opencv/modules/dnn/src/layer.cpp

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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.
#include "precomp.hpp"
namespace cv {
namespace dnn {
CV__DNN_INLINE_NS_BEGIN
Layer::Layer() { preferableTarget = DNN_TARGET_CPU; }
Layer::Layer(const LayerParams& params)
: blobs(params.blobs)
, name(params.name)
, type(params.type)
{
preferableTarget = DNN_TARGET_CPU;
}
void Layer::setParamsFrom(const LayerParams& params)
{
blobs = params.blobs;
name = params.name;
type = params.type;
}
int Layer::inputNameToIndex(String)
{
return -1;
}
int Layer::outputNameToIndex(const String&)
{
return 0;
}
bool Layer::supportBackend(int backendId)
{
return backendId == DNN_BACKEND_OPENCV;
}
Ptr<BackendNode> Layer::initCUDA(
void*,
const std::vector<Ptr<BackendWrapper>>&,
const std::vector<Ptr<BackendWrapper>>&)
{
CV_Error(Error::StsNotImplemented, "CUDA pipeline of " + type + " layers is not defined.");
return Ptr<BackendNode>();
}
Ptr<BackendNode> Layer::initVkCom(const std::vector<Ptr<BackendWrapper>>&)
{
CV_Error(Error::StsNotImplemented, "VkCom pipeline of " + type + " layers is not defined.");
return Ptr<BackendNode>();
}
Ptr<BackendNode> Layer::initHalide(const std::vector<Ptr<BackendWrapper>>&)
{
CV_Error(Error::StsNotImplemented, "Halide pipeline of " + type + " layers is not defined.");
return Ptr<BackendNode>();
}
Ptr<BackendNode> Layer::initNgraph(const std::vector<Ptr<BackendWrapper>>& inputs, const std::vector<Ptr<BackendNode>>& nodes)
{
CV_Error(Error::StsNotImplemented, "Inference Engine pipeline of " + type + " layers is not defined.");
return Ptr<BackendNode>();
}
Ptr<BackendNode> Layer::initWebnn(const std::vector<Ptr<BackendWrapper>>& inputs, const std::vector<Ptr<BackendNode>>& nodes)
{
CV_Error(Error::StsNotImplemented, "WebNN pipeline of " + type + " layers is not defined.");
return Ptr<BackendNode>();
}
Ptr<BackendNode> Layer::initTimVX(void* timVxInfo,
const std::vector<Ptr<BackendWrapper> > & inputsWrapper,
const std::vector<Ptr<BackendWrapper> > & outputsWrapper,
bool isLast)
{
CV_Error(Error::StsNotImplemented, "TimVX pipeline of " + type +
" layers is not defined.");
return Ptr<BackendNode>();
}
Ptr<BackendNode> Layer::tryAttach(const Ptr<BackendNode>& node)
{
return Ptr<BackendNode>();
}
bool Layer::setActivation(const Ptr<ActivationLayer>&) { return false; }
bool Layer::tryFuse(Ptr<Layer>&) { return false; }
void Layer::getScaleShift(Mat& scale, Mat& shift) const
{
scale = Mat();
shift = Mat();
}
void Layer::getScaleZeropoint(float& scale, int& zeropoint) const
{
scale = 1.f;
zeropoint = 0;
}
void Layer::unsetAttached()
{
setActivation(Ptr<ActivationLayer>());
}
template <typename T>
static void vecToPVec(const std::vector<T>& v, std::vector<T*>& pv)
{
pv.resize(v.size());
for (size_t i = 0; i < v.size(); i++)
pv[i] = const_cast<T*>(&v[i]);
}
void Layer::finalize(const std::vector<Mat>& inputs, std::vector<Mat>& outputs)
{
CV_TRACE_FUNCTION();
this->finalize((InputArrayOfArrays)inputs, (OutputArrayOfArrays)outputs);
}
void Layer::finalize(const std::vector<Mat*>& input, std::vector<Mat>& output)
{
CV_UNUSED(input);
CV_UNUSED(output);
}
void Layer::finalize(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr)
{
CV_TRACE_FUNCTION();
std::vector<Mat> inputs, outputs;
inputs_arr.getMatVector(inputs);
outputs_arr.getMatVector(outputs);
std::vector<Mat*> inputsp;
vecToPVec(inputs, inputsp);
this->finalize(inputsp, outputs);
}
std::vector<Mat> Layer::finalize(const std::vector<Mat>& inputs)
{
CV_TRACE_FUNCTION();
std::vector<Mat> outputs;
this->finalize(inputs, outputs);
return outputs;
}
void Layer::forward(std::vector<Mat*>& input, std::vector<Mat>& output, std::vector<Mat>& internals)
{
// We kept this method for compatibility. DNN calls it now only to support users' implementations.
}
void Layer::forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr);
}
void Layer::forward_fallback(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
if (preferableTarget == DNN_TARGET_OPENCL_FP16 && inputs_arr.depth() == CV_16S)
{
std::vector<UMat> inputs;
std::vector<UMat> outputs;
std::vector<UMat> internals;
std::vector<UMat> orig_inputs;
std::vector<UMat> orig_outputs;
std::vector<UMat> orig_internals;
inputs_arr.getUMatVector(orig_inputs);
outputs_arr.getUMatVector(orig_outputs);
internals_arr.getUMatVector(orig_internals);
inputs.resize(orig_inputs.size());
for (size_t i = 0; i < orig_inputs.size(); i++)
convertFp16(orig_inputs[i], inputs[i]);
outputs.resize(orig_outputs.size());
for (size_t i = 0; i < orig_outputs.size(); i++)
outputs[i].create(shape(orig_outputs[i]), CV_32F);
internals.resize(orig_internals.size());
for (size_t i = 0; i < orig_internals.size(); i++)
internals[i].create(shape(orig_internals[i]), CV_32F);
forward(inputs, outputs, internals);
for (size_t i = 0; i < outputs.size(); i++)
convertFp16(outputs[i], orig_outputs[i]);
// sync results back
outputs_arr.assign(orig_outputs);
internals_arr.assign(orig_internals);
return;
}
std::vector<Mat> inpvec;
std::vector<Mat> outputs;
std::vector<Mat> internals;
inputs_arr.getMatVector(inpvec);
outputs_arr.getMatVector(outputs);
internals_arr.getMatVector(internals);
std::vector<Mat*> inputs(inpvec.size());
for (int i = 0; i < inpvec.size(); i++)
inputs[i] = &inpvec[i];
this->forward(inputs, outputs, internals);
// sync results back
outputs_arr.assign(outputs);
internals_arr.assign(internals);
}
void Layer::run(const std::vector<Mat>& inputs, std::vector<Mat>& outputs, std::vector<Mat>& internals)
{
CV_TRACE_FUNCTION();
this->finalize(inputs, outputs);
this->forward(inputs, outputs, internals);
}
bool Layer::tryQuantize(const std::vector<std::vector<float>>& scales,
const std::vector<std::vector<int>>& zeropoints, LayerParams& params)
{
return false;
}
Layer::~Layer() {}
bool Layer::getMemoryShapes(const std::vector<MatShape>& inputs,
const int requiredOutputs,
std::vector<MatShape>& outputs,
std::vector<MatShape>& internals) const
{
CV_Assert(inputs.size());
outputs.assign(std::max(requiredOutputs, (int)inputs.size()), inputs[0]);
return false;
}
bool Layer::updateMemoryShapes(const std::vector<MatShape>& inputs)
{
return true;
}
CV__DNN_INLINE_NS_END
}} // namespace cv::dnn