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Merge branch 4.x

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This commit is contained in:
Alexander Smorkalov
2023-07-27 18:38:25 +03:00
parent 0e8748746f 677a28fd2a
commit 47188b7c7e
151 changed files with 5199 additions and 1111 deletions
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modules/gapi/src/backends/ie/giebackend.cpp
+31 -2
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@@ -949,7 +949,11 @@ inline IE::Blob::Ptr extractBlob(IECallContext& ctx,
auto y_blob = ctx.uu.rctx->CreateBlob(blob_params->first.first, blob_params->first.second);
auto uv_blob = ctx.uu.rctx->CreateBlob(blob_params->second.first, blob_params->second.second);
#if INF_ENGINE_RELEASE >= 2021010000
#if INF_ENGINE_RELEASE > 2023000000
cv::util::throw_error(std::logic_error(
"IE Backend: NV12 feature has been deprecated in OpenVINO 1.0 API."
" The last version which supports this is 2023.0"));
#elif INF_ENGINE_RELEASE >= 2021010000
return IE::make_shared_blob<IE::NV12Blob>(y_blob, uv_blob);
#else
return IE::make_shared_blob<InferenceEngine::NV12Blob>(y_blob, uv_blob);
@@ -982,7 +986,14 @@ static void setBlob(InferenceEngine::InferRequest& req,
req.SetBlob(layer_name, blob);
} else {
GAPI_Assert(ctx.uu.params.kind == ParamDesc::Kind::Import);
#if INF_ENGINE_RELEASE > 2023000000
// NB: SetBlob overload which accepts IE::PreProcessInfo
// has been deprecated - preprocessing can't be configured
// for "Import" networks anymore.
req.SetBlob(layer_name, blob);
#else
req.SetBlob(layer_name, blob, ctx.uu.preproc_map.at(layer_name));
#endif
}
}
@@ -1370,7 +1381,14 @@ static void cfgImagePreprocessing(const IE::InputInfo::Ptr &ii,
if (cv::util::holds_alternative<cv::GFrameDesc>(mm)) {
const auto &meta = util::get<cv::GFrameDesc>(mm);
if (meta.fmt == cv::MediaFormat::NV12) {
#if INF_ENGINE_RELEASE > 2023000000
cv::util::throw_error(std::logic_error(
"IE Backend: cv::MediaFrame with NV12 format is no longer supported"
" because NV12 feature has been deprecated in OpenVINO 1.0 API."
" The last version which supports this is 2023.0"));
#else
ii->getPreProcess().setColorFormat(IE::ColorFormat::NV12);
#endif
}
}
}
@@ -1426,7 +1444,14 @@ static IE::PreProcessInfo createImagePreProcInfo(const cv::GMetaArg &mm,
if (cv::util::holds_alternative<cv::GFrameDesc>(mm)) {
const auto &meta = util::get<cv::GFrameDesc>(mm);
if (meta.fmt == cv::MediaFormat::NV12) {
#if INF_ENGINE_RELEASE > 2023000000
cv::util::throw_error(std::logic_error(
"IE Backend: cv::MediaFrame with NV12 format is no longer supported"
" because NV12 feature has been deprecated in OpenVINO 1.0 API."
" The last version which supports this is 2023.0"));
#else
info.setColorFormat(IE::ColorFormat::NV12);
#endif
}
}
return info;
@@ -2299,7 +2324,11 @@ IE::Blob::Ptr cv::gapi::ie::util::to_ie(const cv::Mat &blob) {
IE::Blob::Ptr cv::gapi::ie::util::to_ie(const cv::Mat &y_plane, const cv::Mat &uv_plane) {
auto y_blob = wrapIE(y_plane, cv::gapi::ie::TraitAs::IMAGE);
auto uv_blob = wrapIE(uv_plane, cv::gapi::ie::TraitAs::IMAGE);
#if INF_ENGINE_RELEASE >= 2021010000
#if INF_ENGINE_RELEASE > 2023000000
cv::util::throw_error(std::logic_error(
"IE Backend: NV12 feature has been deprecated in OpenVINO 1.0 API."
" The last version which supports this is 2023.0"));
#elif INF_ENGINE_RELEASE >= 2021010000
return IE::make_shared_blob<IE::NV12Blob>(y_blob, uv_blob);
#else
return IE::make_shared_blob<InferenceEngine::NV12Blob>(y_blob, uv_blob);
modules/gapi/src/backends/onnx/bindings_onnx.cpp
+18
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@@ -21,6 +21,24 @@ cv::gapi::onnx::PyParams& cv::gapi::onnx::PyParams::cfgNormalize(const std::stri
return *this;
}
cv::gapi::onnx::PyParams&
cv::gapi::onnx::PyParams::cfgAddExecutionProvider(cv::gapi::onnx::ep::OpenVINO ep) {
m_priv->cfgAddExecutionProvider(std::move(ep));
return *this;
}
cv::gapi::onnx::PyParams&
cv::gapi::onnx::PyParams::cfgAddExecutionProvider(cv::gapi::onnx::ep::DirectML ep) {
m_priv->cfgAddExecutionProvider(std::move(ep));
return *this;
}
cv::gapi::onnx::PyParams&
cv::gapi::onnx::PyParams::cfgDisableMemPattern() {
m_priv->cfgDisableMemPattern();
return *this;
}
cv::gapi::GBackend cv::gapi::onnx::PyParams::backend() const {
return m_priv->backend();
}
modules/gapi/src/backends/onnx/dml_ep.cpp
+40
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@@ -0,0 +1,40 @@
// 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) 2023 Intel Corporation
#include "backends/onnx/dml_ep.hpp"
#include "logger.hpp"
#ifdef HAVE_ONNX
#include <onnxruntime_cxx_api.h>
#ifdef HAVE_ONNX_DML
#include "../providers/dml/dml_provider_factory.h"
void cv::gimpl::onnx::addDMLExecutionProvider(Ort::SessionOptions *session_options,
const cv::gapi::onnx::ep::DirectML &dml_ep) {
namespace ep = cv::gapi::onnx::ep;
GAPI_Assert(cv::util::holds_alternative<int>(dml_ep.ddesc));
const int device_id = cv::util::get<int>(dml_ep.ddesc);
try {
OrtSessionOptionsAppendExecutionProvider_DML(*session_options, device_id);
} catch (const std::exception &e) {
std::stringstream ss;
ss << "ONNX Backend: Failed to enable DirectML"
<< " Execution Provider: " << e.what();
cv::util::throw_error(std::runtime_error(ss.str()));
}
}
#else // HAVE_ONNX_DML
void cv::gimpl::onnx::addDMLExecutionProvider(Ort::SessionOptions*,
const cv::gapi::onnx::ep::DirectML&) {
util::throw_error(std::runtime_error("G-API has been compiled with ONNXRT"
" without DirectML support"));
}
#endif // HAVE_ONNX_DML
#endif // HAVE_ONNX
modules/gapi/src/backends/onnx/dml_ep.hpp
+23
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@@ -0,0 +1,23 @@
// 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) 2023 Intel Corporation
#ifndef OPENCV_GAPI_DML_EP_HPP
#define OPENCV_GAPI_DML_EP_HPP
#include "opencv2/gapi/infer/onnx.hpp"
#ifdef HAVE_ONNX
#include <onnxruntime_cxx_api.h>
namespace cv {
namespace gimpl {
namespace onnx {
void addDMLExecutionProvider(Ort::SessionOptions *session_options,
const cv::gapi::onnx::ep::DirectML &dml_ep);
}}}
#endif // HAVE_ONNX
#endif // OPENCV_GAPI_DML_EP_HPP
modules/gapi/src/backends/onnx/gonnxbackend.cpp
+52 -1
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@@ -9,6 +9,8 @@
#ifdef HAVE_ONNX
#include "backends/onnx/dml_ep.hpp"
#include <ade/util/algorithm.hpp> // any_of
#include <ade/util/zip_range.hpp>
#include <opencv2/gapi/infer.hpp>
@@ -143,6 +145,48 @@ public:
void run();
};
static void addOpenVINOExecutionProvider(Ort::SessionOptions *session_options,
const cv::gapi::onnx::ep::OpenVINO &ov_ep) {
OrtOpenVINOProviderOptions options;
options.device_type = ov_ep.device_type.c_str();
options.cache_dir = ov_ep.cache_dir.c_str();
options.num_of_threads = ov_ep.num_of_threads;
options.enable_opencl_throttling = ov_ep.enable_opencl_throttling;
options.enable_dynamic_shapes = ov_ep.enable_dynamic_shapes;
options.context = nullptr;
try {
session_options->AppendExecutionProvider_OpenVINO(options);
} catch (const std::exception &e) {
std::stringstream ss;
ss << "ONNX Backend: Failed to enable OpenVINO"
<< " Execution Provider: " << e.what();
cv::util::throw_error(std::runtime_error(ss.str()));
}
}
static void addExecutionProvider(Ort::SessionOptions *session_options,
const cv::gapi::onnx::ep::EP &execution_provider) {
namespace ep = cv::gapi::onnx::ep;
switch (execution_provider.index()) {
case ep::EP::index_of<ep::OpenVINO>(): {
GAPI_LOG_INFO(NULL, "OpenVINO Execution Provider is added.");
const auto &ov_ep = cv::util::get<ep::OpenVINO>(execution_provider);
addOpenVINOExecutionProvider(session_options, ov_ep);
break;
}
case ep::EP::index_of<ep::DirectML>(): {
GAPI_LOG_INFO(NULL, "DirectML Execution Provider is added.");
const auto &dml_ep = cv::util::get<ep::DirectML>(execution_provider);
addDMLExecutionProvider(session_options, dml_ep);
break;
}
default:
GAPI_LOG_INFO(NULL, "CPU Execution Provider is added.");
break;
}
}
} // namespace onnx
} // namespace gimpl
} // namespace cv
@@ -592,9 +636,16 @@ ONNXCompiled::ONNXCompiled(const gapi::onnx::detail::ParamDesc &pp)
cv::util::throw_error(std::logic_error("Please specify output layer names for "
+ params.model_path));
}
// Create and initialize the ONNX session
Ort::SessionOptions session_options;
GAPI_LOG_INFO(NULL, "Adding Execution Providers for \"" << pp.model_path << "\"");
for (const auto &ep : pp.execution_providers) {
cv::gimpl::onnx::addExecutionProvider(&session_options, ep);
}
if (pp.disable_mem_pattern) {
session_options.DisableMemPattern();
}
this_env = Ort::Env(ORT_LOGGING_LEVEL_WARNING, "");
#ifndef _WIN32
this_session = Ort::Session(this_env, params.model_path.data(), session_options);
modules/gapi/src/backends/ov/govbackend.cpp
+661 -159
View File
@@ -18,6 +18,7 @@
#include <opencv2/gapi/gcommon.hpp>
#include <opencv2/gapi/infer/ov.hpp>
#include <opencv2/core/utils/configuration.private.hpp> // getConfigurationParameterBool
#if defined(HAVE_TBB)
# include <tbb/concurrent_queue.h> // FIXME: drop it from here!
@@ -37,11 +38,37 @@ template<typename T> using QueueClass = cv::gapi::own::concurrent_bounded_queue<
using ParamDesc = cv::gapi::ov::detail::ParamDesc;
static ov::Core getCore() {
// NB: Some of OV plugins fail during ov::Core destroying in specific cases.
// Solution is allocate ov::Core in heap and doesn't destroy it, which cause
// leak, but fixes tests on CI. This behaviour is configurable by using
// OPENCV_GAPI_INFERENCE_ENGINE_CORE_LIFETIME_WORKAROUND=0
static ov::Core create_OV_Core_pointer() {
// NB: 'delete' is never called
static ov::Core* core = new ov::Core();
return *core;
}
static ov::Core create_OV_Core_instance() {
static ov::Core core;
return core;
}
ov::Core cv::gapi::ov::wrap::getCore() {
// NB: to make happy memory leak tools use:
// - OPENCV_GAPI_INFERENCE_ENGINE_CORE_LIFETIME_WORKAROUND=0
static bool param_GAPI_INFERENCE_ENGINE_CORE_LIFETIME_WORKAROUND =
utils::getConfigurationParameterBool(
"OPENCV_GAPI_INFERENCE_ENGINE_CORE_LIFETIME_WORKAROUND",
#if defined(_WIN32) || defined(__APPLE__)
true
#else
false
#endif
);
return param_GAPI_INFERENCE_ENGINE_CORE_LIFETIME_WORKAROUND
? create_OV_Core_pointer() : create_OV_Core_instance();
}
static ov::AnyMap toOV(const ParamDesc::PluginConfigT &config) {
return {config.begin(), config.end()};
}
@@ -101,8 +128,8 @@ static int toCV(const ov::element::Type &type) {
static void copyFromOV(const ov::Tensor &tensor, cv::Mat &mat) {
const auto total = mat.total() * mat.channels();
if (tensor.get_element_type() != toOV(mat.depth()) ||
tensor.get_size() != total ) {
if (toCV(tensor.get_element_type()) != mat.depth() ||
tensor.get_size() != total ) {
std::stringstream ss;
ss << "Failed to copy data from ov::Tensor to cv::Mat."
<< " Data type or number of elements mismatch."
@@ -128,8 +155,8 @@ static void copyToOV(const cv::Mat &mat, ov::Tensor &tensor) {
// TODO: Ideally there should be check that mat and tensor
// dimensions are compatible.
const auto total = mat.total() * mat.channels();
if (tensor.get_element_type() != toOV(mat.depth()) ||
tensor.get_size() != total) {
if (toCV(tensor.get_element_type()) != mat.depth() ||
tensor.get_size() != total) {
std::stringstream ss;
ss << "Failed to copy data from cv::Mat to ov::Tensor."
<< " Data type or number of elements mismatch."
@@ -158,6 +185,14 @@ int cv::gapi::ov::util::to_ocv(const ::ov::element::Type &type) {
return toCV(type);
}
void cv::gapi::ov::util::to_ov(const cv::Mat &mat, ::ov::Tensor &tensor) {
copyToOV(mat, tensor);
}
void cv::gapi::ov::util::to_ocv(const ::ov::Tensor &tensor, cv::Mat &mat) {
copyFromOV(tensor, mat);
}
struct OVUnit {
static const char *name() { return "OVUnit"; }
@@ -167,7 +202,8 @@ struct OVUnit {
// FIXME: Can this logic be encapsulated to prevent checking every time?
if (cv::util::holds_alternative<ParamDesc::Model>(params.kind)) {
const auto desc = cv::util::get<ParamDesc::Model>(params.kind);
model = getCore().read_model(desc.model_path, desc.bin_path);
model = cv::gapi::ov::wrap::getCore()
.read_model(desc.model_path, desc.bin_path);
GAPI_Assert(model);
if (params.num_in == 1u && params.input_names.empty()) {
@@ -182,9 +218,8 @@ struct OVUnit {
std::ifstream file(cv::util::get<ParamDesc::CompiledModel>(params.kind).blob_path,
std::ios_base::in | std::ios_base::binary);
GAPI_Assert(file.is_open());
compiled_model = getCore().import_model(file,
params.device,
toOV(params.config));
compiled_model = cv::gapi::ov::wrap::getCore()
.import_model(file, params.device, toOV(params.config));
if (params.num_in == 1u && params.input_names.empty()) {
params.input_names = { compiled_model.inputs().begin()->get_any_name() };
@@ -197,9 +232,8 @@ struct OVUnit {
cv::gimpl::ov::OVCompiled compile() {
if (cv::util::holds_alternative<ParamDesc::Model>(params.kind)) {
compiled_model = getCore().compile_model(model,
params.device,
toOV(params.config));
compiled_model = cv::gapi::ov::wrap::getCore()
.compile_model(model, params.device, toOV(params.config));
}
return {compiled_model};
}
@@ -343,6 +377,15 @@ cv::GArg OVCallContext::packArg(const cv::GArg &arg) {
switch (ref.shape)
{
case cv::GShape::GMAT: return cv::GArg(m_res.slot<cv::Mat>()[ref.id]);
// Note: .at() is intentional for GArray as object MUST be already there
// (and constructed by either bindIn/Out or resetInternal)
case cv::GShape::GARRAY: return cv::GArg(m_res.slot<cv::detail::VectorRef>().at(ref.id));
// Note: .at() is intentional for GOpaque as object MUST be already there
// (and constructed by either bindIn/Out or resetInternal)
case cv::GShape::GOPAQUE: return cv::GArg(m_res.slot<cv::detail::OpaqueRef>().at(ref.id));
default:
cv::util::throw_error(std::logic_error("Unsupported GShape type"));
break;
@@ -547,6 +590,62 @@ static void PostOutputs(::ov::InferRequest &infer_request,
}
}
class PostOutputsList {
public:
PostOutputsList(size_t size,
std::shared_ptr<OVCallContext> ctx);
void operator()(::ov::InferRequest &infer_request,
std::exception_ptr eptr,
size_t pos) const;
private:
struct Priv {
std::atomic<size_t> finished{0u};
size_t size;
std::shared_ptr<OVCallContext> ctx;
};
std::shared_ptr<Priv> m_priv;
};
PostOutputsList::PostOutputsList(size_t size,
std::shared_ptr<OVCallContext> ctx)
: m_priv(new Priv{}) {
m_priv->size = size;
m_priv->ctx = ctx;
}
void PostOutputsList::operator()(::ov::InferRequest &infer_request,
std::exception_ptr eptr,
size_t pos) const {
auto&& ctx = m_priv->ctx;
auto&& finished = m_priv->finished;
auto&& size = m_priv->size;
ctx->eptr = eptr;
if (!ctx->eptr) {
for (auto i : ade::util::iota(ctx->uu.params.num_out)) {
std::vector<cv::Mat> &out_vec = ctx->outVecR<cv::Mat>(i);
const auto &out_name = ctx->uu.params.output_names[i];
const auto &out_tensor = infer_request.get_tensor(out_name);
out_vec[pos].create(toCV(out_tensor.get_shape()),
toCV(out_tensor.get_element_type()));
copyFromOV(out_tensor, out_vec[pos]);
}
}
++finished;
if (finished == size) {
for (auto i : ade::util::iota(ctx->uu.params.num_out)) {
auto output = ctx->output(i);
ctx->out.meta(output, ctx->getMeta());
ctx->out.post(std::move(output), ctx->eptr);
}
}
}
namespace cv {
namespace gimpl {
namespace ov {
@@ -594,6 +693,34 @@ cv::optional<V> lookUp(const std::map<K, V> &map, const K& key) {
return cv::util::make_optional(std::move(it->second));
}
// NB: This function is used to preprocess input image
// for InferROI, InferList, InferList2 kernels.
static cv::Mat preprocess(const cv::Mat &in_mat,
const cv::Rect &roi,
const ::ov::Shape &model_shape) {
cv::Mat out;
// FIXME: Since there is no information about H and W positions
// among tensor dimmensions assume that model layout is "NHWC".
// (In fact "NHWC" is the only right layout for preprocessing because
// it works only with images.
GAPI_Assert(model_shape.size() == 4u);
const auto H = model_shape[1];
const auto W = model_shape[2];
const auto C = model_shape[3];
// NB: Soft check that at least number of channels matches.
if (static_cast<int>(C) != in_mat.channels()) {
std::stringstream ss;
ss << "OV Backend: Failed to preprocess input data "
" (Number of channels mismatch)."
" Provided data: " << cv::descr_of(in_mat) <<
" and Model shape: " << model_shape;
util::throw_error(std::logic_error(ss.str()));
}
// NB: Crop roi and resize to model size.
cv::resize(in_mat(roi), out, cv::Size(W, H));
return out;
}
static bool isImage(const cv::GMatDesc &desc,
const ::ov::Shape &model_shape) {
return (model_shape.size() == 4u) &&
@@ -603,6 +730,203 @@ static bool isImage(const cv::GMatDesc &desc,
(desc.depth == CV_8U);
}
class PrePostProcWrapper {
public:
PrePostProcWrapper(std::shared_ptr<::ov::Model> &model,
const ParamDesc::Model &model_info,
const std::vector<std::string> &input_names,
const std::vector<std::string> &output_names)
: m_ppp(model),
m_model(model),
m_model_info(model_info),
m_input_names(input_names),
m_output_names(output_names) {
// NB: Do Reshape right away since it must be the first step of model modification
// and applicable for all infer kernels.
const auto new_shapes = broadcastLayerAttr(model_info.new_shapes, input_names);
m_model->reshape(toOV(new_shapes));
const auto &mi = m_model_info;
m_input_tensor_layout = broadcastLayerAttr(mi.input_tensor_layout, m_input_names);
m_input_model_layout = broadcastLayerAttr(mi.input_model_layout, m_input_names);
m_interpolation = broadcastLayerAttr(mi.interpolation, m_input_names);
m_mean_values = broadcastLayerAttr(mi.mean_values, m_input_names);
m_scale_values = broadcastLayerAttr(mi.scale_values, m_input_names);
m_interpolation = broadcastLayerAttr(mi.interpolation, m_input_names);
m_output_tensor_layout = broadcastLayerAttr(mi.output_tensor_layout, m_output_names);
m_output_model_layout = broadcastLayerAttr(mi.output_model_layout, m_output_names);
m_output_tensor_precision = broadcastLayerAttr(mi.output_tensor_precision, m_output_names);
};
void cfgLayouts(const std::string &input_name) {
auto &input_info = m_ppp.input(input_name);
const auto explicit_in_model_layout = lookUp(m_input_model_layout, input_name);
if (explicit_in_model_layout) {
input_info.model().set_layout(::ov::Layout(*explicit_in_model_layout));
} else if (m_model->input(input_name).get_shape().size() == 4u) {
// NB: Back compatibility with IR's without any layout information.
// Note that default is only applicable for 4D inputs in order to
// support auto resize for image use cases.
GAPI_LOG_WARNING(NULL, "Failed to find layout for input layer \""
<< input_name << "\" - NCHW is set by default");
const std::string default_layout = "NCHW";
input_info.model().set_layout(::ov::Layout(default_layout));
m_input_model_layout.emplace(input_name, default_layout);
}
const auto explicit_in_tensor_layout = lookUp(m_input_tensor_layout, input_name);
if (explicit_in_tensor_layout) {
input_info.tensor().set_layout(::ov::Layout(*explicit_in_tensor_layout));
}
}
void cfgScaleMean(const std::string &input_name) {
auto &input_info = m_ppp.input(input_name);
const auto mean_vec = lookUp(m_mean_values, input_name);
if (mean_vec) {
input_info.preprocess().mean(*mean_vec);
}
const auto scale_vec = lookUp(m_scale_values, input_name);
if (scale_vec) {
input_info.preprocess().scale(*scale_vec);
}
}
// FIXME: Decompose this...
void cfgPreProcessing(const std::string &input_name,
const cv::GMetaArg &input_meta,
const bool disable_img_resize = false) {
GAPI_Assert(cv::util::holds_alternative<cv::GMatDesc>(input_meta));
const auto &matdesc = cv::util::get<cv::GMatDesc>(input_meta);
const auto explicit_in_tensor_layout = lookUp(m_input_tensor_layout, input_name);
const auto explicit_in_model_layout = lookUp(m_input_model_layout, input_name);
const auto explicit_resize = lookUp(m_interpolation, input_name);
if (disable_img_resize && explicit_resize.has_value()) {
std::stringstream ss;
util::throw_error(std::logic_error(
"OV Backend: Resize for layer \"" + input_name + "\" will be performed"
" on host via OpenCV so explicitly configured resize is prohibited."));
}
const auto &input_shape = m_model->input(input_name).get_shape();
auto &input_info = m_ppp.input(input_name);
m_ppp.input(input_name).tensor().set_element_type(toOV(matdesc.depth));
if (isImage(matdesc, input_shape)) {
// NB: Image case - all necessary preprocessng is configured automatically.
GAPI_LOG_DEBUG(NULL, "OV Backend: Input: \"" << input_name << "\" is image.");
if (explicit_in_tensor_layout &&
*explicit_in_tensor_layout != "NHWC") {
std::stringstream ss;
ss << "OV Backend: Provided tensor layout " << *explicit_in_tensor_layout
<< " is not compatible with input data " << matdesc << " for layer \""
<< input_name << "\". Expecting NHWC";
util::throw_error(std::logic_error(ss.str()));
} else {
input_info.tensor().set_layout(::ov::Layout("NHWC"));
}
if (!disable_img_resize) {
input_info.tensor().set_spatial_static_shape(matdesc.size.height,
matdesc.size.width);
// NB: Even though resize is automatically configured
// user have an opportunity to specify the interpolation algorithm.
auto interp = explicit_resize
? toOVInterp(*explicit_resize)
: ::ov::preprocess::ResizeAlgorithm::RESIZE_LINEAR;
input_info.preprocess().resize(interp);
}
} else {
// NB: Tensor case - resize or layout conversions must be explicitly specified.
GAPI_LOG_DEBUG(NULL, "OV Backend: Input: \"" << input_name << "\" is tensor.");
if (explicit_resize) {
if (matdesc.isND()) {
// NB: ND case - need to obtain "H" and "W" positions
// in order to configure resize.
const auto model_layout = explicit_in_model_layout
? ::ov::Layout(*explicit_in_model_layout)
: ::ov::layout::get_layout(m_model->input(input_name));
if (!explicit_in_tensor_layout && model_layout.empty()) {
std::stringstream ss;
ss << "Resize for input layer: " << input_name
<< "can't be configured."
<< " Failed to extract H and W positions from layout.";
util::throw_error(std::logic_error(ss.str()));
} else {
const auto layout = explicit_in_tensor_layout
? ::ov::Layout(*explicit_in_tensor_layout) : model_layout;
auto H_idx = ::ov::layout::height_idx(layout);
auto W_idx = ::ov::layout::width_idx(layout);
// NB: If layout is "...HW", H position is -2.
if (H_idx < 0) H_idx = matdesc.dims.size() + H_idx;
if (W_idx < 0) W_idx = matdesc.dims.size() + W_idx;
GAPI_Assert(H_idx >= 0 && H_idx < static_cast<int>(matdesc.dims.size()));
GAPI_Assert(W_idx >= 0 && W_idx < static_cast<int>(matdesc.dims.size()));
input_info.tensor().set_spatial_static_shape(matdesc.dims[H_idx],
matdesc.dims[W_idx]);
input_info.preprocess().resize(toOVInterp(*explicit_resize));
}
} else {
// NB: 2D case - We know exactly where H and W...
input_info.tensor().set_spatial_static_shape(matdesc.size.height,
matdesc.size.width);
input_info.preprocess().resize(toOVInterp(*explicit_resize));
}
}
}
}
void cfgPostProcessing() {
for (const auto &output_name : m_output_names) {
const auto explicit_out_tensor_layout =
lookUp(m_output_tensor_layout, output_name);
if (explicit_out_tensor_layout) {
m_ppp.output(output_name).tensor()
.set_layout(::ov::Layout(*explicit_out_tensor_layout));
}
const auto explicit_out_model_layout =
lookUp(m_output_model_layout, output_name);
if (explicit_out_model_layout) {
m_ppp.output(output_name).model()
.set_layout(::ov::Layout(*explicit_out_model_layout));
}
const auto explicit_out_tensor_prec =
lookUp(m_output_tensor_precision, output_name);
if (explicit_out_tensor_prec) {
m_ppp.output(output_name).tensor()
.set_element_type(toOV(*explicit_out_tensor_prec));
}
}
}
void finalize() {
GAPI_LOG_DEBUG(NULL, "OV Backend: PrePostProcessor: " << m_ppp);
m_model = m_ppp.build();
}
private:
::ov::preprocess::PrePostProcessor m_ppp;
std::shared_ptr<::ov::Model> &m_model;
const ParamDesc::Model &m_model_info;
const std::vector<std::string> &m_input_names;
const std::vector<std::string> &m_output_names;
cv::gimpl::ov::AttrMap<std::string> m_input_tensor_layout;
cv::gimpl::ov::AttrMap<std::string> m_input_model_layout;
cv::gimpl::ov::AttrMap<int> m_interpolation;
cv::gimpl::ov::AttrMap<std::vector<float>> m_mean_values;
cv::gimpl::ov::AttrMap<std::vector<float>> m_scale_values;
cv::gimpl::ov::AttrMap<std::string> m_output_tensor_layout;
cv::gimpl::ov::AttrMap<std::string> m_output_model_layout;
cv::gimpl::ov::AttrMap<int> m_output_tensor_precision;
};
struct Infer: public cv::detail::KernelTag {
using API = cv::GInferBase;
static cv::gapi::GBackend backend() { return cv::gapi::ov::backend(); }
@@ -625,156 +949,21 @@ struct Infer: public cv::detail::KernelTag {
// NB: Pre/Post processing configuration avaiable only for read models.
if (cv::util::holds_alternative<ParamDesc::Model>(uu.params.kind)) {
const auto &model_info = cv::util::get<ParamDesc::Model>(uu.params.kind);
const auto new_shapes =
broadcastLayerAttr(model_info.new_shapes,
uu.params.input_names);
const_cast<std::shared_ptr<::ov::Model>&>(uu.model)->reshape(toOV(new_shapes));
auto& model = const_cast<std::shared_ptr<::ov::Model>&>(uu.model);
PrePostProcWrapper ppp {model, model_info,
uu.params.input_names, uu.params.output_names};
const auto input_tensor_layout =
broadcastLayerAttr(model_info.input_tensor_layout,
uu.params.input_names);
const auto input_model_layout =
broadcastLayerAttr(model_info.input_model_layout,
uu.params.input_names);
const auto interpolation = broadcastLayerAttr(model_info.interpolation,
uu.params.input_names);
const auto mean_values = broadcastLayerAttr(model_info.mean_values,
uu.params.input_names);
const auto scale_values = broadcastLayerAttr(model_info.scale_values,
uu.params.input_names);
// FIXME: Pre/Post processing step shouldn't be configured in this method.
::ov::preprocess::PrePostProcessor ppp(uu.model);
for (auto &&it : ade::util::zip(ade::util::toRange(uu.params.input_names),
ade::util::toRange(in_metas))) {
const auto &mm = std::get<1>(it);
GAPI_Assert(cv::util::holds_alternative<cv::GMatDesc>(mm));
const auto &matdesc = cv::util::get<cv::GMatDesc>(mm);
const auto &input_name = std::get<0>(it);
auto &input_info = ppp.input(input_name);
input_info.tensor().set_element_type(toOV(matdesc.depth));
const auto &mm = std::get<1>(it);
const auto explicit_in_model_layout = lookUp(input_model_layout, input_name);
if (explicit_in_model_layout) {
input_info.model().set_layout(::ov::Layout(*explicit_in_model_layout));
}
const auto explicit_in_tensor_layout = lookUp(input_tensor_layout, input_name);
if (explicit_in_tensor_layout) {
input_info.tensor().set_layout(::ov::Layout(*explicit_in_tensor_layout));
}
const auto explicit_resize = lookUp(interpolation, input_name);
// NB: Note that model layout still can't be empty.
// e.g If model converted to IRv11 without any additional
// info about layout via Model Optimizer.
const auto model_layout = ::ov::layout::get_layout(uu.model->input(input_name));
const auto &input_shape = uu.model->input(input_name).get_shape();
if (isImage(matdesc, input_shape)) {
// NB: Image case - all necessary preprocessng is configured automatically.
GAPI_LOG_DEBUG(NULL, "OV Backend: Input: \"" << input_name << "\" is image.");
// NB: Layout is already set just double check that
// user provided the correct one. In fact, there is only one correct for image.
if (explicit_in_tensor_layout &&
*explicit_in_tensor_layout != "NHWC") {
std::stringstream ss;
ss << "OV Backend: Provided tensor layout " << *explicit_in_tensor_layout
<< " is not compatible with input data " << matdesc << " for layer \""
<< input_name << "\". Expecting NHWC";
util::throw_error(std::logic_error(ss.str()));
}
input_info.tensor().set_layout(::ov::Layout("NHWC"));
input_info.tensor().set_spatial_static_shape(matdesc.size.height,
matdesc.size.width);
// NB: Even though resize is automatically configured
// user have an opportunity to specify the interpolation algorithm.
auto interp = explicit_resize
? toOVInterp(*explicit_resize)
: ::ov::preprocess::ResizeAlgorithm::RESIZE_LINEAR;
input_info.preprocess().resize(interp);
} else {
// NB: Tensor case - resize or layout conversions must be explicitly specified.
GAPI_LOG_DEBUG(NULL, "OV Backend: Input: \"" << input_name << "\" is tensor.");
if (explicit_resize) {
if (matdesc.isND()) {
// NB: ND case - need to obtain "H" and "W" positions
// in order to configure resize.
if (!explicit_in_tensor_layout && model_layout.empty()) {
std::stringstream ss;
ss << "Resize for input layer: " << input_name
<< "can't be configured."
<< " Failed to extract H and W positions from layout.";
util::throw_error(std::logic_error(ss.str()));
} else {
const auto layout = explicit_in_tensor_layout
? ::ov::Layout(*explicit_in_tensor_layout) : model_layout;
auto H_idx = ::ov::layout::height_idx(layout);
auto W_idx = ::ov::layout::width_idx(layout);
// NB: If layout is "...HW", H position is -2.
if (H_idx < 0) H_idx = matdesc.dims.size() + H_idx;
if (W_idx < 0) W_idx = matdesc.dims.size() + W_idx;
GAPI_Assert(H_idx >= 0 && H_idx < static_cast<int>(matdesc.dims.size()));
GAPI_Assert(W_idx >= 0 && W_idx < static_cast<int>(matdesc.dims.size()));
input_info.tensor().set_spatial_static_shape(matdesc.dims[H_idx],
matdesc.dims[W_idx]);
input_info.preprocess().resize(toOVInterp(*explicit_resize));
}
} else {
// NB: 2D case - We know exactly where H and W...
input_info.tensor().set_spatial_static_shape(matdesc.size.height,
matdesc.size.width);
input_info.preprocess().resize(toOVInterp(*explicit_resize));
}
}
}
// NB: Apply mean/scale as the last step of the preprocessing.
// Note that this can be applied to any input data if the
// position of "C" dimension is known.
const auto mean_vec = lookUp(mean_values, input_name);
if (mean_vec) {
input_info.preprocess().mean(*mean_vec);
}
const auto scale_vec = lookUp(scale_values, input_name);
if (scale_vec) {
input_info.preprocess().scale(*scale_vec);
}
ppp.cfgLayouts(input_name);
ppp.cfgPreProcessing(input_name, mm);
ppp.cfgScaleMean(input_name);
}
const auto output_tensor_layout =
broadcastLayerAttr(model_info.output_tensor_layout,
uu.params.output_names);
const auto output_model_layout =
broadcastLayerAttr(model_info.output_model_layout,
uu.params.output_names);
const auto output_tensor_precision =
broadcastLayerAttr(model_info.output_tensor_precision,
uu.params.output_names);
for (const auto &output_name : uu.params.output_names) {
const auto explicit_out_tensor_layout =
lookUp(output_tensor_layout, output_name);
if (explicit_out_tensor_layout) {
ppp.output(output_name).tensor()
.set_layout(::ov::Layout(*explicit_out_tensor_layout));
}
const auto explicit_out_model_layout =
lookUp(output_model_layout, output_name);
if (explicit_out_model_layout) {
ppp.output(output_name).model()
.set_layout(::ov::Layout(*explicit_out_model_layout));
}
const auto explicit_out_tensor_prec =
lookUp(output_tensor_precision, output_name);
if (explicit_out_tensor_prec) {
ppp.output(output_name).tensor()
.set_element_type(toOV(*explicit_out_tensor_prec));
}
}
GAPI_LOG_DEBUG(NULL, "OV Backend: PrePostProcessor: " << ppp);
const_cast<std::shared_ptr<::ov::Model>&>(uu.model) = ppp.build();
ppp.cfgPostProcessing();
ppp.finalize();
}
for (const auto &out_name : uu.params.output_names) {
@@ -815,6 +1004,313 @@ struct Infer: public cv::detail::KernelTag {
}
};
struct InferROI: public cv::detail::KernelTag {
using API = cv::GInferROIBase;
static cv::gapi::GBackend backend() { return cv::gapi::ov::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*/) {
cv::GMetaArgs result;
GConstGOVModel gm(gr);
const auto &uu = gm.metadata(nh).get<OVUnit>();
// Initialize input information
// FIXME: So far it is pretty limited
GAPI_Assert(1u == uu.params.input_names.size());
GAPI_Assert(2u == in_metas.size());
const auto &input_name = uu.params.input_names.at(0);
const auto &mm = in_metas.at(1u);
GAPI_Assert(cv::util::holds_alternative<cv::GMatDesc>(mm));
const auto &matdesc = cv::util::get<cv::GMatDesc>(mm);
const bool is_model = cv::util::holds_alternative<ParamDesc::Model>(uu.params.kind);
const auto &input_shape = is_model ? uu.model->input(input_name).get_shape()
: uu.compiled_model.input(input_name).get_shape();
if (!isImage(matdesc, input_shape)) {
util::throw_error(std::runtime_error(
"OV Backend: InferROI supports only image as the 1th argument"));
}
if (is_model) {
const auto &model_info = cv::util::get<ParamDesc::Model>(uu.params.kind);
auto& model = const_cast<std::shared_ptr<::ov::Model>&>(uu.model);
PrePostProcWrapper ppp {model, model_info,
uu.params.input_names, uu.params.output_names};
ppp.cfgLayouts(input_name);
ppp.cfgPreProcessing(input_name, mm, true /*disable_img_resize*/);
ppp.cfgScaleMean(input_name);
ppp.cfgPostProcessing();
ppp.finalize();
}
for (const auto &out_name : uu.params.output_names) {
cv::GMatDesc outm;
if (cv::util::holds_alternative<ParamDesc::Model>(uu.params.kind)) {
const auto &out = uu.model->output(out_name);
outm = cv::GMatDesc(toCV(out.get_element_type()),
toCV(out.get_shape()));
} else {
GAPI_Assert(cv::util::holds_alternative<ParamDesc::CompiledModel>(uu.params.kind));
const auto &out = uu.compiled_model.output(out_name);
outm = cv::GMatDesc(toCV(out.get_element_type()),
toCV(out.get_shape()));
}
result.emplace_back(std::move(outm));
}
return result;
}
static void run(std::shared_ptr<OVCallContext> ctx,
cv::gimpl::ov::RequestPool &reqPool) {
using namespace std::placeholders;
reqPool.getIdleRequest()->execute(
IInferExecutor::Task {
[ctx](::ov::InferRequest &infer_request) {
GAPI_Assert(ctx->uu.params.num_in == 1);
const auto &input_name = ctx->uu.params.input_names[0];
auto input_tensor = infer_request.get_tensor(input_name);
const auto &shape = input_tensor.get_shape();
const auto &roi = ctx->inArg<cv::detail::OpaqueRef>(0).rref<cv::Rect>();
const auto roi_mat = preprocess(ctx->inMat(1), roi, shape);
copyToOV(roi_mat, input_tensor);
},
std::bind(PostOutputs, _1, _2, ctx)
}
);
}
};
struct InferList: public cv::detail::KernelTag {
using API = cv::GInferListBase;
static cv::gapi::GBackend backend() { return cv::gapi::ov::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*/) {
GConstGOVModel gm(gr);
const auto &uu = gm.metadata(nh).get<OVUnit>();
// 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");
// NB: Pre/Post processing configuration avaiable only for read models.
if (cv::util::holds_alternative<ParamDesc::Model>(uu.params.kind)) {
const auto &model_info = cv::util::get<ParamDesc::Model>(uu.params.kind);
auto& model = const_cast<std::shared_ptr<::ov::Model>&>(uu.model);
PrePostProcWrapper ppp {model, model_info,
uu.params.input_names, uu.params.output_names};
size_t idx = 1u;
for (auto &&input_name : uu.params.input_names) {
const auto &mm = in_metas[idx++];
GAPI_Assert(cv::util::holds_alternative<cv::GMatDesc>(mm));
const auto &matdesc = cv::util::get<cv::GMatDesc>(mm);
const auto &input_shape = uu.model->input(input_name).get_shape();
if (!isImage(matdesc, input_shape)) {
util::throw_error(std::runtime_error(
"OV Backend: Only image is supported"
" as the " + std::to_string(idx) + "th argument for InferList"));
}
ppp.cfgLayouts(input_name);
ppp.cfgPreProcessing(input_name, mm, true /*disable_img_resize*/);
ppp.cfgScaleMean(input_name);
}
ppp.cfgPostProcessing();
ppp.finalize();
}
// 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(std::shared_ptr<OVCallContext> ctx,
cv::gimpl::ov::RequestPool &reqPool) {
const auto& in_roi_vec = ctx->inArg<cv::detail::VectorRef>(0u).rref<cv::Rect>();
// NB: In case there is no input data need to post output anyway
if (in_roi_vec.empty()) {
for (auto i : ade::util::iota(ctx->uu.params.num_out)) {
auto output = ctx->output(i);
ctx->out.meta(output, ctx->getMeta());
ctx->out.post(std::move(output));
}
return;
}
for (auto i : ade::util::iota(ctx->uu.params.num_out)) {
// FIXME: Isn't this should be done automatically
// by some resetInternalData(), etc? (Probably at the GExecutor level)
auto& out_vec = ctx->outVecR<cv::Mat>(i);
out_vec.clear();
out_vec.resize(in_roi_vec.size());
}
PostOutputsList callback(in_roi_vec.size(), ctx);
for (auto&& it : ade::util::indexed(in_roi_vec)) {
const auto pos = ade::util::index(it);
const auto &rc = ade::util::value(it);
reqPool.getIdleRequest()->execute(
IInferExecutor::Task {
[ctx, rc](::ov::InferRequest &infer_request) {
const auto &input_name = ctx->uu.params.input_names[0];
auto input_tensor = infer_request.get_tensor(input_name);
const auto &shape = input_tensor.get_shape();
const auto roi_mat = preprocess(ctx->inMat(1), rc, shape);
copyToOV(roi_mat, input_tensor);
},
std::bind(callback, std::placeholders::_1, std::placeholders::_2, pos)
}
);
}
}
};
struct InferList2: public cv::detail::KernelTag {
using API = cv::GInferList2Base;
static cv::gapi::GBackend backend() { return cv::gapi::ov::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*/) {
GConstGOVModel gm(gr);
const auto &uu = gm.metadata(nh).get<OVUnit>();
// 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");
const auto &op = gm.metadata(nh).get<Op>();
// In contrast to InferList, the InferList2 has only one
// "full-frame" image argument, and all the rest are arrays of
// ether ROI or blobs. So here we set the 0th arg image format
// to all inputs which are ROI-based (skipping the
// "blob"-based ones)
// FIXME: this is filtering not done, actually! GArrayDesc has
// no hint for its underlying type!
const auto &input_name_0 = uu.params.input_names.front();
const auto &mm_0 = in_metas[0u];
const auto &matdesc = cv::util::get<cv::GMatDesc>(mm_0);
const bool is_model = cv::util::holds_alternative<ParamDesc::Model>(uu.params.kind);
const auto &input_shape = is_model ? uu.model->input(input_name_0).get_shape()
: uu.compiled_model.input(input_name_0).get_shape();
if (!isImage(matdesc, input_shape)) {
util::throw_error(std::runtime_error(
"OV Backend: InferList2 supports only image as the 0th argument"));
}
if (is_model) {
const auto &model_info = cv::util::get<ParamDesc::Model>(uu.params.kind);
auto& model = const_cast<std::shared_ptr<::ov::Model>&>(uu.model);
PrePostProcWrapper ppp {model, model_info,
uu.params.input_names, uu.params.output_names};
size_t idx = 1u;
for (auto &&input_name : uu.params.input_names) {
GAPI_Assert(util::holds_alternative<cv::GArrayDesc>(in_metas[idx])
&& "Non-array inputs are not supported");
ppp.cfgLayouts(input_name);
if (op.k.inKinds[idx] == cv::detail::OpaqueKind::CV_RECT) {
ppp.cfgPreProcessing(input_name, mm_0, true /*disable_img_resize*/);
} else {
// This is a cv::GMat (equals to: cv::Mat)
// Just validate that it is really the type
// (other types are prohibited here)
GAPI_Assert(op.k.inKinds[idx] == cv::detail::OpaqueKind::CV_MAT);
}
ppp.cfgScaleMean(input_name);
idx++; // NB: Never forget to increment the counter
}
ppp.cfgPostProcessing();
ppp.finalize();
}
// 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(std::shared_ptr<OVCallContext> ctx,
cv::gimpl::ov::RequestPool &reqPool) {
GAPI_Assert(ctx->inArgs().size() > 1u
&& "This operation must have at least two arguments");
// NB: This blob will be used to make roi from its, so
// it should be treated as image
const auto list_size = ctx->inArg<cv::detail::VectorRef>(1u).size();
if (list_size == 0u) {
for (auto i : ade::util::iota(ctx->uu.params.num_out)) {
auto output = ctx->output(i);
ctx->out.meta(output, ctx->getMeta());
ctx->out.post(std::move(output));
}
return;
}
for (auto i : ade::util::iota(ctx->uu.params.num_out)) {
// FIXME: Isn't this should be done automatically
// by some resetInternalData(), etc? (Probably at the GExecutor level)
auto& out_vec = ctx->outVecR<cv::Mat>(i);
out_vec.clear();
out_vec.resize(list_size);
}
PostOutputsList callback(list_size, ctx);
for (const auto &list_idx : ade::util::iota(list_size)) {
reqPool.getIdleRequest()->execute(
IInferExecutor::Task {
[ctx, list_idx, list_size](::ov::InferRequest &infer_request) {
for (auto in_idx : ade::util::iota(ctx->uu.params.num_in)) {
const auto &this_vec = ctx->inArg<cv::detail::VectorRef>(in_idx+1u);
GAPI_Assert(this_vec.size() == list_size);
const auto &input_name = ctx->uu.params.input_names[in_idx];
auto input_tensor = infer_request.get_tensor(input_name);
const auto &shape = input_tensor.get_shape();
if (this_vec.getKind() == cv::detail::OpaqueKind::CV_RECT) {
const auto &vec = this_vec.rref<cv::Rect>();
const auto roi_mat = preprocess(ctx->inMat(0), vec[list_idx], shape);
copyToOV(roi_mat, input_tensor);
} else if (this_vec.getKind() == cv::detail::OpaqueKind::CV_MAT) {
const auto &vec = this_vec.rref<cv::Mat>();
const auto &mat = vec[list_idx];
copyToOV(mat, input_tensor);
} else {
GAPI_Assert(false &&
"OV Backend: Only Rect and Mat types are supported for InferList2");
}
}
},
std::bind(callback, std::placeholders::_1, std::placeholders::_2, list_idx)
} // task
);
} // for
}
};
} // namespace ov
} // namespace gimpl
} // namespace cv
@@ -858,7 +1354,10 @@ class GOVBackendImpl final: public cv::gapi::GBackend::Priv {
}
virtual cv::GKernelPackage auxiliaryKernels() const override {
return cv::gapi::kernels< cv::gimpl::ov::Infer >();
return cv::gapi::kernels< cv::gimpl::ov::Infer
, cv::gimpl::ov::InferROI
, cv::gimpl::ov::InferList
, cv::gimpl::ov::InferList2 >();
}
virtual bool controlsMerge() const override {
@@ -904,8 +1403,10 @@ cv::gimpl::ov::GOVExecutable::GOVExecutable(const ade::Graph &g,
case NodeType::OP:
if (this_nh == nullptr) {
this_nh = nh;
compiled = const_cast<OVUnit&>(ovm.metadata(this_nh).get<OVUnit>()).compile();
m_reqPool.reset(new RequestPool(createInferRequests(compiled.compiled_model, 1)));
const auto &unit = ovm.metadata(this_nh).get<OVUnit>();
compiled = const_cast<OVUnit&>(unit).compile();
m_reqPool.reset(new RequestPool(createInferRequests(
compiled.compiled_model, unit.params.nireq)));
}
else
util::throw_error(std::logic_error("Multi-node inference is not supported!"));
@@ -937,6 +1438,7 @@ void cv::gimpl::ov::GOVExecutable::run(cv::gimpl::GIslandExecutable::IInput &in
if (cv::util::holds_alternative<cv::gimpl::EndOfStream>(in_msg))
{
m_reqPool->waitAll();
out.post(cv::gimpl::EndOfStream{});
return;
}
modules/gapi/src/backends/ov/util.hpp
+9 -3
View File
@@ -22,13 +22,19 @@ namespace cv {
namespace gapi {
namespace ov {
namespace util {
// NB: These functions are EXPORTed to make them accessible by the
// test suite only.
GAPI_EXPORTS std::vector<int> to_ocv(const ::ov::Shape &shape);
GAPI_EXPORTS int to_ocv(const ::ov::element::Type &type);
}}}}
GAPI_EXPORTS void to_ov(const cv::Mat &mat, ::ov::Tensor &tensor);
GAPI_EXPORTS void to_ocv(const ::ov::Tensor &tensor, cv::Mat &mat);
} // namespace util
namespace wrap {
GAPI_EXPORTS ::ov::Core getCore();
} // namespace wrap
} // namespace ov
} // namespace gapi
} // namespace cv
#endif // HAVE_INF_ENGINE && INF_ENGINE_RELEASE >= 2022010000