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README.md
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ScatterNDPlugin.cu
ScatterNDPlugin.cu
 
 
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config.h
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postprocess.cpp
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postprocess.h
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preprocess.h
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samplecenterpoint.cpp
samplecenterpoint.cpp
 
 

README.md

Centerpoint Pointpillars For TensorRT From ONNX

Running the sample

  1. Generate the onnx model pointpillars_trt.onnx for tensrorrt.
git lfs clone https://github.com/CarkusL/CenterPoint.git
python tools/merge_pfe_rpn_model.py

  1. Prepare the tensorrt environment. I use the nvcr.io/nvidia/tensorrt:21.02-py3 image.

    docker pull nvcr.io/nvidia/tensorrt:21.02-py3
docker run --gpus all -it --rm -v local_dir:container_dir nvcr.io/nvidia/tensorrt:21.02-py3 /bin/bash

  2. (optinal) Generate the tensorrt input file by TensorRT_Visualize.ipynb to get input points.bin in CenterPoint/tensorrt/data/centerpoint

  3. Copy these CenterPoint/tensorrt/sample/centerpoint and CenterPoint/tensorrt/data/centerpoint to Tensorrt root folder. If you use the docker image in Step 1, the folder should be /usr/src/tensorrt/.

  4. Compile this sample by running make in the <TensorRT root directory>/samples/centerpoint directory. The binary named centerpoint will be created in the <TensorRT root directory>/bin directory.

    cd <TensorRT root directory>/samples/centerpoint
make

    Where <TensorRT root directory> is where you installed TensorRT.

  5. Run the sample to build and run the MNIST engine from the ONNX model.

    ./centerpoint

  6. Verify that the sample ran successfully. If the sample runs successfully you should see output similar to the following:

    &&&& RUNNING TensorRT.sample_onnx_centerpoint # ./centerpoint
[08/07/2021-08:26:47] [I] Building and running a GPU inference engine for CenterPoint
[08/07/2021-08:26:55] [I] [TRT] ----------------------------------------------------------------
[08/07/2021-08:26:55] [I] [TRT] Input filename:   ../data/centerpoint/pointpillars_trt.onnx
[08/07/2021-08:26:55] [I] [TRT] ONNX IR version:  0.0.6
[08/07/2021-08:26:55] [I] [TRT] Opset version:    11
[08/07/2021-08:26:55] [I] [TRT] Producer name:    pytorch
[08/07/2021-08:26:55] [I] [TRT] Producer version: 1.7
[08/07/2021-08:26:55] [I] [TRT] Domain:           
[08/07/2021-08:26:55] [I] [TRT] Model version:    0
[08/07/2021-08:26:55] [I] [TRT] Doc string:       
[08/07/2021-08:26:55] [I] [TRT] ----------------------------------------------------------------
[08/07/2021-08:26:55] [W] [TRT] /home/jenkins/workspace/OSS/L0_MergeRequest/oss/parsers/onnx/onnx2trt_utils.cpp:226: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[08/07/2021-08:26:55] [I] [TRT] No importer registered for op: ScatterND. Attempting to import as plugin.
[08/07/2021-08:26:55] [I] [TRT] Searching for plugin: ScatterND, plugin_version: 1, plugin_namespace: 
[08/07/2021-08:26:55] [I] [TRT] Successfully created plugin: ScatterND
[08/07/2021-08:26:55] [W] [TRT] Tensor DataType is determined at build time for tensors not marked as input or output.
[08/07/2021-08:26:55] [W] [TRT] Tensor DataType is determined at build time for tensors not marked as input or output.
[08/07/2021-08:26:55] [W] [TRT] Tensor DataType is determined at build time for tensors not marked as input or output.
[08/07/2021-08:26:55] [W] [TRT] Tensor DataType is determined at build time for tensors not marked as input or output.
[08/07/2021-08:26:55] [W] [TRT] Tensor DataType is determined at build time for tensors not marked as input or output.
[08/07/2021-08:26:55] [W] [TRT] Tensor DataType is determined at build time for tensors not marked as input or output.
[08/07/2021-08:27:02] [I] [TRT] Some tactics do not have sufficient workspace memory to run. Increasing workspace size may increase performance, please check verbose output.
[08/07/2021-08:27:15] [I] [TRT] Detected 2 inputs and 42 output network tensors.
[08/07/2021-08:27:15] [I] getNbInputs: 2 

[08/07/2021-08:27:15] [I] getNbOutputs: 42 

[08/07/2021-08:27:15] [I] getNbOutputs Name: 594 

filePath[idx]: ../data/centerpoint//points/048a45dd2cf54aa5808d8ccc85731d44.bin
[08/07/2021-08:27:15] [I] [INFO] pointNum : 278690
[08/07/2021-08:27:15] [I] PreProcess Time: 11.9223 ms
[08/07/2021-08:27:15] [I] inferenceDuration Time: 7.4477 ms
[08/07/2021-08:27:15] [I] PostProcessDuration Time: 1.97806 ms
filePath[idx]: ../data/centerpoint//points/06be0e3b665c44fa8d17d9f4770bdf9c.bin
[08/07/2021-08:27:15] [I] [INFO] pointNum : 258553
[08/07/2021-08:27:15] [I] PreProcess Time: 7.99008 ms
[08/07/2021-08:27:15] [I] inferenceDuration Time: 6.95937 ms
[08/07/2021-08:27:15] [I] PostProcessDuration Time: 1.21248 ms
filePath[idx]: ../data/centerpoint//points/07fad91090c746ccaa1b2bdb55329e20.bin
[08/07/2021-08:27:15] [I] [INFO] pointNum : 285130
[08/07/2021-08:27:15] [I] PreProcess Time: 9.02984 ms
[08/07/2021-08:27:15] [I] inferenceDuration Time: 6.8961 ms
[08/07/2021-08:27:15] [I] PostProcessDuration Time: 1.38823 ms
filePath[idx]: ../data/centerpoint//points/0a0d6b8c2e884134a3b48df43d54c36a.bin
[08/07/2021-08:27:15] [I] [INFO] pointNum : 278272
[08/07/2021-08:27:15] [I] PreProcess Time: 9.25149 ms
[08/07/2021-08:27:15] [I] inferenceDuration Time: 7.06773 ms
[08/07/2021-08:27:15] [I] PostProcessDuration Time: 1.53539 ms
filePath[idx]: ../data/centerpoint//points/0af0feb5b1394b928dd13d648de898f5.bin
[08/07/2021-08:27:15] [I] [INFO] pointNum : 273692
[08/07/2021-08:27:15] [I] PreProcess Time: 9.12371 ms
[08/07/2021-08:27:15] [I] inferenceDuration Time: 7.11943 ms
[08/07/2021-08:27:15] [I] PostProcessDuration Time: 1.63056 ms
filePath[idx]: ../data/centerpoint//points/0bb62a68055249e381b039bf54b0ccf8.bin
[08/07/2021-08:27:15] [I] [INFO] pointNum : 286052
[08/07/2021-08:27:15] [I] PreProcess Time: 10.143 ms
[08/07/2021-08:27:15] [I] inferenceDuration Time: 7.20578 ms
[08/07/2021-08:27:15] [I] PostProcessDuration Time: 1.82941 ms
...
&&&& PASSED TensorRT.sample_onnx_centerpoint # ./centerpoint

  7. copy the <TensorRT root directory>/data/centerpoint back the CenterPoint/tensorrt/data

  8. Run the TensorRT_Visualize.ipynb to do evaluation and visualiza tensorrt result.

  9. Compare the TensorRT result with Pytorch result.

TensoRT Pytroch
avatar avatar

3D detection on nuScenes Mini dataset

TensorRT postprocess use aligned NMS on Bev, so there are some precision loss.

mAP mATE mASE mAOE mAVE mAAE NDS
Pytorch 0.4163 0.4438 0.4516 0.5674 0.4429 0.3288 0.4847
TensorRT 0.4007 0.4433 0.4537 0.5665 0.4416 0.3191 0.4779