Very close on this. I believe the majority of this code is setup correctly. cudaErrorInvalidValue:invalid argument #23149
Description
Describe the issue
This is the last few steps in a journey to release something I think is going to be pretty amazing. Code is wip dont judge. Goal is global and NOT dry though. This is going to be a learning example too. I get frustrated trying to pull apart hyper OO code while trying to grasp the basic concepts. In case "write better code" is a comment :P.
TLDR: We have a 2dtexture which is converted to a RGB float* cuda array. I can provide that too, but I use similar code in pure tensorrt implementations, and I know it to work. I believe my cuda array and sizing is correct. But if at a glance my init and detect look good I can try to trim stuff up and provide more info.
Run failed: Non-zero status code returned while running Conv node. Name:'p2o.Conv.0' Status Message: CUDA error cudaErrorInvalidValue:invalid argument
#pragma once
#include "globals.h"
#include "cudaFunctions.cuh"
bool bound = false;
const OrtApi* api;
OrtSession* session = nullptr;
OrtEnv* env = nullptr;
OrtMemoryInfo* info_cuda = nullptr;
OrtStatus* status;
OrtValue* scoresValue = nullptr;
OrtValue* boxesValue = nullptr;
OrtValue* cudaTextureOrt = nullptr;
OrtAllocator* allocator;
OrtIoBinding* io_binding;
float* d_output0 = nullptr;
float* d_output1 = nullptr;
std::vector<std::string> readLabels(const std::string& labelPath) {
std::vector<std::string> labels;
std::ifstream infile(labelPath);
std::string line;
while (std::getline(infile, line)) {
labels.push_back(line);
}
infile.close();
return labels;
}
wchar_t* stringToWcharT(const std::string& str) {
std::wstring_convert<std::codecvt_utf8_utf16<wchar_t>> converter;
std::wstring wideStr = converter.from_bytes(str);
wchar_t* wideCStr = new wchar_t[wideStr.size() + 1];
std::copy(wideStr.begin(), wideStr.end(), wideCStr);
wideCStr[wideStr.size()] = L'\0';
return wideCStr;
}
float* TextureToCuda(ID3D11Texture2D* texture, unsigned int width, unsigned int height) {
cudaArray* mappedArray;
cudaGraphicsMapResources(1, &cudaResource);
cudaGraphicsSubResourceGetMappedArray(&mappedArray, cudaResource, 0, 0);
auto rgbCudaArray = KCuda::RemoveAlphaAndFlip(mappedArray, width, height);
cudaGraphicsUnmapResources(1, &cudaResource);
cudaFree(mappedArray);
return rgbCudaArray;
}
void CreateOnnxValueFromTexture(ID3D11Texture2D* texture) {
D3D11_TEXTURE2D_DESC d;
texture->GetDesc(&d);
unsigned int width = d.Width;
unsigned int height = d.Height;
rgbCudaArray = TextureToCuda(texture, width, height);
int64_t shape[] = { 1, 3, height, width };
size_t shape_len = sizeof(shape) / sizeof(shape[0]);
size_t p_data_len = sizeof(float) * 1 * 3 * height * width;
status = api->CreateTensorWithDataAsOrtValue(info_cuda,
rgbCudaArray,
p_data_len,
shape,
shape_len,
ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT,
&cudaTextureOrt
);
if (status != nullptr) {
std::cerr << "Error creating tensor: " << api->GetErrorMessage(status) << std::endl;
api->ReleaseStatus(status);
exit(1);
}
}
void UpdateOnnxValueFromTexture(ID3D11Texture2D* texture) {
D3D11_TEXTURE2D_DESC d;
texture->GetDesc(&d);
unsigned int width = d.Width;
unsigned int height = d.Height;
cudaArray* mappedArray;
cudaGraphicsMapResources(1, &cudaResource);
cudaGraphicsSubResourceGetMappedArray(&mappedArray, cudaResource, 0, 0);
KCuda::UpdateArray(mappedArray, rgbCudaArray, width, height);
cudaGraphicsUnmapResources(1, &cudaResource);
cudaFree(mappedArray);
}
void Detect() {
if (!bound) {
int64_t shape0[] = { 1, 300, 80 };
int64_t shape1[] = { 1, 300, 4 };
int output0_dims[] = { 1, 300, 80 };
int output1_dims[] = { 1, 300, 4 };
size_t output0_size = 1 * 300 * 80 * sizeof(float);
size_t output1_size = 1 * 300 * 4 * sizeof(float);
cudaMalloc(&d_output0, output0_size);
cudaMalloc(&d_output1, output1_size);
status = api->CreateTensorWithDataAsOrtValue(info_cuda, d_output0, output0_size,
shape0, 3, ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT, &scoresValue);
if (status != nullptr) {
std::cerr << "failed scores tensor" << api->GetErrorMessage(status) << std::endl;
api->ReleaseStatus(status);
cleanup();
exit(1);
}
status = api->CreateTensorWithDataAsOrtValue(info_cuda, d_output1, output1_size,
shape1, 3, ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT, &boxesValue);
if (status != nullptr) {
std::cerr << "failed boxes tensor" << api->GetErrorMessage(status) << std::endl;
api->ReleaseStatus(status);
cleanup();
exit(1);
}
status = api->CreateIoBinding(session, &io_binding);
if (status != nullptr) {
std::cerr << "bad input binding" << api->GetErrorMessage(status) << std::endl;
api->ReleaseStatus(status);
cleanup();
exit(1);
}
status = api->BindInput(io_binding, "image", cudaTextureOrt);
if (status != nullptr) {
std::cerr << "bad binding input" << api->GetErrorMessage(status) << std::endl;
api->ReleaseStatus(status);
cleanup();
exit(1);
}
status = api->BindOutput(io_binding, "boxes", boxesValue);
if (status != nullptr) {
std::cerr << "bad binding output boxes" << api->GetErrorMessage(status) << std::endl;
api->ReleaseStatus(status);
cleanup();
exit(1);
}
status = api->BindOutput(io_binding, "scores", scoresValue);
if (status != nullptr) {
std::cerr << "bad output boxes" << api->GetErrorMessage(status) << std::endl;
api->ReleaseStatus(status);
cleanup();
exit(1);
}
bound = true;
}
std::vector<const char*> outputNames = {"scores", "boxes"};
std::vector<const char*> inputNames = { "image" };
std::vector<OrtValue*> inputTensors = { cudaTextureOrt };
std::vector<OrtValue*> outputTensors = { scoresValue , boxesValue };
status = api->RunWithBinding(session, Ort::RunOptions(), io_binding);
if (status != nullptr) {
std::cerr << "Run failed: " << api->GetErrorMessage(status) << std::endl;
api->ReleaseStatus(status);
cleanup();
exit(1);
}
else {
std::cout << "doot";
}
}
void InitializeOnnx() {
std::cout << "[INFO] ONNXRuntime version: " << OrtGetApiBase()->GetVersionString() << std::endl;
std::string labelPath = exeDir + "/labels.txt";
std::string modelPath = exeDir + "/rtdetr_r18vd_6x_coco-modify.onnx";
std::string instanceName = "rtdetr-r18";
size_t deviceId = 0;
size_t batchSize = 1;
float confThreshold = 0.3;
std::vector<std::string> labels = readLabels(labelPath);
api = OrtGetApiBase()->GetApi(ORT_API_VERSION);
status = api->CreateEnv(ORT_LOGGING_LEVEL_WARNING, instanceName.c_str(), &env);
if (status != nullptr) {
std::cerr << "Error creating environment: " << api->GetErrorMessage(status) << std::endl;
api->ReleaseStatus(status);
exit(1);
}
OrtSessionOptions* session_options = nullptr;
status = api->CreateSessionOptions(&session_options);
if (status != nullptr) {
std::cerr << "Error creating session options: " << api->GetErrorMessage(status) << std::endl;
api->ReleaseStatus(status);
api->ReleaseEnv(env);
exit(1);
}
OrtCUDAProviderOptions cuda_options;
cuda_options.device_id = deviceId;
status = api->SessionOptionsAppendExecutionProvider_CUDA(session_options, &cuda_options);
if (status != nullptr) {
std::cerr << "Error appending provider: " << api->GetErrorMessage(status) << std::endl;
api->ReleaseStatus(status);
api->ReleaseEnv(env);
exit(1);
}
api->SetSessionGraphOptimizationLevel(session_options, ORT_ENABLE_EXTENDED);
api->SetIntraOpNumThreads(session_options, 1);
status = api->CreateSession(env, stringToWcharT(modelPath), session_options, &session);
if (status != nullptr) {
std::cerr << "Error creating session: " << api->GetErrorMessage(status) << std::endl;
api->ReleaseStatus(status);
api->ReleaseSessionOptions(session_options);
api->ReleaseEnv(env);
exit(1);
}
const char* allocator_name = "Cuda";
OrtAllocatorType allocator_type = OrtArenaAllocator;
int device_id = 0;
OrtMemType mem_type = OrtMemTypeDefault;
status = api->CreateMemoryInfo(allocator_name, allocator_type, device_id, mem_type, &info_cuda);
if (status != nullptr) {
std::cerr << "Error creating memory info: " << api->GetErrorMessage(status) << std::endl;
api->ReleaseStatus(status);
exit(1);
}
status = api->CreateAllocator(session, info_cuda, &allocator);
if (status != nullptr) {
std::cerr << "failed to get allocator " << api->GetErrorMessage(status) << std::endl;
api->ReleaseStatus(status);
cleanup();
exit(1);
}
std::cout << "[INFO] Model was initialized." << std::endl;
}
This is a modified rt-detr paddle paddle model. I can share the whole thing, but I think I modified this correctly.
To reproduce
I can minimize this too. Some people are human compilers, and someone might just spot it. If you don't see it at a glance, I'll try to do something. I'm not ready for the code to get out yet. I can provide the whole project I suppose. It's fairly nice.
Urgency
LOL this is an interesting one. I'm uh running out of time and money trying to start a company I guess. I have a fairly amazing demo that does some amazing things with screen cap, the windows display manager, and all sorts of stuff. A fairly profound architectural microcosm if you will. Then I also have a idea for a multi object tracking algo that might compete with SOTA so I just gotta get to that and then go get a job I guess. So uh, ya know, idk. It's just my life.
Platform
Windows
OS Version
Win 11 H2
ONNX Runtime Installation
Released Package
ONNX Runtime Version or Commit ID
ONNXRuntime version: 1.20.1
ONNX Runtime API
C
Architecture
X64
Execution Provider
CUDA
Execution Provider Library Version
CUDA 12.3