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for (const auto& val : attrs.pads_begin) {
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if (!value.empty()) value += ",";
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value += asString(val);
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}
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res->params["pads_begin"] = value;
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value.clear();
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for (const auto& val : attrs.pads_end) {
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if (!value.empty()) value += ",";
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value += asString(val);
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}
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res->params["pads_end"] = value;
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res->params["cube_coeff"] = asString(attrs.cube_coeff);
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return res;
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}
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template <>
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CNNLayer::Ptr NodeConverter<ExecGraphInfoSerialization::ExecutionNode>::createLayer(const std::shared_ptr<ngraph::Node>& layer) const {
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auto castedLayer = ngraph::as_type_ptr<ExecGraphInfoSerialization::ExecutionNode>(layer);
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if (castedLayer == nullptr)
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THROW_IE_EXCEPTION << "Cannot convert " << layer->get_friendly_name() << " layer ";
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auto & rtInfo = castedLayer->get_rt_info();
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if (rtInfo.count(ExecGraphInfoSerialization::LAYER_TYPE) == 0) {
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THROW_IE_EXCEPTION << "No " << ExecGraphInfoSerialization::LAYER_TYPE
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<< " attribute is set in " << layer->get_friendly_name() << " node";
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}
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auto getStringValue = [] (const std::shared_ptr<ngraph::Variant> & variant) {
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auto castedVariant = std::dynamic_pointer_cast<ngraph::VariantImpl<std::string>>(variant);
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IE_ASSERT(castedVariant != nullptr);
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return castedVariant->get();
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};
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LayerParams params = { layer->get_friendly_name(),
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getStringValue(rtInfo[ExecGraphInfoSerialization::LAYER_TYPE]),
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details::convertPrecision(layer->get_output_element_type(0)) };
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rtInfo.erase(ExecGraphInfoSerialization::LAYER_TYPE);
|
||||
|
||||
auto res = std::make_shared<InferenceEngine::CNNLayer>(params);
|
||||
for (const auto & kvp : rtInfo) {
|
||||
auto castedVariant = std::dynamic_pointer_cast<ngraph::VariantImpl<std::string>>(kvp.second);
|
||||
// skip RT info which holds fusedNames, etc
|
||||
if (castedVariant)
|
||||
res->params[kvp.first] = getStringValue(castedVariant);
|
||||
}
|
||||
|
||||
return res;
|
||||
}
|
||||
} // namespace Builder
|
||||
} // namespace InferenceEngine
|
||||
@ -24,13 +24,6 @@ namespace InferenceEngine {
|
||||
|
||||
namespace Builder {
|
||||
|
||||
class INodeConverter {
|
||||
public:
|
||||
virtual CNNLayer::Ptr createLayer(const std::shared_ptr<ngraph::Node>& layer) const = 0;
|
||||
virtual bool canCreate(const std::shared_ptr<ngraph::Node>& node) const = 0;
|
||||
virtual ~INodeConverter() = default;
|
||||
};
|
||||
|
||||
template <class T>
|
||||
std::string asString(const T& value) {
|
||||
return std::to_string(value);
|
||||
@ -47,24 +40,25 @@ std::string asString(const std::vector<T>& value) {
|
||||
}
|
||||
|
||||
template <>
|
||||
std::string asString<double>(const double& value);
|
||||
std::string asString<double>(const double& value) {
|
||||
std::ostringstream sStrm;
|
||||
sStrm.precision(std::numeric_limits<double>::digits10);
|
||||
sStrm << std::fixed << value;
|
||||
std::string result = sStrm.str();
|
||||
|
||||
auto pos = result.find_last_not_of("0");
|
||||
if (pos != std::string::npos) result.erase(pos + 1);
|
||||
|
||||
pos = result.find_last_not_of(".");
|
||||
if (pos != std::string::npos) result.erase(pos + 1);
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
template <>
|
||||
std::string asString<float>(const float& value);
|
||||
|
||||
template <class NGT>
|
||||
class NodeConverter : public INodeConverter {
|
||||
public:
|
||||
NodeConverter() = default;
|
||||
~NodeConverter() override = default;
|
||||
|
||||
CNNLayer::Ptr createLayer(const std::shared_ptr<ngraph::Node>& layer) const override;
|
||||
|
||||
bool canCreate(const std::shared_ptr<ngraph::Node>& node) const override {
|
||||
auto castedPtr = ngraph::as_type_ptr<NGT>(node);
|
||||
return castedPtr != nullptr;
|
||||
}
|
||||
};
|
||||
std::string asString<float>(const float& value) {
|
||||
return asString(static_cast<double>(value));
|
||||
}
|
||||
|
||||
} // namespace Builder
|
||||
} // namespace InferenceEngine
|
||||
|
||||
@ -115,3 +115,10 @@ shared_ptr<Node> op::ResampleV2::clone_with_new_inputs(const OutputVector& new_a
|
||||
check_new_args_count(this, new_args);
|
||||
return make_shared<ResampleV2>(new_args.at(0), new_args.at(1), m_attrs);
|
||||
}
|
||||
|
||||
bool op::ResampleV2::visit_attributes(AttributeVisitor& visitor) {
|
||||
visitor.on_attribute("antialias", m_attrs.antialias);
|
||||
visitor.on_attribute("factor", m_attrs.factor);
|
||||
visitor.on_attribute("mode", m_attrs.mode);
|
||||
return true;
|
||||
}
|
||||
|
||||
@ -57,3 +57,7 @@ void op::ScaleShiftIE::validate_and_infer_types() {
|
||||
|
||||
set_output_type(0, data_et, get_input_partial_shape(0));
|
||||
}
|
||||
|
||||
bool ngraph::op::ScaleShiftIE::visit_attributes(AttributeVisitor& visitor) {
|
||||
return true;
|
||||
}
|
||||
|
||||
@ -352,7 +352,7 @@ TEST_F(NGraphReaderTests, ReadInterpolate4Network) {
|
||||
</blobs>
|
||||
</layer>
|
||||
<layer id="4" name="interpolate" precision="FP32" type="Interpolate">
|
||||
<data antialias="False" coordinate_transformation_mode="asymmetric" cube_coeff="123" mode="nearest" nearest_mode="floor" pads_begin="2,3,4,5" pads_end="6,7,8,9" shape_calculation_mode="sizes"/>
|
||||
<data antialias="false" coordinate_transformation_mode="asymmetric" cube_coeff="123" mode="nearest" nearest_mode="floor" pads_begin="2,3,4,5" pads_end="6,7,8,9" shape_calculation_mode="sizes"/>
|
||||
<input>
|
||||
<port id="0">
|
||||
<dim>1</dim>
|
||||
|
||||
Loading…
Reference in New Issue
Block a user