PSENet

preprocessing + inference + postprocessing = 30ms with fp32 on Tesla P40. The original Tensorflow implementation is tensorflow_PSENet. A TensorRT Python api implementation is TensorRT-Python-PSENet.

Key Features

• Generating .wts from Tensorflow.
• Dynamic batch and dynamic shape input.
• Object-Oriented Programming.
• Practice with C++ 11.

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How to Run

• 1. generate .wts

  Download pretrained model from https://github.com/liuheng92/tensorflow_PSENet and put model.ckpt.* to model dir. Add a file model/checkpoint with content

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  model_checkpoint_path: "model.ckpt"
  all_model_checkpoint_paths: "model.ckpt"
  

  Then run

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  python gen_tf_wts.py
  

  which will gengerate a psenet.wts.

• 2. cmake and make
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  mkdir build
  cd build
  cmake ..
  make
  

• 3. build engine and run detection
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  cp ../psenet.wts ./
  cp ../test.jpg ./
  ./psenet -s  // serialize model to plan file
  ./psenet -d  // deserialize plan file and run inference
  

Known Issues

None

Todo

• use ExponentialMovingAverage weight.