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Merge pull request #19222 from YashasSamaga:cuda4dnn-fix-build-diagnostics
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commit
fbb38cc245
@ -17,28 +17,18 @@ namespace cv { namespace dnn { namespace cuda4dnn {
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void checkVersions()
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{
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int cudart_version = 0;
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CUDA4DNN_CHECK_CUDA(cudaRuntimeGetVersion(&cudart_version));
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if (cudart_version != CUDART_VERSION)
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// https://docs.nvidia.com/deeplearning/cudnn/developer-guide/index.html#programming-model
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// cuDNN API Compatibility
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// Beginning in cuDNN 7, the binary compatibility of a patch and minor releases is maintained as follows:
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// Any patch release x.y.z is forward or backward-compatible with applications built against another cuDNN patch release x.y.w (meaning, of the same major and minor version number, but having w!=z).
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// cuDNN minor releases beginning with cuDNN 7 are binary backward-compatible with applications built against the same or earlier patch release (meaning, an application built against cuDNN 7.x is binary compatible with cuDNN library 7.y, where y>=x).
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// Applications compiled with a cuDNN version 7.y are not guaranteed to work with 7.x release when y > x.
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auto cudnn_bversion = cudnnGetVersion();
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auto cudnn_major_bversion = cudnn_bversion / 1000, cudnn_minor_bversion = cudnn_bversion % 1000 / 100;
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if (cudnn_major_bversion != CUDNN_MAJOR || cudnn_minor_bversion < CUDNN_MINOR)
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{
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std::ostringstream oss;
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oss << "CUDART reports version " << cudart_version << " which does not match with the version " << CUDART_VERSION << " with which OpenCV was built";
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CV_LOG_WARNING(NULL, oss.str().c_str());
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}
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auto cudnn_version = cudnnGetVersion();
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if (cudnn_version != CUDNN_VERSION)
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{
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std::ostringstream oss;
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oss << "cuDNN reports version " << cudnn_version << " which does not match with the version " << CUDNN_VERSION << " with which OpenCV was built";
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CV_LOG_WARNING(NULL, oss.str().c_str());
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}
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auto cudnn_cudart_version = cudnnGetCudartVersion();
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if (cudart_version != cudnn_cudart_version)
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{
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std::ostringstream oss;
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oss << "CUDART version " << cudnn_cudart_version << " reported by cuDNN " << cudnn_version << " does not match with the version reported by CUDART " << cudart_version;
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oss << "cuDNN reports version " << cudnn_major_bversion << "." << cudnn_minor_bversion << " which is not compatible with the version " << CUDNN_MAJOR << "." << CUDNN_MINOR << " with which OpenCV was built";
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CV_LOG_WARNING(NULL, oss.str().c_str());
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}
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}
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@ -57,9 +47,6 @@ namespace cv { namespace dnn { namespace cuda4dnn {
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bool isDeviceCompatible()
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{
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if (getDeviceCount() <= 0)
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return false;
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int device_id = getDevice();
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if (device_id < 0)
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return false;
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@ -80,9 +67,6 @@ namespace cv { namespace dnn { namespace cuda4dnn {
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bool doesDeviceSupportFP16()
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{
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if (getDeviceCount() <= 0)
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return false;
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int device_id = getDevice();
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if (device_id < 0)
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return false;
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@ -239,11 +239,10 @@ private:
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#endif
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#ifdef HAVE_CUDA
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if (haveCUDA() && cuda4dnn::isDeviceCompatible())
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if (haveCUDA())
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{
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backends.push_back(std::make_pair(DNN_BACKEND_CUDA, DNN_TARGET_CUDA));
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if (cuda4dnn::doesDeviceSupportFP16())
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backends.push_back(std::make_pair(DNN_BACKEND_CUDA, DNN_TARGET_CUDA_FP16));
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backends.push_back(std::make_pair(DNN_BACKEND_CUDA, DNN_TARGET_CUDA_FP16));
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}
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#endif
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}
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@ -2363,6 +2362,9 @@ struct Net::Impl : public detail::NetImplBase
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CV_Assert(preferableBackend == DNN_BACKEND_CUDA);
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#ifdef HAVE_CUDA
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if (!cudaInfo) /* we need to check only once */
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cuda4dnn::checkVersions();
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if (cuda4dnn::getDeviceCount() <= 0)
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CV_Error(Error::StsError, "No CUDA capable device found.");
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@ -2373,7 +2375,10 @@ struct Net::Impl : public detail::NetImplBase
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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.");
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if (preferableTarget == DNN_TARGET_CUDA_FP16 && !cuda4dnn::doesDeviceSupportFP16())
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CV_Error(Error::StsError, "The selected CUDA device does not support FP16 operations.");
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{
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CV_LOG_WARNING(NULL, "The selected CUDA device does not support FP16 target; switching to FP32 target.");
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preferableTarget = DNN_TARGET_CUDA;
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}
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if (!cudaInfo)
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{
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@ -2384,7 +2389,6 @@ struct Net::Impl : public detail::NetImplBase
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auto d2h_stream = cuda4dnn::csl::Stream(true); // stream for background D2H data transfers
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cudaInfo = std::unique_ptr<CudaInfo_t>(new CudaInfo_t(std::move(context), std::move(d2h_stream)));
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cuda4dnn::checkVersions();
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}
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cudaInfo->workspace = cuda4dnn::csl::Workspace(); // release workspace memory if any
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