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Merge branch '4.x' of github.com:opencv/opencv into at/sync-ie-request-pool
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@@ -197,6 +197,16 @@ inline IE::Blob::Ptr wrapIE(const cv::MediaFrame::View& view,
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template<class MatType>
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inline void copyFromIE(const IE::Blob::Ptr &blob, MatType &mat) {
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const auto& desc = blob->getTensorDesc();
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const auto ie_type = toCV(desc.getPrecision());
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if (ie_type != mat.type()) {
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std::stringstream ss;
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ss << "Failed to copy blob from IE to OCV: "
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<< "Blobs have different data types "
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<< "(IE type: " << ie_type
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<< " vs OCV type: " << mat.type() << ")." << std::endl;
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throw std::logic_error(ss.str());
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}
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switch (blob->getTensorDesc().getPrecision()) {
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#define HANDLE(E,T) \
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case IE::Precision::E: std::copy_n(blob->buffer().as<T*>(), \
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@@ -365,6 +375,13 @@ struct IEUnit {
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cv::util::throw_error(std::logic_error("Unsupported ParamDesc::Kind"));
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}
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if (params.kind == cv::gapi::ie::detail::ParamDesc::Kind::Import &&
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!cv::util::holds_alternative<cv::util::monostate>(params.output_precision)) {
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cv::util::throw_error(
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std::logic_error("Setting output precision isn't supported for imported network"));
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}
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using namespace cv::gapi::wip::onevpl;
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if (params.vpl_preproc_device.has_value() && params.vpl_preproc_ctx.has_value()) {
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using namespace cv::gapi::wip;
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@@ -1188,6 +1205,28 @@ static IE::PreProcessInfo configurePreProcInfo(const IE::InputInfo::CPtr& ii,
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return info;
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}
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using namespace cv::gapi::ie::detail;
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static void configureOutputPrecision(const IE::OutputsDataMap &outputs_info,
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const ParamDesc::PrecisionVariantT &output_precision) {
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cv::util::visit(cv::util::overload_lambdas(
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[&outputs_info](ParamDesc::PrecisionT cvdepth) {
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auto precision = toIE(cvdepth);
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for (auto it : outputs_info) {
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it.second->setPrecision(precision);
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}
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},
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[&outputs_info](const ParamDesc::PrecisionMapT& precision_map) {
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for (auto it : precision_map) {
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outputs_info.at(it.first)->setPrecision(toIE(it.second));
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}
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},
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[&outputs_info](cv::util::monostate) {
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// Do nothing.
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}
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), output_precision
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);
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}
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// NB: This is a callback used by async infer
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// to post outputs blobs (cv::GMat's).
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static void PostOutputs(InferenceEngine::InferRequest &request,
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@@ -1307,7 +1346,7 @@ struct Infer: public cv::detail::KernelTag {
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GAPI_Assert(uu.params.input_names.size() == in_metas.size()
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&& "Known input layers count doesn't match input meta count");
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// NB: Configuring input precision and network reshape must be done
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// NB: Configuring input/output precision and network reshape must be done
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// only in the loadNetwork case.
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using namespace cv::gapi::ie::detail;
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if (uu.params.kind == ParamDesc::Kind::Load) {
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@@ -1341,6 +1380,7 @@ struct Infer: public cv::detail::KernelTag {
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if (!input_reshape_table.empty()) {
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const_cast<IE::CNNNetwork *>(&uu.net)->reshape(input_reshape_table);
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}
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configureOutputPrecision(uu.net.getOutputsInfo(), uu.params.output_precision);
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} else {
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GAPI_Assert(uu.params.kind == ParamDesc::Kind::Import);
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auto inputs = uu.this_network.GetInputsInfo();
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@@ -1456,6 +1496,7 @@ struct InferROI: public cv::detail::KernelTag {
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const_cast<IEUnit::InputFramesDesc &>(uu.net_input_params)
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.set_param(input_name, ii->getTensorDesc());
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}
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configureOutputPrecision(uu.net.getOutputsInfo(), uu.params.output_precision);
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} else {
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GAPI_Assert(uu.params.kind == cv::gapi::ie::detail::ParamDesc::Kind::Import);
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auto inputs = uu.this_network.GetInputsInfo();
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@@ -1573,6 +1614,7 @@ struct InferList: public cv::detail::KernelTag {
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if (!input_reshape_table.empty()) {
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const_cast<IE::CNNNetwork *>(&uu.net)->reshape(input_reshape_table);
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}
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configureOutputPrecision(uu.net.getOutputsInfo(), uu.params.output_precision);
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} else {
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GAPI_Assert(uu.params.kind == cv::gapi::ie::detail::ParamDesc::Kind::Import);
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std::size_t idx = 1u;
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@@ -1726,6 +1768,7 @@ struct InferList2: public cv::detail::KernelTag {
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if (!input_reshape_table.empty()) {
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const_cast<IE::CNNNetwork *>(&uu.net)->reshape(input_reshape_table);
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}
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configureOutputPrecision(uu.net.getOutputsInfo(), uu.params.output_precision);
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} else {
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GAPI_Assert(uu.params.kind == cv::gapi::ie::detail::ParamDesc::Kind::Import);
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auto inputs = uu.this_network.GetInputsInfo();
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