PyTorch 1.0 inference in C++ on Windows10 platforms

Related tags

Deep Learningpytorch-inference
Overview

Serving PyTorch Models in C++ on Windows10 platforms

Dynamic graph

How to use

Prepare Data

examples/data/train/

	- 0
	- 1
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	  .
	  .
	- n

examples/data/test/

	- 0
	- 1
	  .
	  .
	  .
	- n

Train Model

cd examples && python train.py

Transform Model

cd examples && python transform_model.py

Test Model

cd makefile/pytorch
mkdir build && cd build && cmake -A x64 ..

or

mkdir build && cd build && cmake -G "Visual Studio 15 2017 Win64" ..

set Command Arguments -> ..\..\..\examples\checkpoint ..\..\..\examples\images
set Environment -> path=%path%;../../../thirdparty/libtorch/lib;../../../thirdparty/opencv/build/x64/vc15/bin;

Test CUDA Softmax

cd makefile/cuda
mkdir build && cd build && cmake -A x64 ..

or

mkdir build && cd build && cmake -G "Visual Studio 15 2017 Win64" ..

Inference onnx model

cd makefile/tensorRT/classification
mkdir build && cd build && cmake -A x64 ..

or

mkdir build && cd build && cmake -G "Visual Studio 15 2017 Win64" ..
set Environment -> path=%path%;../../../../thirdparty/TensorRT/lib;

Inference caffe model for faster-rcnn

cd makefile/tensorRT/detection
mkdir build && cd build && cmake -A x64 ..

or

mkdir build && cd build && cmake -G "Visual Studio 15 2017 Win64" ..
set Environment -> path=%path%;../../../../thirdparty/TensorRT/lib;

download VGG16_faster_rcnn_final.caffemodel

Thirdparty

thirdparty/
	- libtorch  
	- opencv 
	- CUDA
	- TensorRT

download thirdparty from here.

Docker

docker pull zccyman/deepframe
nvidia-docker run -it --name=mydocker zccyman/deepframe /bin/bash
cd workspace && git clone https://github.com/zccyman/pytorch-inference.git

Environment

  • Windows10
  • VS2017
  • CMake3.13
  • CUDA10.0
  • CUDNN7.3
  • Pyton3.5
  • ONNX1.1.2
  • TensorRT5.0.1
  • Pytorch1.0
  • Libtorch
  • OpenCV4.0.1

Todo List

  • train and transform pytorch model

  • multi-batch inference pytorch model in C++

  • cpu and gpu softmax

  • transform pytorch model to ONNX model, and inference onnx model using tensorRT

  • inference caffe model for faster-rcnn using tensorRT

  • build classification network

  • compress pytorch model

  • object detection pytorch inference using C++ on Window platforms

Notes

  • "torch.jit.trace" doesn’t support nn.DataParallel so far.

  • TensorRT doesn’t supports opset 7 above so far, but pyTorch ONNX exporter seems to export opset 9.

Acknowledgement

Owner
Henson
Henson
GitHub Repository
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