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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"
#ifdef HAVE_CUDA
#include "op_cuda.hpp"
#include "cuda4dnn/init.hpp"
#include "net_impl.hpp"
namespace cv { namespace dnn {
CV__DNN_INLINE_NS_BEGIN
void Net::Impl::initCUDABackend(const std::vector<LayerPin>& blobsToKeep_)
{
CV_Assert(preferableBackend == DNN_BACKEND_CUDA);
if (!cudaInfo) /* we need to check only once */
cuda4dnn::checkVersions();
if (cuda4dnn::getDeviceCount() <= 0)
CV_Error(Error::StsError, "No CUDA capable device found.");
if (cuda4dnn::getDevice() < 0)
CV_Error(Error::StsError, "No CUDA capable device selected.");
if (!cuda4dnn::isDeviceCompatible())
CV_Error(Error::GpuNotSupported, "OpenCV was not built to work with the selected device. Please check CUDA_ARCH_PTX or CUDA_ARCH_BIN in your build configuration.");
if (preferableTarget == DNN_TARGET_CUDA_FP16 && !cuda4dnn::doesDeviceSupportFP16())
{
CV_LOG_WARNING(NULL, "The selected CUDA device does not support FP16 target; switching to FP32 target.");
preferableTarget = DNN_TARGET_CUDA;
}
if (!cudaInfo)
{
cuda4dnn::csl::CSLContext context;
context.stream = cuda4dnn::csl::Stream(true);
context.cublas_handle = cuda4dnn::csl::cublas::Handle(context.stream);
context.cudnn_handle = cuda4dnn::csl::cudnn::Handle(context.stream);
auto d2h_stream = cuda4dnn::csl::Stream(true); // stream for background D2H data transfers
cudaInfo = std::unique_ptr<CudaInfo_t>(new CudaInfo_t(std::move(context), std::move(d2h_stream)));
}
cudaInfo->workspace = cuda4dnn::csl::Workspace(); // release workspace memory if any
for (auto& layer : layers)
{
auto& ld = layer.second;
if (ld.id == 0)
{
for (auto& wrapper : ld.inputBlobsWrappers)
{
auto cudaWrapper = wrapper.dynamicCast<CUDABackendWrapper>();
cudaWrapper->setStream(cudaInfo->context.stream, cudaInfo->d2h_stream);
}
}
for (auto& wrapper : ld.outputBlobsWrappers)
{
auto cudaWrapper = wrapper.dynamicCast<CUDABackendWrapper>();
cudaWrapper->setStream(cudaInfo->context.stream, cudaInfo->d2h_stream);
}
}
for (auto& layer : layers)
{
auto& ld = layer.second;
auto& layerInstance = ld.layerInstance;
if (!layerInstance->supportBackend(DNN_BACKEND_CUDA))
{
std::ostringstream os;
os << "CUDA backend will fallback to the CPU implementation for the layer \"" << ld.name
<< "\" of type " << ld.type << '\n';
CV_LOG_INFO(NULL, os.str().c_str());
continue;
}
/* we make a copy so that `initCUDA` doesn't modify `cudaInfo->context` */
auto context = cudaInfo->context;
auto node = layerInstance->initCUDA(&context, ld.inputBlobsWrappers, ld.outputBlobsWrappers);
ld.backendNodes[DNN_BACKEND_CUDA] = node;
if(!node.empty())
{
auto cudaNode = node.dynamicCast<CUDABackendNode>();
cudaInfo->workspace.require(cudaNode->get_workspace_memory_in_bytes());
}
}
if (blobsToKeep_.size() > 1)
{
for (const auto& pin : blobsToKeep_)
{
LayerData& ld = layers[pin.lid];
ld.cudaD2HBackgroundTransfers.push_back(pin.oid);
}
}
}
CV__DNN_INLINE_NS_END
}} // namespace cv::dnn
#endif // HAVE_CUDA
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