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.

This demo creates a single-layer MLP with TensorRT >= 7.2.x version support.

Helper Files

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

mlp.wts : Converted weight file, can be generated from pytorchx/mlp, for mlp, it looks like:

2
linear.weight 1 3fff7e32
linear.bias 1 3c138a5a

(you can create mlp.wts and copy this content into it directly)

TensorRT C++ API

see HERE

TensorRT Python API

1. Generate mlp.wts (from pytorchx or create on your own)

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

3. Run

   bashCopy

   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