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
A clean and robust Pytorch implementation of PPO on continuous action space.

PPO-Continuous-Pytorch I found the current implementation of PPO on continuous action space is whether somewhat complicated or not stable. And this is

XinJingHao 56 Dec 16, 2022
Implementation of Google Brain's WaveGrad high-fidelity vocoder

WaveGrad Implementation (PyTorch) of Google Brain's high-fidelity WaveGrad vocoder (paper). First implementation on GitHub with high-quality generatio

Ivan Vovk 363 Dec 27, 2022
Customised to detect objects automatically by a given model file(onnx)

LabelImg LabelImg is a graphical image annotation tool. It is written in Python and uses Qt for its graphical interface. Annotations are saved as XML

Heeone Lee 1 Jun 07, 2022
SwinTrack: A Simple and Strong Baseline for Transformer Tracking

SwinTrack This is the official repo for SwinTrack. A Simple and Strong Baseline Prerequisites Environment conda (recommended) conda create -y -n SwinT

LitingLin 196 Jan 04, 2023
Pytorch implementation for "Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion" (NeurIPS 2021)

Density-aware Chamfer Distance This repository contains the official PyTorch implementation of our paper: Density-aware Chamfer Distance as a Comprehe

Tong WU 93 Dec 15, 2022
This repo contains source code and materials for the TEmporally COherent GAN SIGGRAPH project.

TecoGAN This repository contains source code and materials for the TecoGAN project, i.e. code for a TEmporally COherent GAN for video super-resolution

Nils Thuerey 5.2k Jan 02, 2023
repro_eval is a collection of measures to evaluate the reproducibility/replicability of system-oriented IR experiments

repro_eval repro_eval is a collection of measures to evaluate the reproducibility/replicability of system-oriented IR experiments. The measures were d

IR Group at Technische Hochschule Köln 9 May 25, 2022
A PyTorch implementation of "Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning", IJCAI-21

MERIT A PyTorch implementation of our IJCAI-21 paper Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning. Depen

Graph Analysis & Deep Learning Laboratory, GRAND 32 Jan 02, 2023
HDMapNet: A Local Semantic Map Learning and Evaluation Framework

HDMapNet_devkit Devkit for HDMapNet. HDMapNet: A Local Semantic Map Learning and Evaluation Framework Qi Li, Yue Wang, Yilun Wang, Hang Zhao [Paper] [

Tsinghua MARS Lab 421 Jan 04, 2023
Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN

Overview PyTorch 0.4.1 | Python 3.6.5 Annotated implementations with comparative introductions for minimax, non-saturating, wasserstein, wasserstein g

Shayne O'Brien 471 Dec 16, 2022
Intent parsing and slot filling in PyTorch with seq2seq + attention

PyTorch Seq2Seq Intent Parsing Reframing intent parsing as a human - machine translation task. Work in progress successor to torch-seq2seq-intent-pars

Sean Robertson 160 Jan 07, 2023
An implementation demo of the ICLR 2021 paper Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks in PyTorch.

Neural Attention Distillation This is an implementation demo of the ICLR 2021 paper Neural Attention Distillation: Erasing Backdoor Triggers from Deep

Yige-Li 84 Jan 04, 2023
Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?

Adversrial Machine Learning Benchmarks This code belongs to the papers: Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness? Det

Adversarial Machine Learning 9 Nov 27, 2022
UMT is a unified and flexible framework which can handle different input modality combinations, and output video moment retrieval and/or highlight detection results.

Unified Multi-modal Transformers This repository maintains the official implementation of the paper UMT: Unified Multi-modal Transformers for Joint Vi

Applied Research Center (ARC), Tencent PCG 84 Jan 04, 2023
Transformers are Graph Neural Networks!

🚀 Gated Graph Transformers Gated Graph Transformers for graph-level property prediction, i.e. graph classification and regression. Associated article

Chaitanya Joshi 46 Jun 30, 2022
A PyTorch implementation of Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks

SVHNClassifier-PyTorch A PyTorch implementation of Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks If

Potter Hsu 182 Jan 03, 2023
The world's largest toxicity dataset.

The Toxicity Dataset by Surge AI Saving the internet is fun. Combing through thousands of online comments to build a toxicity dataset isn't. That's wh

Surge AI 134 Dec 19, 2022
DSTC10 Track 2 - Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations

DSTC10 Track 2 - Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations This repository contains the data, scripts and baseline co

Alexa 51 Dec 17, 2022
Rotation-Only Bundle Adjustment

ROBA: Rotation-Only Bundle Adjustment Paper, Video, Poster, Presentation, Supplementary Material In this repository, we provide the implementation of

Seong 51 Nov 29, 2022
The Curious Layperson: Fine-Grained Image Recognition without Expert Labels (BMVC 2021)

The Curious Layperson: Fine-Grained Image Recognition without Expert Labels Subhabrata Choudhury, Iro Laina, Christian Rupprecht, Andrea Vedaldi Code

Subhabrata Choudhury 18 Dec 27, 2022