TensorRT-MTR

Warning

This repository has not been developed yet, and moved in ktro2828/autoware_mtr.

Support of inference with TensorRT for sshaoshuai/MTR.

Inputs/Outputs

Inputs

• trajectory <float; [B, N, Tp, Da]>
• trajectory_mask <bool: [B, N, Tp]>
• polyline <float: [B, K, P, Dp]>
• polyline_mask <bool: [B, K, P]>
• polyline_center <float: [B, K, 3]>
• last_pos <float; [B, N, 3]
• track_index <int: [B]>
• label_index <int: [N]>
• intention_points <float: [B, 64, 3]>

where,

• B ...The number of target agents
• N ...The number of all agents
• Tp ...The number of past frames(=11)
• Da ...The number of agent state dimensions(=29)
• K ...The max number of polylines(=768)
• P ...The max number of points contained in each polyline(=20)
• Dp ...The number of polyline state dimensions(=9)

Outputs

• scores <float: [B, M]>
• trajectory <float: [B, M, Tf, Dt]>

where,

• M ...The number of modes
• Tf ...The number of the predicted future frames(=80)
• Dt ...The number of the predicted trajectory dimensions(=7) in the order of (x, y, dx, dy, yaw, vx, cy).

Build & Run

Download onnx

# download onnx.zip
gh release download onnx

Build

cmake -B build && cmake --build build -j${nproc}

Execute

• With trtexec

# with trtexec
<PATH_TO_TRTEXEC_BIN>/trtexec --onnx=<PATH_TO_ONNX> --staticPlugins=./build/libtrtmtr_plugin.so

• With executable

Fist, please install trtmtr with cmake --install <DIR>:

sudo cmake --install build

Note

Note that, $LD_LIBRARY_PATH includes /usr/local/lib.
If not, append export LD_LIBRARY_PATH="/usr/local/lib:$LD_LIBRARY_PATH" to your .bashrc.

Then, run the following command:

trtmtr <PATH_TO_ONNX_OR_ENGINE> [--dynamic --fp16 -n <NUM_REPEAT>]

Unittest

# test agent data container defined in `include/mtr/agent.hpp`
./build/test_agent

# test polyline data container defined in `include/mtr/polyline.hpp`
./build/test_polyline

# test intention point data container defined in `include/mtr/intention_point.hpp`
./build/test_intention_point

TODO

• TensorRT custom plugins
• CUDA kernels
  • pre-process
  • post-process
• Shape inference
  • static shape
  • dynamic shape
• Inference sample
• Visualization
• Evaluation