TensorRT_Parser_Python

TensorRT engine convert (from Onnx engine) and inference in Python.

The Onnx model can be run on any system with difference platform (Operating system/ CUDA / CuDNN / TensorRT) but take a lot of time to parse. Convert the Onnx model to TensorRT model (.trt) help you save a lot of parsing time (4-10 min) but can only run on fixed system you've built.

I. Prerequiste.

• CUDA/CUDNN/TensorRT Installation Guide

• Clone.

  git clone https://github.com/CuteBoiz/TensorRT_Parser_Python
  cd TensorRT_Parser_Python

II. Export Onnx engine to TensorRT engine.

python3 main.py export --weight (--saved_name) (--max_batch_size) (--max_workspace_size) (--fp16) (--input_tensor_name) (--dim) 

Arguments Details

Arguments Details	Type	Default	Note
--weight	str	required	Path to onnx engine.
--saved_name	str	'weight_path'.trt	Saved name of trt engine
--fp16	store_true	false	Use FP16 fast mode (x2 inference time).
--max_batch_size	int	1	Inference max batchsize.
--max_workspace_size	int	1300	Max workspace size(MB)
--input_tensor_name	str	None	Input tensor name (dynamic shape input only).
--dim	int_array	None	Input tensor dimension (dynamic shape input only).

Note: The only GPUs with full-rate FP16 Fast mode performance are Tesla P100, Quadro GP100, and Jetson TX1/TX2.

Note: To get input tensor name/shape of a DL engine: Use Netron.

Examples

• Export Onnx engine to TensorRT engine.

  python3 main.py export --weight ../2020_0421_0925.onnx python3 main.py export --weight ../2020_0421_0925.onnx --saved_name model.trt --max_batch_size 10 --fp16

• Export Onnx engine with Dynamic shape input (batchsize x 3 x 416 x416).

   --input_tensor_name tensorName --dim dims1(,dims2,dims3) (Does not include batchsize dims)
  python3 main.py export --ds --weight ../2020_0421_0925.onnx --input_tensor_name input_1 --dim 128 128 3
  python3 main.py export --ds --weight ../Keras.onnx --input_tensor_name input:0 --dim 3 640 640 --fp16

III. Inference.

python3 main.py infer --weight --data (--batch_size) (--softmax)

Arguments Details

Arguments Details	Type	Default	Note
--weight	str	required	Path to trt engine.
--data	str	required	Path to inference data.
--batch_size	int	1	Inference batchsize.
--softmax	store_true	false	Add softmax to output layer.

Examples

python3 main.py infer --weight ../2020_0421_0925.trt --data ../Dataset/Train/
python3 main.py infer --weight ../2020_0421_0925.trt --data ../Dataset/Train/ --batch_size 6 --softmax

TO-DO

• Batchsize inference.
• Add missing params (max_workspace_size, gpu).
• Multiple inputs support.
• Multiple output support.
• Multi-type of inference data (video/folder/image).