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RAFT

How to Run (Pytorch)

  1. set up a virtual environment.
cd RAFT
https://github.com/princeton-vl/RAFT.git
cd RAFT

# Create a new conda environment with Python 3.11
conda create -n raft -y python=3.11

# Activate the created environment
conda activate raft

# Install the required Python packages
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
pip install opencv-python
pip install matplotlib
pip install tensorboard
pip install scikit-image
  1. download pretrained checkpoints.
./download_models.sh
  1. run the original pytorch model on test images.
python demo.py --model=models/raft-things.pth --path=demo-frames
  1. check pytorch model inference performance
cd ..
python ../gen_video2imgs.py
python infer.py

  • input size: 288 x 512, iters: 20

  • original

    • 249 iterations time: 26.1436 [sec]
    • Average FPS: 9.52 [fps]
    • Average inference time: 104.99 [msec]

  • wrapper for onnx/trt

    • 249 iterations time: 41.2404 [sec]
    • Average FPS: 6.04 [fps]
    • Average inference time: 165.62 [msec]

How to Run (TensorRT)

  1. generate onnx file
python onnx_export.py
// a file '.onnx' will be generated in onnx directory.
  1. build tensorrt model and run
conda activate trte
python onnx2trt.py
// a file '.engine' will be generated in engine directory.
  • input size: 288 x 512, iters: 20
  • 249 iterations time: 11.3397 [sec]
  • Average FPS: 21.96 [fps]
  • Average inference time: 45.54 [msec]

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