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Merge pull request #20925 from TolyaTalamanov:at/enable-2d-inputs-giebackend

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[G-API] Enable 2D inputs for GIEBackend

* Enable 2D inputs

* Fix typo
This commit is contained in:
Anatoliy Talamanov 2021-11-16 15:52:42 +03:00 committed by GitHub
parent b19697e3ac
commit c7df82460c
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 133 additions and 21 deletions
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10
modules/gapi/CMakeLists.txt
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@ -208,6 +208,10 @@ if(OPENCV_GAPI_INF_ENGINE)
ocv_target_link_libraries(${the_module} PRIVATE ${INF_ENGINE_TARGET})
endif()
if (HAVE_NGRAPH)
ocv_target_link_libraries(${the_module} PRIVATE ngraph::ngraph)
endif()
if(HAVE_TBB)
ocv_target_link_libraries(${the_module} PRIVATE tbb)
endif()
@ -223,6 +227,9 @@ set(__test_extra_deps "")
if(OPENCV_GAPI_INF_ENGINE)
list(APPEND __test_extra_deps ${INF_ENGINE_TARGET})
endif()
if(HAVE_NGRAPH)
list(APPEND __test_extra_deps ngraph::ngraph)
endif()
ocv_add_accuracy_tests(${__test_extra_deps})
# FIXME: test binary is linked with ADE directly since ADE symbols
@ -232,6 +239,9 @@ ocv_add_accuracy_tests(${__test_extra_deps})
if(TARGET opencv_test_gapi)
target_include_directories(opencv_test_gapi PRIVATE "${CMAKE_CURRENT_LIST_DIR}/src")
target_link_libraries(opencv_test_gapi PRIVATE ade)
if (HAVE_NGRAPH)
ocv_target_compile_definitions(opencv_test_gapi PRIVATE -DHAVE_NGRAPH)
endif()
endif()
if(HAVE_TBB AND TARGET opencv_test_gapi)

17
modules/gapi/include/opencv2/gapi/infer/ie.hpp
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@ -80,7 +80,10 @@ struct ParamDesc {
// NB: An optional config to setup RemoteContext for IE
cv::util::any context_config;
size_t batch_size;
// NB: batch_size can't be equal to 1 by default, because some of models
// have 2D (Layout::NC) input and if the first dimension not equal to 1
// net.setBatchSize(1) will overwrite it.
cv::optional<size_t> batch_size;
};
} // namespace detail
@ -123,7 +126,7 @@ public:
, {}
, 1u
, {}
, 1u} {
, {}} {
};
/** @overload
@ -145,7 +148,7 @@ public:
, {}
, 1u
, {}
, 1u} {
, {}} {
};
/** @brief Specifies sequence of network input layers names for inference.
@ -329,7 +332,7 @@ public:
@return reference to this parameter structure.
*/
Params<Net>& cfgBatchSize(const size_t size) {
desc.batch_size = size;
desc.batch_size = cv::util::make_optional(size);
return *this;
}
@ -367,7 +370,7 @@ public:
const std::string &device)
: desc{ model, weights, device, {}, {}, {}, 0u, 0u,
detail::ParamDesc::Kind::Load, true, {}, {}, {}, 1u,
{}, 1u},
{}, {}},
m_tag(tag) {
};
@ -385,7 +388,7 @@ public:
const std::string &device)
: desc{ model, {}, device, {}, {}, {}, 0u, 0u,
detail::ParamDesc::Kind::Import, true, {}, {}, {}, 1u,
{}, 1u},
{}, {}},
m_tag(tag) {
};
@ -454,7 +457,7 @@ public:
/** @see ie::Params::cfgBatchSize */
Params& cfgBatchSize(const size_t size) {
desc.batch_size = size;
desc.batch_size = cv::util::make_optional(size);
return *this;
}

53
modules/gapi/src/backends/ie/giebackend.cpp
View File

@ -127,7 +127,6 @@ inline int toCV(IE::Precision prec) {
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
@ -148,7 +147,6 @@ inline IE::TensorDesc toIE(const cv::Mat &mat, cv::gapi::ie::TraitAs hint) {
return IE::TensorDesc(toIE(mat.depth()),
IE::SizeVector{1, channels, height, width}, bdesc);
}
return IE::TensorDesc(toIE(mat.depth()), toIE(sz), toIELayout(sz.dims()));
}
@ -241,7 +239,10 @@ struct IEUnit {
if (params.kind == cv::gapi::ie::detail::ParamDesc::Kind::Load) {
net = cv::gimpl::ie::wrap::readNetwork(params);
net.setBatchSize(params.batch_size);
// NB: Set batch size only if user asked. (don't set by default)
if (params.batch_size.has_value()) {
net.setBatchSize(params.batch_size.value());
}
} else if (params.kind == cv::gapi::ie::detail::ParamDesc::Kind::Import) {
this_plugin = cv::gimpl::ie::wrap::getPlugin(params);
this_network = cv::gimpl::ie::wrap::importNetwork(this_plugin, params, rctx);
@ -520,7 +521,9 @@ inline IE::Blob::Ptr extractRemoteBlob(IECallContext& ctx, std::size_t i) {
blob_params->second);
}
inline IE::Blob::Ptr extractBlob(IECallContext& ctx, std::size_t i) {
inline IE::Blob::Ptr extractBlob(IECallContext& ctx,
std::size_t i,
cv::gapi::ie::TraitAs hint) {
if (ctx.uu.rctx != nullptr) {
return extractRemoteBlob(ctx, i);
}
@ -532,7 +535,7 @@ inline IE::Blob::Ptr extractBlob(IECallContext& ctx, std::size_t i) {
return wrapIE(*(ctx.views.back()), frame.desc());
}
case cv::GShape::GMAT: {
return wrapIE(ctx.inMat(i), cv::gapi::ie::TraitAs::IMAGE);
return wrapIE(ctx.inMat(i), hint);
}
default:
GAPI_Assert("Unsupported input shape for IE backend");
@ -545,6 +548,9 @@ static void setBlob(InferenceEngine::InferRequest& req,
const std::string& layer_name,
const IE::Blob::Ptr& blob,
const IECallContext& ctx) {
// 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)
using namespace cv::gapi::ie::detail;
if (ctx.uu.params.kind == ParamDesc::Kind::Load) {
req.SetBlob(layer_name, blob);
@ -950,7 +956,14 @@ struct Infer: public cv::detail::KernelTag {
uu.params.layer_names_to_reshape.end()) {
configureInputReshapeByImage(ii, mm, input_reshape_table);
}
ii->getPreProcess().setResizeAlgorithm(IE::RESIZE_BILINEAR);
// NB: Configure resize only for NCHW/NHWC layout,
// since it isn't supposed to work with others.
auto layout = ii->getTensorDesc().getLayout();
if (layout == IE::Layout::NCHW ||
layout == IE::Layout::NHWC) {
ii->getPreProcess().setResizeAlgorithm(IE::RESIZE_BILINEAR);
}
}
// FIXME: This isn't the best place to call reshape function.
@ -1000,11 +1013,16 @@ struct Infer: public cv::detail::KernelTag {
// non-generic version for now:
// - assumes all inputs/outputs are always Mats
for (auto i : ade::util::iota(ctx->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)
IE::Blob::Ptr this_blob = extractBlob(*ctx, i);
setBlob(req, ctx->uu.params.input_names[i], this_blob, *ctx);
const auto& layer_name = ctx->uu.params.input_names[i];
auto layout =
ctx->uu.this_network.GetInputsInfo().
at(layer_name)->getTensorDesc().getLayout();
auto hint =
(layout == IE::Layout::NCHW || layout == IE::Layout::NHWC)
? cv::gapi::ie::TraitAs::IMAGE : cv::gapi::ie::TraitAs::TENSOR;
IE::Blob::Ptr this_blob = extractBlob(*ctx, i, hint);
setBlob(req, layer_name, this_blob, *ctx);
}
// FIXME: Should it be done by kernel ?
// What about to do that in RequestPool ?
@ -1092,7 +1110,10 @@ struct InferROI: public cv::detail::KernelTag {
GAPI_Assert(ctx->uu.params.num_in == 1);
auto&& this_roi = ctx->inArg<cv::detail::OpaqueRef>(0).rref<cv::Rect>();
IE::Blob::Ptr this_blob = extractBlob(*ctx, 1);
// NB: This blob will be used to make roi from its, so
// it should be treated as image
IE::Blob::Ptr this_blob =
extractBlob(*ctx, 1, cv::gapi::ie::TraitAs::IMAGE);
setBlob(req,
*(ctx->uu.params.input_names.begin()),
IE::make_shared_blob(this_blob, toIE(this_roi)),
@ -1187,7 +1208,9 @@ struct InferList: public cv::detail::KernelTag {
return;
}
IE::Blob::Ptr this_blob = extractBlob(*ctx, 1);
// NB: This blob will be used to make roi from its, so
// it should be treated as image
IE::Blob::Ptr this_blob = extractBlob(*ctx, 1, cv::gapi::ie::TraitAs::IMAGE);
std::vector<std::vector<int>> cached_dims(ctx->uu.params.num_out);
for (auto i : ade::util::iota(ctx->uu.params.num_out)) {
@ -1331,7 +1354,9 @@ struct InferList2: public cv::detail::KernelTag {
cv::gimpl::ie::RequestPool &reqPool) {
GAPI_Assert(ctx->inArgs().size() > 1u
&& "This operation must have at least two arguments");
IE::Blob::Ptr blob_0 = extractBlob(*ctx, 0);
// NB: This blob will be used to make roi from its, so
// it should be treated as image
IE::Blob::Ptr blob_0 = extractBlob(*ctx, 0, cv::gapi::ie::TraitAs::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)) {

74
modules/gapi/test/infer/gapi_infer_ie_test.cpp
View File

@ -22,6 +22,20 @@
#include "backends/ie/util.hpp"
#include "backends/ie/giebackend/giewrapper.hpp"
#ifdef HAVE_NGRAPH
#if defined(__clang__) // clang or MSVC clang
#pragma clang diagnostic push
#pragma clang diagnostic ignored "-Wunused-parameter"
#elif defined(_MSC_VER)
#pragma warning(push)
#pragma warning(disable : 4100)
#elif defined(__GNUC__)
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wunused-parameter"
#endif
#include <ngraph/ngraph.hpp>
#endif
namespace opencv_test
{
namespace {
@ -2858,6 +2872,66 @@ TEST(TestAgeGender, ThrowBlobAndInputPrecisionMismatch)
cv::compile_args(cv::gapi::networks(pp))));
}
#ifdef HAVE_NGRAPH
TEST(Infer, ModelWith2DInputs)
{
const std::string model_name = "ModelWith2DInputs";
const std::string model_path = model_name + ".xml";
const std::string weights_path = model_name + ".bin";
const std::string device_id = "CPU";
const int W = 10;
const int H = 5;
// NB: Define model with 2D inputs.
auto in1 = std::make_shared<ngraph::op::Parameter>(
ngraph::element::Type_t::u8,
ngraph::Shape(std::vector<size_t>{{H, W}})
);
auto in2 = std::make_shared<ngraph::op::Parameter>(
ngraph::element::Type_t::u8,
ngraph::Shape(std::vector<size_t>{{H, W}})
);
auto result = std::make_shared<ngraph::op::v1::Add>(in1, in2);
auto func = std::make_shared<ngraph::Function>(
ngraph::OutputVector{result},
ngraph::ParameterVector{in1, in2}
);
cv::Mat in_mat1(std::vector<int>{H, W}, CV_8U),
in_mat2(std::vector<int>{H, W}, CV_8U),
gapi_mat, ref_mat;
cv::randu(in_mat1, 0, 100);
cv::randu(in_mat2, 0, 100);
cv::add(in_mat1, in_mat2, ref_mat, cv::noArray(), CV_32F);
// Compile xml file
IE::CNNNetwork(func).serialize(model_path);
// Configure & run G-API
cv::GMat g_in1, g_in2;
cv::GInferInputs inputs;
inputs[in1->get_name()] = g_in1;
inputs[in2->get_name()] = g_in2;
auto outputs = cv::gapi::infer<cv::gapi::Generic>(model_name, inputs);
auto out = outputs.at(result->get_name());
cv::GComputation comp(cv::GIn(g_in1, g_in2), cv::GOut(out));
auto pp = cv::gapi::ie::Params<cv::gapi::Generic>(model_name,
model_path,
weights_path,
device_id);
comp.apply(cv::gin(in_mat1, in_mat2), cv::gout(gapi_mat),
cv::compile_args(cv::gapi::networks(pp)));
normAssert(ref_mat, gapi_mat, "Test model output");
}
#endif // HAVE_NGRAPH
} // namespace opencv_test
#endif // HAVE_INF_ENGINE