• Hardware Platform (Jetson )
• DeepStream Version 6.1
• JetPack Version tritonserver2.27.0-jetpack5.0.2
• TensorRT Version 8.4.1.5 + cuda11.4.239
• Issue Type( bugs)
The NvDsInferLayerInfo in std::vector is found in the YoloV5 custom post-processing plug-in after GRPC communication through tritonserver and Gst-nvinferserver The buffer pointer becomes an outfield pointer, causing the program to throw signal 11.
tritonserver config：
config.pbtxt
platform: "tensorrt_plan"
max_batch_size: 1
default_model_filename: "mutilModelsA.engine"
input [
{
name: "images"
data_type: TYPE_FP32
dims: [ 3, 384, 640 ]
}
]
output [
{
name: "output1"
data_type: TYPE_FP32
dims: [ 3, 12, 20 , 28 ]
},
{
name: "output2"
data_type: TYPE_FP32
dims: [ 3, 24, 40 , 28 ]
},
{
name: "output3"
data_type: TYPE_FP32
dims: [ 3, 48, 80 , 28 ]
}
]
instance_group [
{
count: 1
kind: KIND_GPU
gpus: [ 0 ]
}
]
dynamic_batching {
preferred_batch_size: [1]
max_queue_delay_microseconds: 5000000
preserve_ordering: true
}
version_policy: { all { }}
optimization { execution_accelerators {
gpu_execution_accelerator : [ {
name : "tensorrt"
parameters { key: "precision_mode" value: "FP16" }
parameters { key: "max_workspace_size_bytes" value: "1073741824" }
}]
}}
Gst-nvinferserver ：
infer_config {
unique_id: 1
gpu_ids: [0]
max_batch_size: 1
backend {
inputs: [ {
name: "images"
}
]
outputs: [
{name: "output1"},
{name: "output2"},
{name: "output3"}
]
triton {
model_name: "mutilModelsA1"
version: 1
grpc {
url: "localhost:8052"
}
}
}
preprocess {
network_format: IMAGE_FORMAT_RGB
tensor_order: TENSOR_ORDER_LINEAR
tensor_name: "images"
maintain_aspect_ratio: 0
frame_scaling_hw: FRAME_SCALING_HW_DEFAULT
frame_scaling_filter: 1
normalize {
scale_factor: 0.0039215697906911373
channel_offsets: [0, 0, 0]
}
}
custom_lib {
path: "models/plugins/libnvdsinfer_custom_impl_Yolo.so"
}
postprocess {
labelfile_path: "models/Runmodels/mutilModelsA1/labels.txt"
detection {
num_detected_classes: 23
custom_parse_bbox_func: "NvDsInferParseCustomYoloV5_3_Out"
per_class_params {
key: 0
value { pre_threshold: 0.6 }
}
nms {
confidence_threshold:0.2
topk:20
iou_threshold:0.6
}
}
}
}
input_control {
process_mode: PROCESS_MODE_FULL_FRAME
operate_on_gie_id: -1
interval: 25
}
output_control {
detect_control {
default_filter {
bbox_filter {
min_width: 32,
min_height: 32
}
}
}
}
- Post-processing code interface
static inline std::vector<const NvDsInferLayerInfo*>
SortLayers(const std::vector<NvDsInferLayerInfo> & outputLayersInfo)
{
std::vector<const NvDsInferLayerInfo*> outLayers;
for (auto const &layer : outputLayersInfo) {
outLayers.push_back (&layer);
}
std::sort(outLayers.begin(), outLayers.end(),
[ ](const NvDsInferLayerInfo* a, const NvDsInferLayerInfo* b) {
return a->inferDims.d[1] < b->inferDims.d[1];
});
return outLayers;
}
extern "C" bool NvDsInferParseCustomYoloV5_3_Out(
std::vector<NvDsInferLayerInfo> const& outputLayersInfo,
NvDsInferNetworkInfo const& networkInfo,
NvDsInferParseDetectionParams const& detectionParams,
std::vector<NvDsInferParseObjectInfo>& objectList)
{
const int anchor[3][6] = {{116, 90, 156, 198, 373, 326},{30, 61, 62, 45, 59, 119},{10, 13, 16, 30, 33, 23}};
assert(outputLayersInfo.size() == 3); //3 out
assert(outputLayersInfo[0].inferDims.numDims == 4); //3*grid_h*grid_w*idScore
assert(outputLayersInfo[0].inferDims.d[3] == (detectionParams.numClassesConfigured+5));
const std::vector<const NvDsInferLayerInfo*> sortedLayers =
SortLayers (outputLayersInfo);
for (uint idx = 0; idx < outputLayersInfo.size(); ++idx) {
const NvDsInferLayerInfo &layer = *sortedLayers[idx];
const uint gridSizeH = layer.inferDims.d[1];
const uint gridSizeW = layer.inferDims.d[2];
const uint stride = DIVUP(networkInfo.width, gridSizeW);
assert(stride == DIVUP(networkInfo.height, gridSizeH));
size_t size = layer.inferDims.numElements;
float *pBuf = (float*)(layer.buffer);
std::cout <<"*pBuf:" <<*pBuf<<std::endl; //There is a paragraph error (signal 11) I think it's become a wild pointer
std::vector<NvDsInferParseObjectInfo> outObjs =
decodeYoloV5_3_Tensor((const float*)(layer.buffer), anchor[idx], gridSizeW, gridSizeH, stride,
detectionParams, networkInfo.width, networkInfo.height);
objectList.insert(objectList.end(), outObjs.begin(), outObjs.end());
}
return true;
}