// 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
, {}
, 1u
, {}
- , 1u} {
+ , {}} {
};
/** @overload
, {}
, 1u
, {}
- , 1u} {
+ , {}} {
};
/** @brief Specifies sequence of network input layers names for inference.
@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;
}
const std::string &device)
: desc{ model, weights, device, {}, {}, {}, 0u, 0u,
detail::ParamDesc::Kind::Load, true, {}, {}, {}, 1u,
- {}, 1u},
+ {}, {}},
m_tag(tag) {
};
const std::string &device)
: desc{ model, {}, device, {}, {}, {}, 0u, 0u,
detail::ParamDesc::Kind::Import, true, {}, {}, {}, 1u,
- {}, 1u},
+ {}, {}},
m_tag(tag) {
};
/** @see ie::Params::cfgBatchSize */
Params& cfgBatchSize(const size_t size) {
- desc.batch_size = size;
+ desc.batch_size = cv::util::make_optional(size);
return *this;
}
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
return IE::TensorDesc(toIE(mat.depth()),
IE::SizeVector{1, channels, height, width}, bdesc);
}
-
return IE::TensorDesc(toIE(mat.depth()), toIE(sz), toIELayout(sz.dims()));
}
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);
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);
}
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");
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);
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.
// 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 ?
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)),
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)) {
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)) {
#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 {
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
projects / platform / upstream / opencv.git / commitdiff