I’m trying to run inference on Triton Inference Server with faster rcnn model trained with TLT, converted to TensorRT.
In docs I can see:
Note that the models can also be deployed outside of DeepStream using TensorRT but users will need to do image pre-processing and post-process the output Tensor after inference.
What kind of pre-process and post-process should I use for FasterRCNN and other available object detection models?
Currently I have OpenCV’s Mat (8UC3)-> 32FC3 → channel-wise array (3, width, height) (RRRGGGBBB) → pack data into bytestring and send request.
Then I parse output, but NMS_1 always equals zero.
Triton generated this config: {"name":"frcnn_fp16","versions":["1"],"platform":"tensorrt_plan","inputs":[{"name":"input_image","datatype":"FP32","shape":[-1,3,384,1248]}],"outputs":[{"name":"NMS","datatype":"FP32","shape":[-1,1,100,7]},{"name":"NMS_1","datatype":"FP32","shape":[-1,1,1,1]}]}
Hi to all,
I was able to launch the INCEPTION SSD network plan file via tritonserver and write a python client to query it. However while I can get the correct output for NMS for the bboxes, NMS_1 (Keepcount) always gives me zero value. How is it possible ? The weird thing is that if I run the example script of tensorrt which doesn’t use the triton server I get the correct output of 100. thanks in advance