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未验证 提交 1af084cb 编写于 作者: A Alexander Smorkalov 提交者： GitHub
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Merge pull request #23477 from TolyaTalamanov:at/handle-multimeta-giebackend 

[G-API] Handle meta from multiple inputs in IE backend
上级 136121f6 66abbf41
隐藏空白更改
内联 并排
Showing with 47 addition and 15 deletion
modules/gapi/src/backends/ie/giebackend.cpp
浏览文件 @ 1af084cb
......@@ -456,6 +456,7 @@ public:
cv::gimpl::GIslandExecutable::IOutput & output,
const cv::GArgs & args,
const std::vector<cv::gimpl::RcDesc> & outs,
cv::GRunArg::Meta && meta,
std::vector<cv::gimpl::GIslandExecutable::InObj> && input_objs,
std::vector<cv::gimpl::GIslandExecutable::OutObj> && output_objs);
......@@ -477,9 +478,8 @@ public:
const cv::Mat& inMat (std::size_t input) const;
const cv::MediaFrame& inFrame(std::size_t input) const;
const cv::GRunArg& input (std::size_t idx) const;
cv::GRunArgP output (std::size_t idx);
cv::Mat& outMatR(std::size_t idx);
cv::GRunArgP output (std::size_t idx);
cv::Mat& outMatR(std::size_t idx);
const IEUnit &uu;
cv::gimpl::GIslandExecutable::IOutput &out;
......@@ -491,6 +491,8 @@ public:
// To store exception appeared in callback.
std::exception_ptr eptr;
const cv::GRunArg::Meta& getMeta() { return m_meta; };
using req_key_t = void*;
cv::MediaFrame* prepareKeepAliveFrameSlot(req_key_t key);
size_t releaseKeepAliveFrame(req_key_t key);
......@@ -499,6 +501,9 @@ private:
cv::GArg packArg(const cv::GArg &arg);
// To propagate accumulated meta from all inputs to output.
cv::GRunArg::Meta m_meta;
// To store input/output data from frames
std::vector<cv::gimpl::GIslandExecutable::InObj> m_input_objs;
std::vector<cv::gimpl::GIslandExecutable::OutObj> m_output_objs;
......@@ -525,9 +530,11 @@ IECallContext::IECallContext(const IEUnit &
cv::gimpl::GIslandExecutable::IOutput & output,
const cv::GArgs & args,
const std::vector<cv::gimpl::RcDesc> & outs,
cv::GRunArg::Meta && meta,
std::vector<cv::gimpl::GIslandExecutable::InObj> && input_objs,
std::vector<cv::gimpl::GIslandExecutable::OutObj> && output_objs)
: uu(unit), out(output), m_input_objs(std::move(input_objs)), m_output_objs(std::move(output_objs))
: uu(unit), out(output), m_meta(std::move(meta)),
m_input_objs(std::move(input_objs)), m_output_objs(std::move(output_objs))
{
for (auto& it : m_input_objs) cv::gimpl::magazine::bindInArg (m_res, it.first, it.second);
for (auto& it : m_output_objs) cv::gimpl::magazine::bindOutArg(m_res, it.first, it.second);
......@@ -575,10 +582,6 @@ cv::GRunArgP IECallContext::output(std::size_t idx) {
return m_output_objs[idx].second;
};
const cv::GRunArg& IECallContext::input(std::size_t idx) const {
return m_input_objs[idx].second;
}
cv::detail::VectorRef& IECallContext::outVecRef(std::size_t idx) {
return cv::util::get<cv::detail::VectorRef>(m_results.at(idx));
}
......@@ -1062,6 +1065,12 @@ void cv::gimpl::ie::GIEExecutable::run(cv::gimpl::GIslandExecutable::IInput &in
GAPI_Assert(cv::util::holds_alternative<cv::GRunArgs>(in_msg));
const auto in_vector = cv::util::get<cv::GRunArgs>(in_msg);
// NB: Collect meta from all inputs.
cv::GRunArg::Meta stub_meta;
for (auto &&in_arg : in_vector)
{
stub_meta.insert(in_arg.meta.begin(), in_arg.meta.end());
}
// (1) Collect island inputs/outputs
input_objs.reserve(in_desc.size());
......@@ -1084,7 +1093,7 @@ void cv::gimpl::ie::GIEExecutable::run(cv::gimpl::GIslandExecutable::IInput &in
const auto &op = m_gm.metadata(this_nh).get<Op>();
// (2) Create kernel context
auto ctx = std::make_shared<IECallContext>(uu, out, op.args, op.outs,
std::move(input_objs), std::move(output_objs));
std::move(stub_meta), std::move(input_objs), std::move(output_objs));
const auto &kk = giem.metadata(this_nh).get<IECallable>();
......@@ -1096,6 +1105,7 @@ void cv::gimpl::ie::GIEExecutable::run(cv::gimpl::GIslandExecutable::IInput &in
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), eptr);
}
return;
......@@ -1247,7 +1257,7 @@ static void PostOutputs(InferenceEngine::InferRequest &request,
IE::Blob::Ptr this_blob = request.GetBlob(ctx->uu.params.output_names[i]);
copyFromIE(this_blob, out_mat);
auto output = ctx->output(i);
ctx->out.meta(output, ctx->input(0).meta);
ctx->out.meta(output, ctx->getMeta());
ctx->out.post(std::move(output), ctx->eptr);
}
......@@ -1314,7 +1324,7 @@ void PostOutputsList::operator()(InferenceEngine::InferRequest &req,
if (finished == size) {
for (auto i : ade::util::iota(ctx->uu.params.num_out)) {
auto output = ctx->output(i);
ctx->out.meta(output, ctx->input(0).meta);
ctx->out.meta(output, ctx->getMeta());
ctx->out.post(std::move(output), ctx->eptr);
}
}
......@@ -1374,6 +1384,11 @@ struct Infer: public cv::detail::KernelTag {
}
}
for (auto &&p : uu.params.const_inputs) {
const auto ii = inputs.at(p.first);
ii->setPrecision(toIE(p.second.first.depth()));
}
// FIXME: This isn't the best place to call reshape function.
// Сorrect solution would be to do this in compile() method of network,
// but now input meta isn't passed to compile() method.
......@@ -1474,7 +1489,8 @@ struct InferROI: public cv::detail::KernelTag {
// only in the loadNetwork case.
if (uu.params.kind == cv::gapi::ie::detail::ParamDesc::Kind::Load) {
// 0th is ROI, 1st is input image
auto ii = uu.net.getInputsInfo().at(input_name);
auto inputs = uu.net.getInputsInfo();
auto ii = inputs.at(input_name);
configureInputInfo(ii, mm);
if (uu.params.layer_names_to_reshape.find(input_name) !=
uu.params.layer_names_to_reshape.end()) {
......@@ -1496,6 +1512,11 @@ struct InferROI: public cv::detail::KernelTag {
const_cast<IEUnit::InputFramesDesc &>(uu.net_input_params)
.set_param(input_name, ii->getTensorDesc());
}
for (auto &&p : uu.params.const_inputs) {
inputs.at(p.first)->setPrecision(toIE(p.second.first.depth()));
}
configureOutputPrecision(uu.net.getOutputsInfo(), uu.params.output_precision);
} else {
GAPI_Assert(uu.params.kind == cv::gapi::ie::detail::ParamDesc::Kind::Import);
......@@ -1614,6 +1635,12 @@ struct InferList: public cv::detail::KernelTag {
if (!input_reshape_table.empty()) {
const_cast<IE::CNNNetwork *>(&uu.net)->reshape(input_reshape_table);
}
for (auto &&p : uu.params.const_inputs) {
const auto ii = inputs.at(p.first);
ii->setPrecision(toIE(p.second.first.depth()));
}
configureOutputPrecision(uu.net.getOutputsInfo(), uu.params.output_precision);
} else {
GAPI_Assert(uu.params.kind == cv::gapi::ie::detail::ParamDesc::Kind::Import);
......@@ -1642,7 +1669,7 @@ struct InferList: public cv::detail::KernelTag {
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->input(0).meta);
ctx->out.meta(output, ctx->getMeta());
ctx->out.post(std::move(output));
}
return;
......@@ -1751,8 +1778,9 @@ struct InferList2: public cv::detail::KernelTag {
// NB: Configuring input precision and network reshape must be done
// only in the loadNetwork case.
if (uu.params.kind == cv::gapi::ie::detail::ParamDesc::Kind::Load) {
auto inputs = uu.net.getInputsInfo();
// This is a cv::Rect -- configure the IE preprocessing
auto ii = uu.net.getInputsInfo().at(input_name);
auto ii = inputs.at(input_name);
configureInputInfo(ii, mm_0);
if (uu.params.layer_names_to_reshape.find(input_name) !=
uu.params.layer_names_to_reshape.end()) {
......@@ -1762,6 +1790,10 @@ struct InferList2: public cv::detail::KernelTag {
ii->getPreProcess().setResizeAlgorithm(IE::RESIZE_BILINEAR);
}
for (auto &&p : uu.params.const_inputs) {
inputs.at(p.first)->setPrecision(toIE(p.second.first.depth()));
}
// FIXME: This isn't the best place to call reshape function.
// Сorrect solution would be to do this in compile() method of network,
// but now input meta isn't passed to compile() method.
......@@ -1806,7 +1838,7 @@ struct InferList2: public cv::detail::KernelTag {
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->input(0).meta);
ctx->out.meta(output, ctx->getMeta());
ctx->out.post(std::move(output));
}
return;
......
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