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Merge branch 4.x

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This commit is contained in:
Alexander Smorkalov
2023-06-01 09:37:38 +03:00
parent d24ffe9a65 fc5d412ba7
commit 5af40a0269
565 changed files with 84396 additions and 17589 deletions
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modules/gapi/src/backends/ie/giebackend.cpp
+47 -15
View File
@@ -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;
modules/gapi/src/backends/onnx/bindings_onnx.cpp
+13
View File
@@ -8,6 +8,19 @@ cv::gapi::onnx::PyParams::PyParams(const std::string& tag,
const std::string& model_path)
: m_priv(std::make_shared<Params<cv::gapi::Generic>>(tag, model_path)) {}
cv::gapi::onnx::PyParams& cv::gapi::onnx::PyParams::cfgMeanStd(const std::string &layer_name,
const cv::Scalar &m,
const cv::Scalar &s) {
m_priv->cfgMeanStdDev(layer_name, m, s);
return *this;
}
cv::gapi::onnx::PyParams& cv::gapi::onnx::PyParams::cfgNormalize(const std::string &layer_name,
bool flag) {
m_priv->cfgNormalize(layer_name, flag);
return *this;
}
cv::gapi::GBackend cv::gapi::onnx::PyParams::backend() const {
return m_priv->backend();
}
modules/gapi/src/backends/onnx/gonnxbackend.cpp
+23 -15
View File
@@ -44,7 +44,8 @@ static std::string pdims(const std::vector<int64_t> &dims) {
struct TensorInfo {
TensorInfo() = default;
explicit TensorInfo(const Ort::TensorTypeAndShapeInfo& info)
explicit TensorInfo(const Ort::ConstTensorTypeAndShapeInfo &info)
: dims(info.GetShape())
, type(info.GetElementType())
, is_dynamic(ade::util::find(dims, -1) != dims.end()) {
@@ -283,6 +284,7 @@ inline void preprocess(const cv::Mat& src,
cv::resize(csc, rsz, cv::Size(new_w, new_h));
if (src.depth() == CV_8U && type == CV_32F) {
rsz.convertTo(pp, type, ti.normalize ? 1.f / 255 : 1.f);
if (ti.mstd.has_value()) {
pp -= ti.mstd->mean;
pp /= ti.mstd->stdev;
@@ -688,11 +690,10 @@ std::vector<TensorInfo> ONNXCompiled::getTensorInfo(TensorPosition pos) {
: this_session.GetOutputTypeInfo(i);
tensor_info.emplace_back(info.GetTensorTypeAndShapeInfo());
char *name_p = pos == INPUT
? this_session.GetInputName(i, allocator)
: this_session.GetOutputName(i, allocator);
tensor_info.back().name = name_p;
allocator.Free(name_p);
Ort::AllocatedStringPtr name_p = pos == INPUT
? this_session.GetInputNameAllocated(i, allocator)
: this_session.GetOutputNameAllocated(i, allocator);
tensor_info.back().name = std::string(name_p.get());
}
return tensor_info;
@@ -1143,24 +1144,31 @@ namespace {
if (pp.is_generic) {
auto& info = cv::util::any_cast<cv::detail::InOutInfo>(op.params);
for (const auto& a : info.in_names)
for (const auto& layer_name : info.in_names)
{
pp.input_names.push_back(a);
}
// Adding const input is necessary because the definition of input_names
// includes const input.
for (const auto& a : pp.const_inputs)
{
pp.input_names.push_back(a.first);
pp.input_names.push_back(layer_name);
if (!pp.generic_mstd.empty()) {
const auto &ms = pp.generic_mstd.at(layer_name);
pp.mean.push_back(ms.first);
pp.stdev.push_back(ms.second);
}
if (!pp.generic_norm.empty()) {
pp.normalize.push_back(pp.generic_norm.at(layer_name));
}
}
pp.num_in = info.in_names.size();
// Incorporate extra parameters associated with input layer names
// FIXME(DM): The current form assumes ALL input layers require
// this information, this is obviously not correct
for (const auto& a : info.out_names)
{
pp.output_names.push_back(a);
}
pp.num_out = info.out_names.size();
}
} // if(is_generic) -- note, the structure is already filled at the user
// end when a non-generic Params are used
gm.metadata(nh).set(ONNXUnit{pp});
gm.metadata(nh).set(ONNXCallable{ki.run});