trt-yolo-app on xavier

Hi, I install the trt-yolo-app on my xavier, But When I test it with my own trained yolov3 network, i noticed that the yolo.cpp requires m_InputW must equal to m_InputH , So my work can’t go on, I think it is relative to the upsampling operation. Can you give me some advice?


Do you use our GitHub:
Could you share where the width=height limitation is?


yeah, of course, I use the link in that repo, I want to use Xavier to run YOLOv3 which I trained with TensorRT, I noticed trt-yolo-app, when I run it , I find the feature map in it must be symmetric.
For example:

at line236 in yolo.cpp:
        else if ("type") == "yolo")
            nvinfer1::Dims prevTensorDims = previous->getDimensions();
            assert(prevTensorDims.d[1] == prevTensorDims.d[2]);
            TensorInfo& curYoloTensor =;
            curYoloTensor.gridSize = prevTensorDims.d[1];
            curYoloTensor.stride = m_InputW / curYoloTensor.gridSize;
   = curYoloTensor.gridSize
                * curYoloTensor.gridSize
                * (curYoloTensor.numBBoxes * (5 + curYoloTensor.numClasses));

it only requires one dimension, the upsample layer also likes this, but I have fixed these bugs, but I found when I ran it, it costed 1200 ms for one image??? I dont know what happend, But its too slow than I run it without TensorRT… I wonder why…
What’s more , the function"NV_CUDA_CHECK()", where can i find its details, I have some troubles in destroying memory, which makes me can not inference too many pictures.
Hope to get your reply! thanks!!!


Could you enable the profiler in TensorRT first?

This can give you a layer level performance report and we can find where is the bottleneck from.