718c41634f
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
132 lines
4.2 KiB
C++
132 lines
4.2 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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// Copyright (C) 2018, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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#include "../precomp.hpp"
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#include "../op_inf_engine.hpp"
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#include "../op_cuda.hpp"
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#include "layers_common.hpp"
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#include "../ie_ngraph.hpp"
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#ifdef HAVE_OPENCL
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#include "opencl_kernels_dnn.hpp"
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#endif
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#ifdef HAVE_CUDA
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#include "../cuda4dnn/primitives/const.hpp"
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using namespace cv::dnn::cuda4dnn;
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#endif
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namespace cv { namespace dnn {
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class ConstLayerImpl CV_FINAL : public ConstLayer
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{
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public:
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ConstLayerImpl(const LayerParams& params)
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{
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setParamsFrom(params);
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CV_Assert(blobs.size() == 1);
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}
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virtual bool supportBackend(int backendId) CV_OVERRIDE
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{
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return backendId == DNN_BACKEND_OPENCV ||
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backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
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backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ||
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backendId == DNN_BACKEND_CUDA;
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}
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virtual bool getMemoryShapes(const std::vector<MatShape> &inputs,
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const int requiredOutputs,
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std::vector<MatShape> &outputs,
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std::vector<MatShape> &internals) const CV_OVERRIDE
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{
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CV_Assert(inputs.empty());
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outputs.assign(1, shape(blobs[0]));
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return false;
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}
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#ifdef HAVE_OPENCL
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bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
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{
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std::vector<UMat> outputs;
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outs.getUMatVector(outputs);
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if (outs.depth() == CV_16S)
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convertFp16(blobs[0], outputs[0]);
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else
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blobs[0].copyTo(outputs[0]);
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return true;
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}
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#endif
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void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
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{
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CV_TRACE_FUNCTION();
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CV_TRACE_ARG_VALUE(name, "name", name.c_str());
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CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget),
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forward_ocl(inputs_arr, outputs_arr, internals_arr))
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std::vector<Mat> outputs;
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outputs_arr.getMatVector(outputs);
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blobs[0].copyTo(outputs[0]);
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}
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#ifdef HAVE_DNN_IE_NN_BUILDER_2019
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virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
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{
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InferenceEngine::Builder::ConstLayer ieLayer(name);
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ieLayer.setData(wrapToInfEngineBlob(blobs[0]));
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return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
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}
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#endif // HAVE_DNN_IE_NN_BUILDER_2019
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#ifdef HAVE_DNN_NGRAPH
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virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
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const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
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{
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auto node = std::make_shared<ngraph::op::Constant>(ngraph::element::f32,
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getShape<size_t>(blobs[0]),
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blobs[0].data);
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return Ptr<BackendNode>(new InfEngineNgraphNode(node));
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}
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#endif // HAVE_DNN_NGRAPH
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#ifdef HAVE_CUDA
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Ptr<BackendNode> initCUDA(
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void *context_,
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const std::vector<Ptr<BackendWrapper>>& inputs,
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const std::vector<Ptr<BackendWrapper>>& outputs
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) override
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{
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auto context = reinterpret_cast<csl::CSLContext*>(context_);
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CV_Assert(blobs.size() == 1);
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return make_cuda_node<cuda4dnn::ConstOp>(preferableTarget, std::move(context->stream), blobs[0]);
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}
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#endif
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virtual bool tryQuantize(const std::vector<std::vector<float> > &scales,
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const std::vector<std::vector<int> > &zeropoints, LayerParams& params) CV_OVERRIDE
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{
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Mat quantizedBlob;
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blobs[0].convertTo(quantizedBlob, CV_8S, 1.f/scales[1][0], zeropoints[1][0]);
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params.blobs.clear();
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params.blobs.push_back(quantizedBlob);
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return true;
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}
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};
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Ptr<Layer> ConstLayer::create(const LayerParams& params)
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{
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return Ptr<Layer>(new ConstLayerImpl(params));
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}
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}} // namespace cv::dnn
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