• Hardware Platform: GPU
Tensor RT version: TensorRT 8.6.1.6
LPDNet model
using the LPDNet_usa_pruned_tao5.onnx file to load and run on triton inference server. I am getting the inference results using this model.
config file
name: “lpdnet”
platform: “onnxruntime_onnx”
max_batch_size : 1
input [
{
name: “input_1:0”
data_type:TYPE_FP32
format: FORMAT_NCHW
dims: [3,480,640]
}
]
output [
{
name: “output_cov/Sigmoid:0”
data_type:TYPE_FP32
dims: [1,30,40]
}
]
output [
{
name: “output_bbox/BiasAdd:0”
data_type:TYPE_FP32
dims: [4,30,40]
}
]
dynamic_batching { }
How to do post processing in this LPDNet model?
In the taotoolkil sample code for detectnet_v2 , a clustering config is required. What clustering config should i use for postprocessing in this case?
Is any code available for doing post processing in python
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