I am trying to integrate a ONNX model for secondary inference with DeepStream. The goal is to get the output tensors for post-processing.
This is what I have in the custom application.
pgie → nvtracker → sgie (onnx model)
About the model:
Input layer: 3x512x512
Output layer: 128x128x1
Objects are being detected by the pgie and information about them can be retrieved from the src pad probe buffer. The sgie however doesn’t seem to be processing the detection. process-mode is set to 2.
[property]
gpu-id=0
net-scale-factor=1
model-engine-file=onnx_b1_gpu0_fp32.engine
infer-dims=3;512;512
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
input-object-min-width=100
input-object-min-height=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=4
parse-bbox-func-name=NvDsInferParseCustomOnnx
custom-lib-path=custom_bbox_onnx/libnvdsinfer_custom_bbox_onnx.so
Just as a test, I set process-mode to 1 and the parsing code did get invoked albeit with output layer data containing incorrect values in the output layer buffer (NvDsInferLayerInfo layer.buffer).
process-mode=1 does invoke the bounding box parser but the floating point values in the output layer buffer turn out to be completely incorrect (100.0 and above instead of being between 0 and 1).
Is there a way to verify if this model can work with DeepStream?
• Hardware Platform (Jetson / GPU)
GPU
• DeepStream Version
5.0
• JetPack Version (valid for Jetson only)
• TensorRT Version
7.0 TensorRT OSS
• NVIDIA GPU Driver Version (valid for GPU only)
460.39
• Issue Type( questions, new requirements, bugs)
Questions
• 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)