yolo_standard_libray/tensorrtx-master/mlp

MLP

MLP is the most basic net in this tensorrtx project for starters. You can learn the basic procedures of building TensorRT app from the provided APIs. The process of building a TensorRT engine explained in the chart below.

Helper Files

logging.h : A logger file for using NVIDIA TRT API (mostly same for all models)

mlp.wts : Converted weight file (simple file, you can open and check it)

TensorRT C++ API

// 1. generate mlp.wts from https://github.com/wang-xinyu/pytorchx/tree/master/mlp -- or use the given .wts file

// 2. put mlp.wts into tensorrtx/mlp (if using the generated weights)

// 3. build and run

    cd tensorrtx/mlp

    mkdir build

    cd build

    cmake ..

    make

    sudo ./mlp -s   // serialize model to plan file i.e. 'mlp.engine'

    sudo ./mlp -d   // deserialize plan file and run inference

TensorRT Python API

# 1. Generate mlp.wts from https://github.com/wang-xinyu/pytorchx/tree/master/mlp -- or use the given .wts file

# 2. Put mlp.wts into tensorrtx/mlp (if using the generated weights)

# 3. Install Python dependencies (tensorrt/pycuda/numpy)

# 4. Run 
    
    cd tensorrtx/mlp
    
    python mlp.py -s   # serialize model to plan file, i.e. 'mlp.engine'
    
    python mlp.py -d   # deserialize plan file and run inference

Note

It also supports the latest CUDA-11.4 and TensorRT-8.2.x