Please provide complete information as applicable to your setup.
• Hardware Platform (Jetson / GPU) -> aws dGPU T4
• DeepStream Version -> SDK 5.0
• TensorRT Version -> 7
• NVIDIA GPU Driver Version (valid for GPU only) -> 440.82
Created custom layers which are written into the library libdecodeplugin.so. The code for custom layers which use IPluginCreator to create plugins is below:
#include "decode.h"
#include "stdio.h"
namespace nvinfer1
{
DecodePlugin::DecodePlugin()
{
}
DecodePlugin::~DecodePlugin()
{
}
// create the plugin at runtime from a byte stream
DecodePlugin::DecodePlugin(const void* data, size_t length)
{
}
void DecodePlugin::serialize(void* buffer) const
{
}
size_t DecodePlugin::getSerializationSize() const
{
return 0;
}
int DecodePlugin::initialize()
{
return 0;
}
Dims DecodePlugin::getOutputDimensions(int index, const Dims* inputs, int nbInputDims)
{
//output the result to channel
int totalCount = 0;
totalCount += decodeplugin::INPUT_H / 8 * decodeplugin::INPUT_W / 8 * 2 * sizeof(decodeplugin::Detection) / sizeof(float);
totalCount += decodeplugin::INPUT_H / 16 * decodeplugin::INPUT_W / 16 * 2 * sizeof(decodeplugin::Detection) / sizeof(float);
totalCount += decodeplugin::INPUT_H / 32 * decodeplugin::INPUT_W / 32 * 2 * sizeof(decodeplugin::Detection) / sizeof(float);
return Dims3(totalCount + 1, 1, 1);
}
// Set plugin namespace
void DecodePlugin::setPluginNamespace(const char* pluginNamespace)
{
mPluginNamespace = pluginNamespace;
}
const char* DecodePlugin::getPluginNamespace() const
{
return mPluginNamespace;
}
// Return the DataType of the plugin output at the requested index
DataType DecodePlugin::getOutputDataType(int index, const nvinfer1::DataType* inputTypes, int nbInputs) const
{
return DataType::kFLOAT;
}
// Return true if output tensor is broadcast across a batch.
bool DecodePlugin::isOutputBroadcastAcrossBatch(int outputIndex, const bool* inputIsBroadcasted, int nbInputs) const
{
return false;
}
// Return true if plugin can use input that is broadcast across batch without replication.
bool DecodePlugin::canBroadcastInputAcrossBatch(int inputIndex) const
{
return false;
}
void DecodePlugin::configurePlugin(const PluginTensorDesc* in, int nbInput, const PluginTensorDesc* out, int nbOutput)
{
}
// Attach the plugin object to an execution context and grant the plugin the access to some context resource.
void DecodePlugin::attachToContext(cudnnContext* cudnnContext, cublasContext* cublasContext, IGpuAllocator* gpuAllocator)
{
}
// Detach the plugin object from its execution context.
void DecodePlugin::detachFromContext() {}
const char* DecodePlugin::getPluginType() const
{
return "Decode_TRT";
}
const char* DecodePlugin::getPluginVersion() const
{
return "1";
}
void DecodePlugin::destroy()
{
delete this;
}
// Clone the plugin
IPluginV2IOExt* DecodePlugin::clone() const
{
DecodePlugin *p = new DecodePlugin();
p->setPluginNamespace(mPluginNamespace);
return p;
}
__device__ float Logist(float data){ return 1./(1. + expf(-data)); };
__global__ void CalDetection(const float *input, float *output, int num_elem, int step, int anchor) {
int idx = threadIdx.x + blockDim.x * blockIdx.x;
if (idx >= num_elem) return;
int h = decodeplugin::INPUT_H / step;
int w = decodeplugin::INPUT_W / step;
int y = idx / w;
int x = idx % w;
const float *bbox_reg = &input[0];
const float *cls_reg = &input[2 * 4 * num_elem];
const float *lmk_reg = &input[2 * 4 * num_elem + 2 * 2 * num_elem];
for (int k = 0; k < 2; ++k) {
float conf1 = cls_reg[idx + k * num_elem * 2];
float conf2 = cls_reg[idx + k * num_elem * 2 + num_elem];
conf2 = expf(conf2) / (expf(conf1) + expf(conf2));
if (conf2 <= 0.02) continue;
float *res_count = output;
int count = (int)atomicAdd(res_count, 1);
char* data = (char *)res_count + sizeof(float) + count * sizeof(decodeplugin::Detection);
decodeplugin::Detection* det = (decodeplugin::Detection*)(data);
float prior[4];
prior[0] = ((float)x + 0.5) / w;
prior[1] = ((float)y + 0.5) / h;
prior[2] = (float)anchor * (k + 1) / decodeplugin::INPUT_W;
prior[3] = (float)anchor * (k + 1) / decodeplugin::INPUT_H;
//Location
det->bbox[0] = prior[0] + bbox_reg[idx + k * num_elem * 4] * 0.1 * prior[2];
det->bbox[1] = prior[1] + bbox_reg[idx + k * num_elem * 4 + num_elem] * 0.1 * prior[3];
det->bbox[2] = prior[2] * expf(bbox_reg[idx + k * num_elem * 4 + num_elem * 2] * 0.2);
det->bbox[3] = prior[3] * expf(bbox_reg[idx + k * num_elem * 4 + num_elem * 3] * 0.2);
det->bbox[0] -= det->bbox[2] / 2;
det->bbox[1] -= det->bbox[3] / 2;
det->bbox[2] += det->bbox[0];
det->bbox[3] += det->bbox[1];
det->bbox[0] *= decodeplugin::INPUT_W;
det->bbox[1] *= decodeplugin::INPUT_H;
det->bbox[2] *= decodeplugin::INPUT_W;
det->bbox[3] *= decodeplugin::INPUT_H;
det->class_confidence = conf2;
for (int i = 0; i < 10; i += 2) {
det->landmark[i] = prior[0] + lmk_reg[idx + k * num_elem * 10 + num_elem * i] * 0.1 * prior[2];
det->landmark[i+1] = prior[1] + lmk_reg[idx + k * num_elem * 10 + num_elem * (i + 1)] * 0.1 * prior[3];
det->landmark[i] *= decodeplugin::INPUT_W;
det->landmark[i+1] *= decodeplugin::INPUT_H;
}
}
}
void DecodePlugin::forwardGpu(const float *const * inputs, float * output, cudaStream_t stream, int batchSize)
{
int num_elem = 0;
int base_step = 8;
int base_anchor = 16;
int thread_count;
cudaMemset(output, 0, sizeof(float));
for (unsigned int i = 0; i < 3; ++i)
{
num_elem = decodeplugin::INPUT_H / base_step * decodeplugin::INPUT_W / base_step;
thread_count = (num_elem < thread_count_) ? num_elem : thread_count_;
CalDetection<<< (num_elem + thread_count - 1) / thread_count, thread_count>>>
(inputs[i], output, num_elem, base_step, base_anchor);
base_step *= 2;
base_anchor *= 4;
}
}
int DecodePlugin::enqueue(int batchSize, const void*const * inputs, void** outputs, void* workspace, cudaStream_t stream)
{
//assert(batchSize == 1);
//GPU
//CUDA_CHECK(cudaStreamSynchronize(stream));
forwardGpu((const float *const *)inputs,(float *)outputs[0],stream,batchSize);
return 0;
};
PluginFieldCollection DecodePluginCreator::mFC{};
std::vector<PluginField> DecodePluginCreator::mPluginAttributes;
DecodePluginCreator::DecodePluginCreator()
{
mPluginAttributes.clear();
mFC.nbFields = mPluginAttributes.size();
mFC.fields = mPluginAttributes.data();
}
const char* DecodePluginCreator::getPluginName() const
{
return DECODE_PLUGIN_NAME;
}
const char* DecodePluginCreator::getPluginVersion() const
{
return DECODE_PLUGIN_VERSION;
}
const PluginFieldCollection* DecodePluginCreator::getFieldNames()
{
return &mFC;
}
IPluginV2IOExt* DecodePluginCreator::createPlugin(const char* name, const PluginFieldCollection* fc)
{
DecodePlugin* obj = new DecodePlugin();
obj->setPluginNamespace(mNamespace.c_str());
return obj;
}
IPluginV2IOExt* DecodePluginCreator::deserializePlugin(const char* name, const void* serialData, size_t serialLength)
{
// This object will be deleted when the network is destroyed, which will
// call PReluPlugin::destroy()
DecodePlugin* obj = new DecodePlugin(serialData, serialLength);
obj->setPluginNamespace(mNamespace.c_str());
return obj;
}
REGISTER_TENSORRT_PLUGIN(DecodePluginCreator);
}
saved in .cu extension file. Now when I deserialize the engine file in NVIDIA tensorrt container works perfectly fine. But when I use the libdecodeplugin.so to deserialize the generated engine file it doesn’t seem to work in NVIDIA DeepStream container. Below is the terminal output:
Warn: 'threshold' parameter has been deprecated. Use 'pre-cluster-threshold' instead.
Now playing: ../../../samples/streams/sample_720p.mp4
ERROR: ../nvdsinfer/nvdsinfer_func_utils.cpp:60 Could not open lib: /opt/nvidia/deepstream/deepstream-5.0/sources/apps/deepstream-retinaface-multistream/libdecodeplugin.so, error string: /opt/nvidia/deepstream/deepstream-5.0/sources/apps/deepstream-retinaface-multistream/libdecodeplugin.so: undefined symbol: getPluginRegistry
0:00:00.986501706 101 0x557a92760ed0 ERROR nvinfer gstnvinfer.cpp:596:gst_nvinfer_logger:<primary-nvinference-engine> NvDsInferContext[UID 1]: Error in NvDsInferContextImpl::initialize() <nvdsinfer_context_impl.cpp:1015> [UID = 1]: Could not open custom lib: (null)
0:00:00.986549778 101 0x557a92760ed0 WARN nvinfer gstnvinfer.cpp:781:gst_nvinfer_start:<primary-nvinference-engine> error: Failed to create NvDsInferContext instance
0:00:00.986563826 101 0x557a92760ed0 WARN nvinfer gstnvinfer.cpp:781:gst_nvinfer_start:<primary-nvinference-engine> error: Config file path: retinaface_pgie_config.txt, NvDsInfer Error: NVDSINFER_CUSTOM_LIB_FAILED
Running...
ERROR from element primary-nvinference-engine: Failed to create NvDsInferContext instance
Error details: gstnvinfer.cpp(781): gst_nvinfer_start (): /GstPipeline:dstest1-pipeline/GstNvInfer:primary-nvinference-engine:
Config file path: retinaface_pgie_config.txt, NvDsInfer Error: NVDSINFER_CUSTOM_LIB_FAILED
Returned, stopping playback
Deleting pipeline
I did not find any good resources for this issue. Thanks in advance!