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
	  .
	  .
	  .
	- 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
TJU Deep Learning & Neural Network

Deep_Learning & Neural_Network_Lab 实验环境 Python 3.9 Anaconda3(官网下载或清华镜像都行) PyTorch 1.10.1(安装代码如下) conda install pytorch torchvision torchaudio cudatool

St3ve Lee 1 Jan 19, 2022
[ICCV 2021] Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain

Amplitude-Phase Recombination (ICCV'21) Official PyTorch implementation of "Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neur

Guangyao Chen 53 Oct 05, 2022
style mixing for animation face

An implementation of StyleGAN on Animation dataset. Install git clone https://github.com/MorvanZhou/anime-StyleGAN cd anime-StyleGAN pip install -r re

Morvan 46 Nov 30, 2022
Pytorch implementation of the paper Improving Text-to-Image Synthesis Using Contrastive Learning

T2I_CL This is the official Pytorch implementation of the paper Improving Text-to-Image Synthesis Using Contrastive Learning Requirements Linux Python

42 Dec 31, 2022
IAUnet: Global Context-Aware Feature Learning for Person Re-Identification

IAUnet This repository contains the code for the paper: IAUnet: Global Context-Aware Feature Learning for Person Re-Identification Ruibing Hou, Bingpe

30 Jul 14, 2022
HyDiff: Hybrid Differential Software Analysis

HyDiff: Hybrid Differential Software Analysis This repository provides the tool and the evaluation subjects for the paper HyDiff: Hybrid Differential

Yannic Noller 22 Oct 20, 2022
Human head pose estimation using Keras over TensorFlow.

RealHePoNet: a robust single-stage ConvNet for head pose estimation in the wild.

Rafael Berral Soler 71 Jan 05, 2023
这是一个unet-pytorch的源码，可以训练自己的模型

Unet：U-Net: Convolutional Networks for Biomedical Image Segmentation目标检测模型在Pytorch当中的实现 目录 性能情况 Performance 所需环境 Environment 注意事项 Attention 文件下载 Downl

Bubbliiiing 567 Jan 05, 2023
Multimodal commodity image retrieval 多模态商品图像检索

Multimodal commodity image retrieval 多模态商品图像检索 Not finished yet... introduce explain:The specific description of the project and the product image dat

hongjie 8 Nov 25, 2022
Open source single image super-resolution toolbox containing various functionality for training a diverse number of state-of-the-art super-resolution models. Also acts as the companion code for the IEEE signal processing letters paper titled 'Improving Super-Resolution Performance using Meta-Attention Layers’.

Deep-FIR Codebase - Super Resolution Meta Attention Networks About This repository contains the main coding framework accompanying our work on meta-at

17 Jun 17, 2022
Repository for benchmarking graph neural networks

Benchmarking Graph Neural Networks Updates Nov 2, 2020 Project based on DGL 0.4.2. See the relevant dependencies defined in the environment yml files

NTU Graph Deep Learning Lab 2k Jan 03, 2023
Simple helper library to convert a collection of numpy data to tfrecord, and build a tensorflow dataset from the tfrecord.

numpy2tfrecord Simple helper library to convert a collection of numpy data to tfrecord, and build a tensorflow dataset from the tfrecord. Installation

Ryo Yonetani 2 Jan 16, 2022
Simple reference implementation of GraphSAGE.

Reference PyTorch GraphSAGE Implementation Author: William L. Hamilton Basic reference PyTorch implementation of GraphSAGE. This reference implementat

William L Hamilton 861 Jan 06, 2023
📝 Wrapper library for text generation / language models at char and word level with RNN in TensorFlow

tensorlm Generate Shakespeare poems with 4 lines of code. Installation tensorlm is written in / for Python 3.4+ and TensorFlow 1.1+ pip3 install tenso

Kilian Batzner 63 May 22, 2021
Clockwork Variational Autoencoder

Clockwork Variational Autoencoders (CW-VAE) Vaibhav Saxena, Jimmy Ba, Danijar Hafner If you find this code useful, please reference in your paper: @ar

Vaibhav Saxena 35 Nov 06, 2022
FMA: A Dataset For Music Analysis

FMA: A Dataset For Music Analysis Michaël Defferrard, Kirell Benzi, Pierre Vandergheynst, Xavier Bresson. International Society for Music Information

Michaël Defferrard 1.8k Dec 29, 2022
Code for the submitted paper Surrogate-based cross-correlation for particle image velocimetry

Surrogate-based cross-correlation (SBCC) This repository contains code for the submitted paper Surrogate-based cross-correlation for particle image ve

5 Jun 30, 2022
[CVPR 2021 Oral] ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis

ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis [arxiv|pdf|v

Yinan He 78 Dec 22, 2022
Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation.

Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation. It was introduced in Wright, Logan G. & Onodera, Tatsuhiro et al. (2021)1 to train Physical Neural Networ

McMahon Lab 230 Jan 05, 2023
Self-supervised learning algorithms provide a way to train Deep Neural Networks in an unsupervised way using contrastive losses

Self-supervised learning Self-supervised learning algorithms provide a way to train Deep Neural Networks in an unsupervised way using contrastive loss

Arijit Das 2 Mar 26, 2022