yinfan98
commented
10 months ago Hi, I think you trans your onnx -> TensorRT in windows? This step should in your device.
Open HEIseYOUmolc opened 10 months ago
yinfan98
commented
10 months ago Hi, I think you trans your onnx -> TensorRT in windows? This step should in your device.
HEIseYOUmolc
commented
10 months ago Hi, I think you trans your onnx -> TensorRT in windows? This step should in your device.
@yinfan98 Thanks for the reply, is what you said a must on the target device? My mmdet on Jeston Nano only allows version 2.27 to apply my model more difficult /(ㄒoㄒ)/~~
yinfan98
commented
10 months ago Hi @HEIseYOUmolc , you need to export the TensorRT model on the Jetson device. Maybe you can try deployee: https://platform.openmmlab.com/deploee to export TensorRT model on Jetson. I've submitted a PR that supports mmdet 3.0 on jetpack 4.6 devices. it will be live shortly!
HEIseYOUmolc
commented
10 months ago Hi @HEIseYOUmolc , you need to export the TensorRT model on the Jetson device. Maybe you can try deployee: https://platform.openmmlab.com/deploee to export TensorRT model on Jetson. I've submitted a PR that supports mmdet 3.0 on jetpack 4.6 devices. it will be live shortly! @yinfan98
great，Waiting for your release.
I already try to convert my model by trtexec, and try replace the .engine file with the trtexec conversion ，but in c++ demo it also have error like onnxruntime [01/07/2024-18:35:51] [I] === Trace details === [01/07/2024-18:35:51] [I] Trace averages of 10 runs: [01/07/2024-18:35:51] [I] Average on 10 runs - GPU latency: 500.888 ms - Host latency: 501.397 ms (end to end 502 ms, enqueue 191.146 ms) [01/07/2024-18:35:51] [I] [01/07/2024-18:35:51] [I] === Performance summary === [01/07/2024-18:35:51] [I] Throughput: 1.99203 qps [01/07/2024-18:35:51] [I] Latency: min = 498.864 ms, max = 503.506 ms, mean = 501.397 ms, median = 501.815 ms, percentile(99%) = 503.506 ms [01/07/2024-18:35:51] [I] End-to-End Host Latency: min = 499.323 ms, max = 504.746 ms, mean = 502 ms, median = 502.486 ms, percentile(99%) = 504.746 ms [01/07/2024-18:35:51] [I] Enqueue Time: min = 25.5835 ms, max = 363.854 ms, mean = 191.146 ms, median = 189.779 ms, percentile(99%) = 363.854 ms [01/07/2024-18:35:51] [I] H2D Latency: min = 0.48291 ms, max = 0.531982 ms, mean = 0.504181 ms, median = 0.501709 ms, percentile(99%) = 0.531982 ms [01/07/2024-18:35:51] [I] GPU Compute Time: min = 498.351 ms, max = 503.006 ms, mean = 500.888 ms, median = 501.289 ms, percentile(99%) = 503.006 ms [01/07/2024-18:35:51] [I] D2H Latency: min = 0.00305176 ms, max = 0.00585938 ms, mean = 0.00477295 ms, median = 0.00488281 ms, percentile(99%) = 0.00585938 ms [01/07/2024-18:35:51] [I] Total Host Walltime: 5.02 s [01/07/2024-18:35:51] [I] Total GPU Compute Time: 5.00888 s [01/07/2024-18:35:51] [I] Explanations of the performance metrics are printed in the verbose logs. [01/07/2024-18:35:51] [I] &&&& PASSED TensorRT.trtexec [TensorRT v8201] # trtexec --onnx=/home/nvidia/文档/mmdeploy_models/test/end2end.onnx --saveEngine=end2end.engine --plugins=/home/nvidia/文档/mmdeploy/mmdeploy/lib/libmmdeploy_tensorrt_ops.so --workspace=1024
error message
[2024-01-09 15:26:53.939] [mmdeploy] [info] [model.cpp:35] [DirectoryModel] Load model: "/home/nvidia/文档/mmdeploy_models/rtdetr-trt-sta-640/"
[2024-01-09 15:26:54.174] [mmdeploy] [error] [resize.cpp:84] unsupported interpolation method: bicubic
[2024-01-09 15:26:54.174] [mmdeploy] [error] [task.cpp:99] error parsing config: {
"context": {
"device": "
HEIseYOUmolc
commented
10 months ago Latest test result: I used jetsonnano for converting the tensorrt format and the conversion works fine, but I still get the following error during the c++ run:
./object_detection cuda /home/nvidia/Documents/work-dir /home/nvidia/Pictures/resources/test1.jpg
[2024-01-15 16:55:53.326] [mmdeploy] [info] [model.cpp:35] [DirectoryModel] Load model: "/home/nvidia/Documents/work-dir"
[2024-01-15 16:55:54.065] [mmdeploy] [error] [resize.cpp:84] unsupported interpolation method: bicubic
[2024-01-15 16:55:54.066] [mmdeploy] [error] [task.cpp:99] error parsing config: {
"context": {
"device": "
I think it's probably due to unsupported interpolation method: bicubic, but I don't have the ability to fix him ah!
Checklist
Describe the bug
模型转换由windows执行，将产生的onnx模型与engine模型文件迁移至jetson nano 模组中，部署过程中出现如下错误信息。仅在onnxruntime cpu 设置下可正常运行，cuda模式下均不可运行。
此外，可以使用trt在jetson nano 上转换onnx模型为engine格式，但我对engine了解较少，是否有其他部署方法？
Reproduction
在不同设置编译下的文件进行运行
trt编译
cmake .. -DMMDEPLOY_BUILD_SDK=ON -DMMDEPLOY_BUILD_SDK_PYTHON_API=ON -DMMDEPLOY_BUILD_EXAMPLES=ON -DMMDEPLOY_TARGET_DEVICES="cuda;cpu" -DMMDEPLOY_TARGET_BACKENDS="trt" -DMMDEPLOY_CODEBASES=all -Dpplcv_DIR=${PPLCV_DIR}/cuda-build/install/lib/cmake/pplort编译
cmake .. -DMMDEPLOY_BUILD_SDK=ON -DMMDEPLOY_BUILD_SDK_PYTHON_API=ON -DMMDEPLOY_BUILD_EXAMPLES=ON -DMMDEPLOY_TARGET_DEVICES="cuda;cpu" -DMMDEPLOY_TARGET_BACKENDS="ort" -DMMDEPLOY_CODEBASES=all -Dpplcv_DIR=${PPLCV_DIR}/cuda-build/install/lib/cmake/ppl -DONNXRUNTIME_DIR=${ONNXRUNTIME_DIR}运行命令
使用TensorRT
./object_detection cuda /home/nvidia/文档/mmdeploy_models/rtdetr-trt-sta-640/ /home/nvidia/图片/resources/test.jpg使用ONNXRUNTIME./object_detection cuda /home/nvidia/文档/mmdeploy_models/rtdetr-ort-dyn/ /home/nvidia/图片/resources/test.jpgEnvironment
Error traceback