This repo is official PyTorch implementation of MobileHumanPose: Toward real-time 3D human pose estimation in mobile devices(CVPRW 2021).

Overview

Github Code of "MobileHumanPose: Toward real-time 3D human pose estimation in mobile devices"

Introduction

This repo is official PyTorch implementation of MobileHumanPose: Toward real-time 3D human pose estimation in mobile devices(CVPRW 2021).

Dependencies

This code is tested under Ubuntu 16.04, CUDA 11.2 environment with two NVIDIA RTX or V100 GPUs.

Python 3.6.5 version with virtualenv is used for development.

Directory

Root

The ${ROOT} is described as below.

${ROOT}
|-- data
|-- demo
|-- common
|-- main
|-- tool
|-- vis
`-- output
  • data contains data loading codes and soft links to images and annotations directories.
  • demo contains demo codes.
  • common contains kernel codes for 3d multi-person pose estimation system. Also custom backbone is implemented in this repo
  • main contains high-level codes for training or testing the network.
  • tool contains data pre-processing codes. You don't have to run this code. I provide pre-processed data below.
  • vis contains scripts for 3d visualization.
  • output contains log, trained models, visualized outputs, and test result.

Data

You need to follow directory structure of the data as below.

${POSE_ROOT}
|-- data
|   |-- Human36M
|   |   |-- bbox_root
|   |   |   |-- bbox_root_human36m_output.json
|   |   |-- images
|   |   |-- annotations
|   |-- MPII
|   |   |-- images
|   |   |-- annotations
|   |-- MSCOCO
|   |   |-- bbox_root
|   |   |   |-- bbox_root_coco_output.json
|   |   |-- images
|   |   |   |-- train2017
|   |   |   |-- val2017
|   |   |-- annotations
|   |-- MuCo
|   |   |-- data
|   |   |   |-- augmented_set
|   |   |   |-- unaugmented_set
|   |   |   |-- MuCo-3DHP.json
|   |-- MuPoTS
|   |   |-- bbox_root
|   |   |   |-- bbox_mupots_output.json
|   |   |-- data
|   |   |   |-- MultiPersonTestSet
|   |   |   |-- MuPoTS-3D.json

Output

You need to follow the directory structure of the output folder as below.

${POSE_ROOT}
|-- output
|-- |-- log
|-- |-- model_dump
|-- |-- result
`-- |-- vis
  • Creating output folder as soft link form is recommended instead of folder form because it would take large storage capacity.
  • log folder contains training log file.
  • model_dump folder contains saved checkpoints for each epoch.
  • result folder contains final estimation files generated in the testing stage.
  • vis folder contains visualized results.

3D visualization

  • Run $DB_NAME_img_name.py to get image file names in .txt format.
  • Place your test result files (preds_2d_kpt_$DB_NAME.mat, preds_3d_kpt_$DB_NAME.mat) in single or multi folder.
  • Run draw_3Dpose_$DB_NAME.m

Running 3DMPPE_POSENET

Requirements

cd main
pip install -r requirements.txt

Setup Training

  • In the main/config.py, you can change settings of the model including dataset to use, network backbone, and input size and so on.

Train

In the main folder, run

python train.py --gpu 0-1 --backbone LPSKI

to train the network on the GPU 0,1.

If you want to continue experiment, run

python train.py --gpu 0-1 --backbone LPSKI --continue

--gpu 0,1 can be used instead of --gpu 0-1.

Test

Place trained model at the output/model_dump/.

In the main folder, run

python test.py --gpu 0-1 --test_epoch 20-21 --backbone LPSKI

to test the network on the GPU 0,1 with 20th and 21th epoch trained model. --gpu 0,1 can be used instead of --gpu 0-1. For the backbone you can either choose BACKBONE_DICT = { 'LPRES':LpNetResConcat, 'LPSKI':LpNetSkiConcat, 'LPWO':LpNetWoConcat }

Human3.6M dataset using protocol 1

For the evaluation, you can run test.py or there are evaluation codes in Human36M.

Human3.6M dataset using protocol 2

For the evaluation, you can run test.py or there are evaluation codes in Human36M.

MuPoTS-3D dataset

For the evaluation, run test.py. After that, move data/MuPoTS/mpii_mupots_multiperson_eval.m in data/MuPoTS/data. Also, move the test result files (preds_2d_kpt_mupots.mat and preds_3d_kpt_mupots.mat) in data/MuPoTS/data. Then run mpii_mupots_multiperson_eval.m with your evaluation mode arguments.

TFLite inference

For the inference in mobile devices we also tested in mobile devices which converting PyTorch implementation through onnx and finally serving into TFlite. Official demo app is available in here

Reference

What this repo cames from: Training section and is based on following paper and github

@InProceedings{Moon_2019_ICCV_3DMPPE,
  author = {Moon, Gyeongsik and Chang, Juyong and Lee, Kyoung Mu},
  title = {Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image},
  booktitle = {The IEEE Conference on International Conference on Computer Vision (ICCV)},
  year = {2019}
}
Owner
Choi Sang Bum
Deep Learning will be implemented inside Mobile [email protected]
Choi Sang Bum
ServiceX Transformer that converts flat ROOT ntuples into columnwise data

ServiceX_Uproot_Transformer ServiceX Transformer that converts flat ROOT ntuples into columnwise data Usage You can invoke the transformer from the co

Vis 0 Jan 20, 2022
ECCV2020 paper: Fashion Captioning: Towards Generating Accurate Descriptions with Semantic Rewards. Code and Data.

This repo contains some of the codes for the following paper Fashion Captioning: Towards Generating Accurate Descriptions with Semantic Rewards. Code

Xuewen Yang 56 Dec 08, 2022
Unofficial Implementation of MLP-Mixer, gMLP, resMLP, Vision Permutator, S2MLPv2, RaftMLP, ConvMLP, ConvMixer in Jittor and PyTorch.

Unofficial Implementation of MLP-Mixer, gMLP, resMLP, Vision Permutator, S2MLPv2, RaftMLP, ConvMLP, ConvMixer in Jittor and PyTorch! Now, Rearrange and Reduce in einops.layers.jittor are support!!

130 Jan 08, 2023
Pure python implementation reverse-mode automatic differentiation

MiniGrad A minimal implementation of reverse-mode automatic differentiation (a.k.a. autograd / backpropagation) in pure Python. Inspired by Andrej Kar

Kenny Song 76 Sep 12, 2022
[NeurIPS 2021] The PyTorch implementation of paper "Self-Supervised Learning Disentangled Group Representation as Feature"

IP-IRM [NeurIPS 2021] The PyTorch implementation of paper "Self-Supervised Learning Disentangled Group Representation as Feature". Codes will be relea

Wang Tan 67 Dec 24, 2022
DiffWave is a fast, high-quality neural vocoder and waveform synthesizer.

DiffWave DiffWave is a fast, high-quality neural vocoder and waveform synthesizer. It starts with Gaussian noise and converts it into speech via itera

LMNT 498 Jan 03, 2023
nfelo: a power ranking, prediction, and betting model for the NFL

nfelo nfelo is a power ranking, prediction, and betting model for the NFL. Nfelo take's 538's Elo framework and further adapts it for the NFL, hence t

6 Nov 22, 2022
Ludwig is a toolbox that allows to train and evaluate deep learning models without the need to write code.

Translated in 🇰🇷 Korean/ Ludwig is a toolbox that allows users to train and test deep learning models without the need to write code. It is built on

Ludwig 8.7k Dec 31, 2022
Improving the robustness and performance of biomedical NLP models through adversarial training

RobustBioNLP Improving the robustness and performance of biomedical NLP models through adversarial training In this repository you can find suppliment

Milad Moradi 3 Sep 20, 2022
Code for AA-RMVSNet: Adaptive Aggregation Recurrent Multi-view Stereo Network (ICCV 2021).

AA-RMVSNet Code for AA-RMVSNet: Adaptive Aggregation Recurrent Multi-view Stereo Network (ICCV 2021) in PyTorch. paper link: arXiv | CVF Change Log Ju

Qingtian Zhu 97 Dec 30, 2022
Machine Learning Time-Series Platform

cesium: Open-Source Platform for Time Series Inference Summary cesium is an open source library that allows users to: extract features from raw time s

632 Dec 26, 2022
A computational optimization project towards the goal of gerrymandering the results of a hypothetical election in the UK.

A computational optimization project towards the goal of gerrymandering the results of a hypothetical election in the UK.

Emma 1 Jan 18, 2022
Implementation of association rules mining algorithms (Apriori|FPGrowth) using python.

Association Rules Mining Using Python Implementation of association rules mining algorithms (Apriori|FPGrowth) using python. As a part of hw1 code in

Pre 2 Nov 10, 2021
CCNet: Criss-Cross Attention for Semantic Segmentation (TPAMI 2020 & ICCV 2019).

CCNet: Criss-Cross Attention for Semantic Segmentation Paper Links: Our most recent TPAMI version with improvements and extensions (Earlier ICCV versi

Zilong Huang 1.3k Dec 27, 2022
OBBDetection: an oriented object detection toolbox modified from MMdetection

OBBDetection note: If you have questions or good suggestions, feel free to propose issues and contact me. introduction OBBDetection is an oriented obj

MIXIAOXIN_HO 3 Nov 11, 2022
Source code for our EMNLP'21 paper 《Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning》

Child-Tuning Source code for EMNLP 2021 Long paper: Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning. 1. Environ

46 Dec 12, 2022
Implementation of the federated dual coordinate descent (FedDCD) method.

FedDCD.jl Implementation of the federated dual coordinate descent (FedDCD) method. Installation To install, just call Pkg.add("https://github.com/Zhen

Zhenan Fan 6 Sep 21, 2022
PyTorch implementation of Self-supervised Contrastive Regularization for DG (SelfReg)

SelfReg PyTorch official implementation of Self-supervised Contrastive Regularization for Domain Generalization (SelfReg, https://arxiv.org/abs/2104.0

64 Dec 16, 2022
PRIN/SPRIN: On Extracting Point-wise Rotation Invariant Features

PRIN/SPRIN: On Extracting Point-wise Rotation Invariant Features Overview This repository is the Pytorch implementation of PRIN/SPRIN: On Extracting P

Yang You 17 Mar 02, 2022
Pytorch implementation of forward and inverse Haar Wavelets 2D

Pytorch implementation of forward and inverse Haar Wavelets 2D

Sergei Belousov 9 Oct 30, 2022