cleanup (4e45d987) · Commits · Elphel / tensorflow-feed-from-gpu

array.cu

+1 −1

Original line number	Original line	Diff line number	Diff line
	@@ -70,7 +70,7 @@ int myCreateCUDAArray(uint8_t *tf_ptr){

	// check if the GPU did the same as the CPU		// check if the GPU did the same as the CPU
	bool workedCorrectly = true;		bool workedCorrectly = true;
	printf("CUDA kernel incrementing test:\n");		printf("CUDA kernel simple incrementing test:\n");
	for(int i=0;i<numberOfNumbers;i++)		for(int i=0;i<numberOfNumbers;i++)
	{		{
	if (numbers1[i] != numbers2[i])		if (numbers1[i] != numbers2[i])

main.cpp

+41 −37

Original line number	Original line	Diff line number	Diff line
	@@ -32,27 +32,19 @@ using tensorflow::Status;
	using tensorflow::Tensor;		using tensorflow::Tensor;


	Status loadGraph(unique_ptr<tensorflow::Session> *session){		Status createGraphAndSession(unique_ptr<tensorflow::Session> *session){

	tensorflow::GraphDef graph_def;		tensorflow::GraphDef graph_def;

	using namespace tensorflow;		using namespace tensorflow;
	using namespace tensorflow::ops;

	auto scope = Scope::NewRootScope();		auto scope = Scope::NewRootScope();

	// TF likes power of 2		// TF likes power of 2
	tensorflow::TensorShape shape = tensorflow::TensorShape({256});		tensorflow::TensorShape shape = tensorflow::TensorShape({256});

	auto a = Placeholder(scope.WithOpName("array_tensor_in"), DT_UINT8, Placeholder::Shape(shape));		auto a = ops::Placeholder(scope.WithOpName("array_tensor_in"), DT_UINT8, ops::Placeholder::Shape(shape));
			auto b = ops::Identity(scope.WithOpName("array_tensor_out"), a);
	//auto c0 = Const(scope,{256});
	//auto c1 = Const(scope, (uint8_t)1, {});
	//auto c2 = Fill(scope, c0, c1);

	auto b = Identity(scope.WithOpName("array_tensor_out"), a);
	//auto b = Identity(scope.WithOpName("array_tensor_out"), c2);
	//auto b = Add(scope.WithOpName("array_tensor_out"),c2,a);

	TF_CHECK_OK(scope.ToGraphDef(&graph_def));		TF_CHECK_OK(scope.ToGraphDef(&graph_def));

	@@ -65,7 +57,7 @@ Status loadGraph(unique_ptr<tensorflow::Session> *session){
	session->reset(tensorflow::NewSession(session_options));		session->reset(tensorflow::NewSession(session_options));
	Status session_create_status = (*session)->Create(graph_def);		Status session_create_status = (*session)->Create(graph_def);
	if (!session_create_status.ok()){		if (!session_create_status.ok()){
	LOG(ERROR) << "loadGraph(): ERROR" << session_create_status;		LOG(ERROR) << "createGraphAndSession(): ERROR" << session_create_status;
	}		}
	return Status::OK();		return Status::OK();
	}		}
	@@ -109,7 +101,7 @@ int main(int, char**) {
	LOG(INFO) << "Tensorflow built with CUDA, keep running" << endl;		LOG(INFO) << "Tensorflow built with CUDA, keep running" << endl;
	}		}

	// check and init CUDA drivers and libs		// check and init CUDA drivers and libs?
	void *hHandleDriver = nullptr;		void *hHandleDriver = nullptr;
	CUresult cuda_res = cuInit(0, __CUDA_API_VERSION, hHandleDriver);		CUresult cuda_res = cuInit(0, __CUDA_API_VERSION, hHandleDriver);
	if (cuda_res != CUDA_SUCCESS)		if (cuda_res != CUDA_SUCCESS)
	@@ -123,14 +115,17 @@ int main(int, char**) {
	}		}
	LOG(INFO) << "\033[1;32m" << "CUDA init: ok" << "\033[0m";		LOG(INFO) << "\033[1;32m" << "CUDA init: ok" << "\033[0m";


			// construct graph and create TF session
	std::unique_ptr<tensorflow::Session> session;		std::unique_ptr<tensorflow::Session> session;
			createGraphAndSession(&session);

	loadGraph(&session);

			// do the opts and allocate tensor in GPU
			// NOTE: must match with graph names
	const string inputLayer = "array_tensor_in:0";		const string inputLayer = "array_tensor_in:0";
	const string outputLayer = "array_tensor_out:0";		const string outputLayer = "array_tensor_out:0";

	// do the opts
	CallableOptions opts;		CallableOptions opts;
	Session::CallableHandle feed_gpu_fetch_cpu;		Session::CallableHandle feed_gpu_fetch_cpu;

	@@ -150,7 +145,6 @@ int main(int, char**) {

	// TF likes power of 2 and 256s		// TF likes power of 2 and 256s
	tensorflow::TensorShape shape = tensorflow::TensorShape({256});		tensorflow::TensorShape shape = tensorflow::TensorShape({256});
	// allocate tensor on the GPU
	tensorflow::PlatformGpuId platform_gpu_id(0);		tensorflow::PlatformGpuId platform_gpu_id(0);

	tensorflow::GPUMemAllocator *sub_allocator =		tensorflow::GPUMemAllocator *sub_allocator =
	@@ -165,23 +159,32 @@ int main(int, char**) {

	LOG(INFO) << "\033[1;37m" << "Is CUDA Tensor? " << (IsCUDATensor(inputTensor)?"\033[1;32myes":"\033[1;31mno") << "\033[0m";		LOG(INFO) << "\033[1;37m" << "Is CUDA Tensor? " << (IsCUDATensor(inputTensor)?"\033[1;32myes":"\033[1;31mno") << "\033[0m";

			// pointer to tensor data
	tensorflow::uint8 *p = inputTensor.flat<tensorflow::uint8>().data();		tensorflow::uint8 *p = inputTensor.flat<tensorflow::uint8>().data();

			// run and check
			vector<Tensor> outputs;

			for(int i=0;i<2;i++){

			cout << endl;

			if (i==0){
			LOG(INFO) << "\033[1;32m" << "RunCallable()... No feeding (zeroes)" << "\033[0m";
			}
			if (i==1){
			LOG(INFO) << "\033[1;32m" << "RunCallable()... Feeding from CUDA kernel"<< "\033[0m";
	// CUDA kernel call		// CUDA kernel call
			// NOTE: do not allocate memory for p inside the kernel
	myCreateCUDAArray(p);		myCreateCUDAArray(p);
			}
	vector<Tensor> outputs;
	LOG(INFO) << "RunCallable()...";

	runStatus = session->RunCallable(feed_gpu_fetch_cpu, {inputTensor}, &outputs, nullptr);		runStatus = session->RunCallable(feed_gpu_fetch_cpu, {inputTensor}, &outputs, nullptr);
	if (!runStatus.ok())		if (!runStatus.ok()){
	{
	LOG(ERROR) << "Running model failed: " << runStatus;		LOG(ERROR) << "Running model failed: " << runStatus;
	return -1;		return -1;
	}		}

	LOG(INFO) << "RunCallable() output:";		LOG(INFO) << "RunCallable() output:";

	LOG(INFO) << outputs[0].DebugString();		LOG(INFO) << outputs[0].DebugString();
	auto tmap = outputs[0].tensor<uint8_t, 1>();		auto tmap = outputs[0].tensor<uint8_t, 1>();

	@@ -191,6 +194,7 @@ int main(int, char**) {
	if (d!=255) cout << ", ";		if (d!=255) cout << ", ";
	}		}
	cout << "\033[0m" << endl;		cout << "\033[0m" << endl;
			}

	session->ReleaseCallable(feed_gpu_fetch_cpu);		session->ReleaseCallable(feed_gpu_fetch_cpu);