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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
// Copyright (C) 2018, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
#include "../precomp.hpp"
#include "../op_inf_engine.hpp"
#include "../op_cuda.hpp"
#include "layers_common.hpp"
#include "../ie_ngraph.hpp"
#include "../op_webnn.hpp"
#include "../op_cann.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#ifdef HAVE_OPENCL
#include "opencl_kernels_dnn.hpp"
#endif
#ifdef HAVE_CUDA
#include "../cuda4dnn/primitives/const.hpp"
using namespace cv::dnn::cuda4dnn;
#endif
namespace cv { namespace dnn {
class ConstLayerImpl CV_FINAL : public ConstLayer
{
public:
ConstLayerImpl(const LayerParams& params)
{
setParamsFrom(params);
CV_Assert(blobs.size() == 1);
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
return true;
#endif
return backendId == DNN_BACKEND_OPENCV ||
backendId == DNN_BACKEND_WEBNN ||
backendId == DNN_BACKEND_CUDA ||
backendId == DNN_BACKEND_CANN;
}
virtual bool getMemoryShapes(const std::vector<MatShape> &inputs,
const int requiredOutputs,
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const CV_OVERRIDE
{
CV_Assert(inputs.empty());
outputs.assign(1, shape(blobs[0]));
return false;
}
#ifdef HAVE_OPENCL
bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
{
std::vector<UMat> outputs;
outs.getUMatVector(outputs);
if (outs.depth() == CV_16F) {
auto blob = blobs[0];
if (blob.type() != CV_32F) {
blob.convertTo(blob, CV_32F);
}
blob.convertTo(outputs[0], CV_16F);
}
else
blobs[0].convertTo(outputs[0], outputs[0].type());
return true;
}
#endif
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
std::vector<Mat> outputs;
outputs_arr.getMatVector(outputs);
blobs[0].convertTo(outputs[0], outputs[0].type());
}
#ifdef HAVE_CANN
virtual Ptr<BackendNode> initCann(const std::vector<Ptr<BackendWrapper> > &inputs,
const std::vector<Ptr<BackendWrapper> > &outputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
auto mat_shape = shape(blobs[0]);
std::vector<int64_t> mat_shape_{mat_shape.begin(), mat_shape.end()};
auto ge_shape = ge::Shape(mat_shape_);
auto ge_dtype = ge::DT_FLOAT;
switch (blobs[0].type())
{
case CV_32F: break;
case CV_32S: ge_dtype = ge::DT_INT32; break;
default: CV_Error(Error::StsNotImplemented, "Unsuppported data type");
}
auto size_of_type = sizeof(float);
switch (blobs[0].type())
{
case CV_32F: break;
case CV_32S: size_of_type = sizeof(int); break;
default: CV_Error(Error::StsNotImplemented, "Unsuppported data type");
}
auto desc = std::make_shared<ge::TensorDesc>(ge_shape, ge::FORMAT_NCHW, ge_dtype);
auto ge_tensor = std::make_shared<ge::Tensor>();
ge_tensor->SetTensorDesc(*desc);
ge_tensor->SetData(blobs[0].data, ge_shape.GetShapeSize() * size_of_type);
auto op = std::make_shared<ge::op::Const>(name);
op->set_attr_value(*ge_tensor);
return Ptr<BackendNode>(new CannBackendNode(op));
}
#endif // HAVE_CANN
#ifdef HAVE_DNN_NGRAPH
virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
ov::element::Type dType;
if (blobs[0].depth() == CV_32F) {
dType = ov::element::f32;
} else if (blobs[0].depth() == CV_32S) {
dType = ov::element::i32;
} else if (blobs[0].depth() == CV_8S) {
dType = ov::element::i8;
} else {
CV_Error(Error::StsNotImplemented, format("Unexpected Const data depth: %d", blobs[0].depth()));
}
std::shared_ptr<ov::Node> node =
std::make_shared<ov::op::v0::Constant>(dType,
getShape<size_t>(blobs[0]),
blobs[0].data);
if (node->get_element_type() != ov::element::f32) {
node = std::make_shared<ov::op::v0::Convert>(node, ov::element::f32);
}
return Ptr<BackendNode>(new InfEngineNgraphNode(node));
}
#endif // HAVE_DNN_NGRAPH
#ifdef HAVE_WEBNN
virtual Ptr<BackendNode> initWebnn(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
{
ml::Operand operand = nullptr;
Ptr<WebnnBackendNode> node = nodes[0].dynamicCast<WebnnBackendNode>();
auto& webnnGraphBuilder = node->net->builder;
operand = webnn::BuildConstant(webnnGraphBuilder, webnn::getShape(blobs[0]), blobs[0].data, blobs[0].total()*blobs[0].elemSize(), ml::OperandType::Float32);
return Ptr<BackendNode>(new WebnnBackendNode(operand));
}
#endif
#ifdef HAVE_CUDA
Ptr<BackendNode> initCUDA(
void *context_,
const std::vector<Ptr<BackendWrapper>>& inputs,
const std::vector<Ptr<BackendWrapper>>& outputs
) override
{
auto context = reinterpret_cast<csl::CSLContext*>(context_);
CV_Assert(blobs.size() == 1);
Mat blob = blobs[0];
if (blob.type() != CV_32F) {
blob.convertTo(blob, CV_32F);
}
return make_cuda_node<cuda4dnn::ConstOp>(preferableTarget, std::move(context->stream), blob);
}
#endif
virtual bool tryQuantize(const std::vector<std::vector<float> > &scales,
const std::vector<std::vector<int> > &zeropoints, LayerParams& params) CV_OVERRIDE
{
Mat quantizedBlob;
blobs[0].convertTo(quantizedBlob, CV_8S, 1.f/scales[1][0], zeropoints[1][0]);
params.blobs.clear();
params.blobs.push_back(quantizedBlob);
return true;
}
};
Ptr<Layer> ConstLayer::create(const LayerParams& params)
{
return Ptr<Layer>(new ConstLayerImpl(params));
}
}} // namespace cv::dnn
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