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659ffaddb4
opencv/modules/gapi/src/backends/ie/giebackend.cpp
Brian Wignall 659ffaddb4 Fix spelling typos
2019-12-26 06:45:03 -05:00

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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
//
// Copyright (C) 2018 Intel Corporation
#include "precomp.hpp"
// needs to be included regardless if IE is present or not
// (cv::gapi::ie::backend() is still there and is defined always)
#include "backends/ie/giebackend.hpp"
#ifdef HAVE_INF_ENGINE
#if INF_ENGINE_RELEASE <= 2019010000
# error G-API IE module supports only OpenVINO IE >= 2019 R1
#endif
#include <functional>
#include <unordered_set>
#include <ade/util/algorithm.hpp>
#include <ade/util/range.hpp>
#include <ade/util/zip_range.hpp>
#include <ade/util/chain_range.hpp>
#include <ade/typed_graph.hpp>
#include <opencv2/core/utility.hpp>
#include <opencv2/core/utils/logger.hpp>
#include <opencv2/gapi/gcommon.hpp>
#include <opencv2/gapi/garray.hpp>
#include <opencv2/gapi/util/any.hpp>
#include <opencv2/gapi/gtype_traits.hpp>
#include <opencv2/gapi/infer.hpp>
#include "compiler/gobjref.hpp"
#include "compiler/gmodel.hpp"
#include "backends/ie/util.hpp"
#include "api/gbackend_priv.hpp" // FIXME: Make it part of Backend SDK!
namespace IE = InferenceEngine;
namespace {
inline IE::ROI toIE(const cv::Rect &rc) {
return IE::ROI
{ 0u
, static_cast<std::size_t>(rc.x)
, static_cast<std::size_t>(rc.y)
, static_cast<std::size_t>(rc.width)
, static_cast<std::size_t>(rc.height)
};
}
inline IE::SizeVector toIE(const cv::MatSize &sz) {
return cv::to_own<IE::SizeVector::value_type>(sz);
}
inline std::vector<int> toCV(const IE::SizeVector &vsz) {
std::vector<int> result;
result.reserve(vsz.size());
for (auto sz : vsz) {
result.push_back(ade::util::checked_cast<int>(sz));
}
return result;
}
inline IE::Layout toIELayout(const std::size_t ndims) {
static const IE::Layout lts[] = {
IE::Layout::SCALAR,
IE::Layout::C,
IE::Layout::NC,
IE::Layout::CHW,
IE::Layout::NCHW,
IE::Layout::NCDHW,
};
// FIXME: This is not really a good conversion,
// since it may also stand for NHWC/HW/CN/NDHWC data
CV_Assert(ndims < sizeof(lts) / sizeof(lts[0]));
return lts[ndims];
}
inline IE::Precision toIE(int depth) {
switch (depth) {
case CV_8U: return IE::Precision::U8;
case CV_32F: return IE::Precision::FP32;
default: GAPI_Assert(false && "Unsupported data type");
}
return IE::Precision::UNSPECIFIED;
}
inline int toCV(IE::Precision prec) {
switch (prec) {
case IE::Precision::U8: return CV_8U;
case IE::Precision::FP32: return CV_32F;
default: GAPI_Assert(false && "Unsupported data type");
}
return -1;
}
inline IE::TensorDesc toIE(const cv::Mat &mat, cv::gapi::ie::TraitAs hint) {
const auto &sz = mat.size;
// NB: For some reason RGB image is 2D image
// (since channel component is not counted here).
// Note: regular 2D vectors also fall into this category
if (sz.dims() == 2 && hint == cv::gapi::ie::TraitAs::IMAGE)
{
// NB: This logic is mainly taken from IE samples
const size_t pixsz = CV_ELEM_SIZE1(mat.type());
const size_t channels = mat.channels();
const size_t height = mat.size().height;
const size_t width = mat.size().width;
const size_t strideH = mat.step.buf[0];
const size_t strideW = mat.step.buf[1];
const bool is_dense =
strideW == pixsz * channels &&
strideH == strideW * width;
if (!is_dense)
cv::util::throw_error(std::logic_error("Doesn't support conversion"
" from non-dense cv::Mat"));
return IE::TensorDesc(toIE(mat.depth()),
IE::SizeVector{1, channels, height, width},
IE::Layout::NHWC);
}
return IE::TensorDesc(toIE(mat.depth()), toIE(sz), toIELayout(sz.dims()));
}
inline IE::Blob::Ptr wrapIE(const cv::Mat &mat, cv::gapi::ie::TraitAs hint) {
const auto tDesc = toIE(mat, hint);
switch (mat.depth()) {
// NB: Seems there's no way to create an untyped (T-less) Blob::Ptr
// in IE given only precision via TensorDesc. So we have to do this:
#define HANDLE(E,T) \
case CV_##E: return IE::make_shared_blob<T>(tDesc, const_cast<T*>(mat.ptr<T>()))
HANDLE(8U, uint8_t);
HANDLE(32F, float);
#undef HANDLE
default: GAPI_Assert(false && "Unsupported data type");
}
return IE::Blob::Ptr{};
}
template<class MatType>
inline void copyFromIE(const IE::Blob::Ptr &blob, MatType &mat) {
switch (blob->getTensorDesc().getPrecision()) {
#define HANDLE(E,T) \
case IE::Precision::E: std::copy_n(blob->buffer().as<T*>(), \
mat.total(), \
reinterpret_cast<T*>(mat.data)); \
break;
HANDLE(U8, uint8_t);
HANDLE(FP32, float);
#undef HANDLE
default: GAPI_Assert(false && "Unsupported data type");
}
}
// IE-specific metadata, represents a network with its parameters
struct IEUnit {
static const char *name() { return "IEModelConfig"; }
cv::gapi::ie::detail::ParamDesc params;
IE::CNNNetwork net;
IE::InputsDataMap inputs;
IE::OutputsDataMap outputs;
explicit IEUnit(const cv::gapi::ie::detail::ParamDesc &pp)
: params(pp) {
IE::CNNNetReader reader;
reader.ReadNetwork(params.model_path);
reader.ReadWeights(params.weights_path);
net = reader.getNetwork();
inputs = net.getInputsInfo();
outputs = net.getOutputsInfo();
// The practice shows that not all inputs and not all outputs
// are mandatory to specify in IE model.
// So what we're concerned here about is:
// if operation's (not topology's) input/output number is
// greater than 1, then we do care about input/output layer
// names. Otherwise, names are picked up automatically.
// TODO: Probably this check could be done at the API entry point? (gnet)
if (params.num_in > 1u && params.num_in != params.input_names.size()) {
cv::util::throw_error(std::logic_error("Please specify input layer names for "
+ params.model_path));
}
if (params.num_out > 1u && params.num_out != params.output_names.size()) {
cv::util::throw_error(std::logic_error("Please specify output layer names for "
+ params.model_path));
}
if (params.num_in == 1u && params.input_names.empty()) {
params.input_names = { inputs.begin()->first };
}
if (params.num_out == 1u && params.output_names.empty()) {
params.output_names = { outputs.begin()->first };
}
}
// This method is [supposed to be] called at Island compilation stage
// TODO: Move to a new OpenVINO Core API!
cv::gimpl::ie::IECompiled compile() const {
auto this_plugin = IE::PluginDispatcher().getPluginByDevice(params.device_id);
// Load extensions (taken from DNN module)
if (params.device_id == "CPU" || params.device_id == "FPGA")
{
const std::string suffixes[] = { "_avx2", "_sse4", ""};
const bool haveFeature[] = {
cv::checkHardwareSupport(CPU_AVX2),
cv::checkHardwareSupport(CPU_SSE4_2),
true
};
std::vector<std::string> candidates;
for (auto &&it : ade::util::zip(ade::util::toRange(suffixes),
ade::util::toRange(haveFeature)))
{
std::string suffix;
bool available = false;
std::tie(suffix, available) = it;
if (!available) continue;
#ifdef _WIN32
candidates.push_back("cpu_extension" + suffix + ".dll");
#elif defined(__APPLE__)
candidates.push_back("libcpu_extension" + suffix + ".so"); // built as loadable module
candidates.push_back("libcpu_extension" + suffix + ".dylib"); // built as shared library
#else
candidates.push_back("libcpu_extension" + suffix + ".so");
#endif // _WIN32
}
for (auto &&extlib : candidates)
{
try
{
this_plugin.AddExtension(IE::make_so_pointer<IE::IExtension>(extlib));
CV_LOG_INFO(NULL, "DNN-IE: Loaded extension plugin: " << extlib);
break;
}
catch(...)
{
CV_LOG_WARNING(NULL, "Failed to load IE extension " << extlib);
}
}
}
auto this_network = this_plugin.LoadNetwork(net, {}); // FIXME: 2nd parameter to be
// configurable via the API
auto this_request = this_network.CreateInferRequest();
// Bind const data to infer request
for (auto &&p : params.const_inputs) {
// FIXME: SetBlob is known to be inefficient,
// it is worth to make a customizable "initializer" and pass the
// cv::Mat-wrapped blob there to support IE's optimal "GetBlob idiom"
// Still, constant data is to set only once.
this_request.SetBlob(p.first, wrapIE(p.second.first, p.second.second));
}
return {this_plugin, this_network, this_request};
}
};
struct IECallContext
{
// Input parameters passed to an inference operation.
std::vector<cv::GArg> args;
//FIXME: avoid conversion of arguments from internal representation to OpenCV one on each call
//to OCV kernel. (This can be achieved by a two single time conversions in GCPUExecutable::run,
//once on enter for input and output arguments, and once before return for output arguments only
//FIXME: check if the above applies to this backend (taken from CPU)
std::unordered_map<std::size_t, cv::GRunArgP> results;
// Generic accessor API
template<typename T>
const T& inArg(std::size_t input) { return args.at(input).get<T>(); }
// Syntax sugar
const cv::gapi::own::Mat& inMat(std::size_t input) {
return inArg<cv::gapi::own::Mat>(input);
}
cv::gapi::own::Mat& outMatR(std::size_t output) {
return *cv::util::get<cv::gapi::own::Mat*>(results.at(output));
}
template<typename T> std::vector<T>& outVecR(std::size_t output) { // FIXME: the same issue
return outVecRef(output).wref<T>();
}
cv::detail::VectorRef& outVecRef(std::size_t output) {
return cv::util::get<cv::detail::VectorRef>(results.at(output));
}
};
struct IECallable {
static const char *name() { return "IERequestCallable"; }
// FIXME: Make IECallContext manage them all? (3->1)
using Run = std::function<void(cv::gimpl::ie::IECompiled &, const IEUnit &, IECallContext &)>;
Run run;
};
struct KImpl {
cv::gimpl::CustomMetaFunction::CM customMetaFunc;
IECallable::Run run;
};
// FIXME: Is there a way to take a typed graph (our GModel),
// and create a new typed graph _ATOP_ of that (by extending with a couple of
// new types?).
// Alternatively, is there a way to compose types graphs?
//
// If not, we need to introduce that!
using GIEModel = ade::TypedGraph
< cv::gimpl::Protocol
, cv::gimpl::Op
, cv::gimpl::NetworkParams
, cv::gimpl::CustomMetaFunction
, IEUnit
, IECallable
>;
// FIXME: Same issue with Typed and ConstTyped
using GConstGIEModel = ade::ConstTypedGraph
< cv::gimpl::Protocol
, cv::gimpl::Op
, cv::gimpl::NetworkParams
, cv::gimpl::CustomMetaFunction
, IEUnit
, IECallable
>;
} // anonymous namespace
// GCPUExcecutable implementation //////////////////////////////////////////////
cv::gimpl::ie::GIEExecutable::GIEExecutable(const ade::Graph &g,
const std::vector<ade::NodeHandle> &nodes)
: m_g(g), m_gm(m_g) {
// FIXME: Currently this backend is capable to run a single inference node only.
// Need to extend our island fusion with merge/not-to-merge decision making parametrization
GConstGIEModel iem(g);
for (auto &nh : nodes) {
switch (m_gm.metadata(nh).get<NodeType>().t) {
case NodeType::OP:
if (this_nh == nullptr) {
this_nh = nh;
this_iec = iem.metadata(this_nh).get<IEUnit>().compile();
}
else
util::throw_error(std::logic_error("Multi-node inference is not supported!"));
break;
case NodeType::DATA: {
m_dataNodes.push_back(nh);
const auto &desc = m_gm.metadata(nh).get<Data>();
if (desc.storage == Data::Storage::CONST_VAL) {
util::throw_error(std::logic_error("No const data please!"));
}
if (desc.storage == Data::Storage::INTERNAL) {
util::throw_error(std::logic_error("No internal data please!"));
}
break;
}
default: util::throw_error(std::logic_error("Unsupported NodeType type"));
}
}
}
// FIXME: Document what it does
cv::GArg cv::gimpl::ie::GIEExecutable::packArg(const cv::GArg &arg) {
// No API placeholders allowed at this point
// FIXME: this check has to be done somewhere in compilation stage.
GAPI_Assert( arg.kind != cv::detail::ArgKind::GMAT
&& arg.kind != cv::detail::ArgKind::GSCALAR
&& arg.kind != cv::detail::ArgKind::GARRAY);
if (arg.kind != cv::detail::ArgKind::GOBJREF) {
util::throw_error(std::logic_error("Inference supports G-types ONLY!"));
}
GAPI_Assert(arg.kind == cv::detail::ArgKind::GOBJREF);
// Wrap associated CPU object (either host or an internal one)
// FIXME: object can be moved out!!! GExecutor faced that.
const cv::gimpl::RcDesc &ref = arg.get<cv::gimpl::RcDesc>();
switch (ref.shape)
{
case GShape::GMAT: return GArg(m_res.slot<cv::gapi::own::Mat>()[ref.id]);
// Note: .at() is intentional for GArray as object MUST be already there
// (and constructed by either bindIn/Out or resetInternal)
case GShape::GARRAY: return GArg(m_res.slot<cv::detail::VectorRef>().at(ref.id));
default:
util::throw_error(std::logic_error("Unsupported GShape type"));
break;
}
}
void cv::gimpl::ie::GIEExecutable::run(std::vector<InObj> &&input_objs,
std::vector<OutObj> &&output_objs) {
// Update resources with run-time information - what this Island
// has received from user (or from another Island, or mix...)
// FIXME: Check input/output objects against GIsland protocol
for (auto& it : input_objs) magazine::bindInArg (m_res, it.first, it.second);
for (auto& it : output_objs) magazine::bindOutArg(m_res, it.first, it.second);
// FIXME: Running just a single node now.
// Not sure if need to support many of them, though
// FIXME: Make this island-unmergeable?
const auto &op = m_gm.metadata(this_nh).get<Op>();
// Initialize kernel's execution context:
// - Input parameters
IECallContext context;
context.args.reserve(op.args.size());
using namespace std::placeholders;
ade::util::transform(op.args,
std::back_inserter(context.args),
std::bind(&GIEExecutable::packArg, this, _1));
// - Output parameters.
for (const auto &out_it : ade::util::indexed(op.outs)) {
// FIXME: Can the same GArg type resolution mechanism be reused here?
const auto out_port = ade::util::index(out_it);
const auto out_desc = ade::util::value(out_it);
context.results[out_port] = magazine::getObjPtr(m_res, out_desc);
}
// And now trigger the execution
GConstGIEModel giem(m_g);
const auto &uu = giem.metadata(this_nh).get<IEUnit>();
const auto &kk = giem.metadata(this_nh).get<IECallable>();
kk.run(this_iec, uu, context);
for (auto &it : output_objs) magazine::writeBack(m_res, it.first, it.second);
}
namespace cv {
namespace gimpl {
namespace ie {
struct Infer: public cv::detail::KernelTag {
using API = cv::GInferBase;
static cv::gapi::GBackend backend() { return cv::gapi::ie::backend(); }
static KImpl kernel() { return KImpl{outMeta, run}; }
static cv::GMetaArgs outMeta(const ade::Graph &gr,
const ade::NodeHandle &nh,
const cv::GMetaArgs &in_metas,
const cv::GArgs &/*in_args*/) {
// Specify network's output layer metadata to the framework
// Also specify the input information to the IE from the framework
// NB: Have no clue if network's input [dimensions] may ever define
// its output dimensions. It seems possible with OpenCV DNN APIs
cv::GMetaArgs result;
GConstGIEModel gm(gr);
const auto &uu = gm.metadata(nh).get<IEUnit>();
// Initialize input information
// Note our input layers list order matches the API order and so
// meta order.
GAPI_Assert(uu.params.input_names.size() == in_metas.size()
&& "Known input layers count doesn't match input meta count");
for (auto &&it : ade::util::zip(ade::util::toRange(uu.params.input_names),
ade::util::toRange(in_metas))) {
auto &&ii = uu.inputs.at(std::get<0>(it));
const auto & mm = std::get<1>(it);
GAPI_Assert(util::holds_alternative<cv::GMatDesc>(mm)
&& "Non-GMat inputs are not supported");
const auto &meta = util::get<cv::GMatDesc>(mm);
ii->setPrecision(toIE(meta.depth));
ii->setLayout(meta.isND() ? IE::Layout::NCHW : IE::Layout::NHWC);
ii->getPreProcess().setResizeAlgorithm(IE::RESIZE_BILINEAR);
}
// FIXME: It would be nice here to have an exact number of network's
// input/output parameters. Probably GCall should store it here for us.
// It doesn't, as far as I know..
for (const auto &out_name : uu.params.output_names) {
// NOTE: our output_names vector follows the API order
// of this operation's outputs
const IE::DataPtr& ie_out = uu.outputs.at(out_name);
const IE::SizeVector dims = ie_out->getTensorDesc().getDims();
cv::GMatDesc outm(toCV(ie_out->getPrecision()),
toCV(ie_out->getTensorDesc().getDims()));
result.emplace_back(outm);
}
return result;
}
static void run(IECompiled &iec, const IEUnit &uu, IECallContext &ctx) {
// non-generic version for now:
// - assumes all inputs/outputs are always Mats
for (auto i : ade::util::iota(uu.params.num_in)) {
// TODO: Ideally we shouldn't do SetBlob() but GetBlob() instead,
// and redirect our data producers to this memory
// (A memory dialog comes to the picture again)
const cv::Mat this_mat = to_ocv(ctx.inMat(i));
// FIXME: By default here we trait our inputs as images.
// May be we need to make some more intelligence here about it
IE::Blob::Ptr this_blob = wrapIE(this_mat, cv::gapi::ie::TraitAs::IMAGE);
iec.this_request.SetBlob(uu.params.input_names[i], this_blob);
}
iec.this_request.Infer();
for (auto i : ade::util::iota(uu.params.num_out)) {
// TODO: Think on avoiding copying here.
// Either we should ask IE to use our memory (what is not always the
// best policy) or use IE-allocated buffer inside (and pass it to the graph).
// Not a <very> big deal for classifiers and detectors,
// but may be critical to segmentation.
cv::gapi::own::Mat& out_mat = ctx.outMatR(i);
IE::Blob::Ptr this_blob = iec.this_request.GetBlob(uu.params.output_names[i]);
copyFromIE(this_blob, out_mat);
}
}
};
struct InferList: public cv::detail::KernelTag {
using API = cv::GInferListBase;
static cv::gapi::GBackend backend() { return cv::gapi::ie::backend(); }
static KImpl kernel() { return KImpl{outMeta, run}; }
static cv::GMetaArgs outMeta(const ade::Graph &gr,
const ade::NodeHandle &nh,
const cv::GMetaArgs &in_metas,
const cv::GArgs &/*in_args*/) {
// Specify the input information to the IE from the framework
// NB: Have no clue if network's input [dimensions] may ever define
// its output dimensions. It seems possible with OpenCV DNN APIs
GConstGIEModel gm(gr);
const auto &uu = gm.metadata(nh).get<IEUnit>();
// Initialize input information
// Note our input layers list order matches the API order and so
// meta order.
GAPI_Assert(uu.params.input_names.size() == (in_metas.size() - 1u)
&& "Known input layers count doesn't match input meta count");
std::size_t idx = 1u;
for (auto &&input_name : uu.params.input_names) {
auto &&ii = uu.inputs.at(input_name);
const auto & mm = in_metas[idx++];
GAPI_Assert(util::holds_alternative<cv::GMatDesc>(mm)
&& "Non-GMat inputs are not supported");
const auto &meta = util::get<cv::GMatDesc>(mm);
ii->setPrecision(toIE(meta.depth));
ii->setLayout(meta.isND() ? IE::Layout::NCHW : IE::Layout::NHWC);
ii->getPreProcess().setResizeAlgorithm(IE::RESIZE_BILINEAR);
}
// roi-list version is much easier at the moment.
// All our outputs are vectors which don't have
// metadata at the moment - so just create a vector of
// "empty" array metadatas of the required size.
return cv::GMetaArgs(uu.params.output_names.size(),
cv::GMetaArg{cv::empty_array_desc()});
}
static void run(IECompiled &iec, const IEUnit &uu, IECallContext &ctx) {
// non-generic version for now:
// - assumes zero input is always ROI list
// - assumes all inputs/outputs are always Mats
GAPI_Assert(uu.params.num_in == 1); // roi list is not counted in net's inputs
const auto& in_roi_vec = ctx.inArg<cv::detail::VectorRef>(0u).rref<cv::Rect>();
const cv::Mat this_mat = to_ocv(ctx.inMat(1u));
// Since we do a ROI list inference, always assume our input buffer is image
IE::Blob::Ptr this_blob = wrapIE(this_mat, cv::gapi::ie::TraitAs::IMAGE);
// FIXME: This could be done ONCE at graph compile stage!
std::vector< std::vector<int> > cached_dims(uu.params.num_out);
for (auto i : ade::util::iota(uu.params.num_out)) {
const IE::DataPtr& ie_out = uu.outputs.at(uu.params.output_names[i]);
cached_dims[i] = toCV(ie_out->getTensorDesc().getDims());
ctx.outVecR<cv::Mat>(i).clear();
// FIXME: Isn't this should be done automatically
// by some resetInternalData(), etc? (Probably at the GExecutor level)
}
for (const auto &rc : in_roi_vec) {
// FIXME: Assumed only 1 input
IE::Blob::Ptr roi_blob = IE::make_shared_blob(this_blob, toIE(rc));
iec.this_request.SetBlob(uu.params.input_names[0u], roi_blob);
iec.this_request.Infer();
// While input is fixed to be 1,
// there may be still multiple outputs
for (auto i : ade::util::iota(uu.params.num_out)) {
std::vector<cv::Mat> &out_vec = ctx.outVecR<cv::Mat>(i);
IE::Blob::Ptr out_blob = iec.this_request.GetBlob(uu.params.output_names[i]);
cv::Mat out_mat(cached_dims[i], toCV(out_blob->getTensorDesc().getPrecision()));
copyFromIE(out_blob, out_mat); // FIXME: Avoid data copy. Not sure if it is possible though
out_vec.push_back(std::move(out_mat));
}
}
}
};
} // namespace ie
} // namespace gapi
} // namespace cv
// IE backend implementation of GBackend::Priv ///////////////////////
namespace {
class GIEBackendImpl final: public cv::gapi::GBackend::Priv {
virtual void unpackKernel(ade::Graph &gr,
const ade::NodeHandle &nh,
const cv::GKernelImpl &ii) override {
using namespace cv::gimpl;
// FIXME: Introduce a DNNBackend interface which'd specify
// the framework for this???
GIEModel gm(gr);
const auto &np = gm.metadata(nh).get<NetworkParams>();
const auto &pp = cv::util::any_cast<cv::gapi::ie::detail::ParamDesc>(np.opaque);
const auto &ki = cv::util::any_cast<KImpl>(ii.opaque);
gm.metadata(nh).set(IEUnit{pp});
gm.metadata(nh).set(IECallable{ki.run});
gm.metadata(nh).set(CustomMetaFunction{ki.customMetaFunc});
}
virtual EPtr compile(const ade::Graph &graph,
const cv::GCompileArgs &,
const std::vector<ade::NodeHandle> &nodes) const override {
return EPtr{new cv::gimpl::ie::GIEExecutable(graph, nodes)};
}
virtual cv::gapi::GKernelPackage auxiliaryKernels() const override {
return cv::gapi::kernels< cv::gimpl::ie::Infer
, cv::gimpl::ie::InferList
>();
}
};
}
cv::gapi::GBackend cv::gapi::ie::backend() {
static cv::gapi::GBackend this_backend(std::make_shared<GIEBackendImpl>());
return this_backend;
}
cv::Mat cv::gapi::ie::util::to_ocv(InferenceEngine::Blob::Ptr blob) {
const auto& tdesc = blob->getTensorDesc();
return cv::Mat(toCV(tdesc.getDims()),
toCV(tdesc.getPrecision()),
blob->buffer().as<uint8_t*>());
}
std::vector<int> cv::gapi::ie::util::to_ocv(const InferenceEngine::SizeVector &dims) {
return toCV(dims);
}
InferenceEngine::Blob::Ptr cv::gapi::ie::util::to_ie(cv::Mat &blob) {
return wrapIE(blob, cv::gapi::ie::TraitAs::IMAGE);
}
#else // HAVE_INF_ENGINE
cv::gapi::GBackend cv::gapi::ie::backend() {
// Still provide this symbol to avoid linking issues
util::throw_error(std::runtime_error("G-API has been compiled without OpenVINO IE support"));
}
#endif // HAVE_INF_ENGINE
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