Thank you for the pointer. I’ll look into correcting it.
I should post more details about the model I am using to determine the right value for network-type and other nvinfer config parameters. I was looking into the nvinfer documentation and it says
Blockquote
Gst-nvinfer currently works on the following type of networks:
• Multi-class object detection
• Multi-label classification
• Segmentation
If the network-type is 2, then as per this thread Getting Segmentation Meta Data (NvDsInferSegmentationMeta) of custom segmentation model I need to implement NvDsInferSegmentationMeta.
However, this is a pose-estimation model. It outputs a heatmap (128x128) which I need to analyze. In the GitHub - NVIDIA-AI-IOT/deepstream_pose_estimation: This is a sample DeepStream application to demonstrate a human pose estimation pipeline. example, I see that then network-type is set to 100 and there is no bbox parsing function. The output tensor meta is being analyzed in the src pad buffer probe.
With the following SGIE configuration, the output tensor meta in obj_user_meta_list gives me constant float values above 1.0 but under 100.0. Now, this is without a bbox parsing function.
[property]
gpu-id=0
net-scale-factor=1
model-engine-file=onnx_b1_gpu0_fp32.engine
batch-size=1
force-implicit-batch-dim=0
model-color-format=0
process-mode=2
## 0=FP32, 1=INT8, 2=FP16 mode
network-mode=0
is-classifier=0
output-blob-names=model_1
segmentation-threshold=0.1
input-object-min-width=100
input-object-min-height=100
#network-type 0: Detector 1: Classifier 2: Segmentation 3: Instance Segmentation
network-type=100
operate-on-gie-id=1
operate-on-class-ids=0;1
#scaling-filter=0
#scaling-compute-hw=0
output-tensor-meta=1
gie-unique-id=3
How do you determine the right parameters for pose-estimation model?
Is a bbox parser even needed for such a model?
Here is what nvinfer reports when it loads the model
INFO: ../nvdsinfer/nvdsinfer_model_builder.cpp:685 [FullDims Engine Info]: layers num: 2
0 INPUT kFLOAT model_1_input 3x512x512 min: 1x3x512x512 opt: 1x3x512x512 Max: 1x3x512x512
1 OUTPUT kFLOAT model_1 128x128x1 min: 0 opt: 0 Max: 0