• Hardware Platform (Jetson / GPU) Dual Nvidia A2
• DeepStream Version 6.3
• JetPack Version (valid for Jetson only)
• TensorRT Version 8.5
• NVIDIA GPU Driver Version (valid for GPU only) 535.154.05
• Issue Type( questions, new requirements, bugs) questions & bug
• How to reproduce the issue ? (This is for bugs. Including which sample app is using, the configuration files content, the command line used and other details for reproducing)
• Requirement details( This is for new requirement. Including the module name-for which plugin or for which sample application, the function description
Hello there, I want to replace peoplenet as pgie with yolov8 in my system as a tensorrt engine , I started out with exporting the original yolov8 torch model from the official ultralytics repo to a dynamic onnx version using this code
from ultralytics import YOLO
model = YOLO('yolov8s.pt') # load an official model
# model = YOLO('path/to/best.pt') # load a custom model
# Predict with the model
# results = model('https://ultralytics.com/images/bus.jpg', save= True) # predict on an image
model.export(format ="onnx",dynamic=True,imgsz=(640,640))
then used the following command to export to trt model
trtexec --saveEngine=./yolov8_s.engine --fp16 --onnx=./yolov8s.onnx --minShapes=images:1x3x640x640 --optShapes=images:64x3x640x640 --maxShapes=images:64x3x640x640 --shapes=images:64x3x640x640 --workspace=10000
when running the deepstream pipeline I’m facing this error
ERROR: infer_postprocess.cpp:623 Could not find output coverage layer for parsing objects
ERROR: infer_postprocess.cpp:1078 Failed to parse bboxes
ERROR: infer_postprocess.cpp:388 detection parsing output tensor data failed, uid:1, nvinfer error:NVDSINFER_OUTPUT_PARSING_FAILED
ERROR: infer_postprocess.cpp:275 Infer context initialize inference info failed, nvinfer error:NVDSINFER_OUTPUT_PARSING_FAILED
0:00:03.940816100 1 0x7f3e20001d80 WARN nvinferserver gstnvinferserver.cpp:581:gst_nvinfer_server_push_buffer:<primary-inference> error: inference failed with unique-id:1
and after some investigations I can tell that my configs need to be adjusted, the problem is I don’t know how to adjust them to work properly with deepstream 6.3, please let me know what needs to be modified.
the triton config file is
name: "yolov8"
platform: "tensorrt_plan"
max_batch_size: 64
default_model_filename: "yolov8_s.engine"
input [
{
name: "images"
data_type: TYPE_FP32
format: FORMAT_NCHW
dims: [ 3, 640, 640 ]
}
]
output [
{
name: "output0"
data_type: TYPE_FP32
dims: [ 84, 8400]
}
]
instance_group [
{
kind: KIND_GPU
count: 1
gpus: 0
}
]
and for the deepstream config
infer_config {
unique_id: 1
gpu_ids: [0]
max_batch_size: 64
backend {
triton {
model_name: "yolov8"
version: -1
model_repo {
root: "/opt/nvidia/deepstream/deepstream-6.3/samples/triton_model_repo"
strict_model_config: true
}
}
}
preprocess {
network_format: IMAGE_FORMAT_RGB
tensor_order: TENSOR_ORDER_LINEAR
tensor_name: "images"
maintain_aspect_ratio: 1
frame_scaling_hw: FRAME_SCALING_HW_DEFAULT
frame_scaling_filter: 1
symmetric_padding: 1
normalize {
scale_factor: 0.0039215697906911373
channel_offsets: [0, 0, 0]
}
}
postprocess {
labelfile_path: "/opt/nvidia/deepstream/deepstream/samples/configs/tao_pretrained_models/labels_peoplenet.txt"
detection {
num_detected_classes: 80
per_class_params {
key: 0
value { pre_threshold: 0.25 }
}
nms {
confidence_threshold:0.2
topk:100
iou_threshold:0.45
}
}
}
extra {
copy_input_to_host_buffers: false
output_buffer_pool_size: 128
}
}
input_control {
process_mode: PROCESS_MODE_FULL_FRAME
operate_on_gie_id: -1
interval: 0
}
output_control {
output_tensor_meta: true
}
