Deep Inertial Prediction (DIPr)

Related tags

Deep Learningdipr
Overview

Deep Inertial Prediction

For more information and context related to this repo, please refer to our website.

Getting Started (non Docker)

Note: you will need to have pytorch installed (tested with 1.8 and higher)

python3 -m venv <venv_path>
source <venv_path>/bin/activate

git clone https://github.com/arcturus-industries/dipr.git && cd dipr
pip3 install -e .
python3 dipr/evaluate.py --challenge_folder <data_path>

Getting Started (with Docker)

You will need docker and realpath commands to be installed

git clone https://github.com/arcturus-industries/dipr.git && cd dipr
# on x86_64 systems
./build-and-run.sh <data_path>
# on arm64 systems (like mac M1)
./build-and-run-aarch64.sh <data_path>

M1 Mac note: You can use either the X86_64 container or the arm64 container. If you use the x86_64 container, you may see "Could not initialize NNPACK! Reason: Unsupported hardware." This is only a warning. It will however take a long time to run (about 30 minutes or longer after the docker build finishes)

Package Content

  • dataset.py - sample API to read the challenge hdf5 dataset format
  • cnn_backend.py - a file with CNN inference backends (currenly only TorchScript is supported). If you plan to work on a DL inference framework other than TorchScript, implement it there
  • noise_utils.py - a file with noise calibration and parameters, you may adjust them to generate your own noise levels
  • imu_fallback.py - a sample implmentation of ImuFallback with CNN velocity measurements
  • utils.py - auxiliary tools
  • evaluate.py - sample test script that runs ImuFallback on available datasets and outputs Mean Absolute Velocity metric

Running sample evaluation script

python3 evaluate.py --challenge_folder <data_path>

or for the docker versions

# on x86_64 systems
./build-and-run.sh <data_path>
# on arm64 systems (like mac M1)
./build-and-run-aarch64.sh <data_path>

It will output something like:

python3.9 evaluate.py -df shared
Dataset OpenVR_2021-09-02_17-40-34-synthetic, segments durations [7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0 ] sec
Processing datasets: 100%|██████████| 1/1 [05:04<00:00, 304.92s/files]
all_vel_mae_cnn 2.12cm/s
all_vel_mae_fallback 9.73cm/s
all_pose_mae_fallback 15.51cm

Which mean it found OpenVR_2021-09-02_17-40-34-synthetic test dataset, and executed ImuFallback on 13 segments of duration 7 seconds, and estimated over them averaged Mean Absolute Velocity Error as 9.73cm/s

It also outputs image with tracking plots to <challenge_folder_root>/_results/<datasetname>.png. There are plots for IMU only tracking, ImuFallback + CNN traking and ground truth

Challenge folder Content

train_synthetic - a folder with train datasets, available after sign-up https://dipr.ai/sign-up

test_synthetic - a folder where evaluation script looks for test datasets (we share only one example dataset)

_results - a folder where evaluation script stores some results

pretrained - an example CNN model we ship

Known Issues

Installing dependencies natively on Apple Silicon may fail with the following:

> pip3 install -e .
...
    error: Command "clang -Wno-unused-result -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -iwithsysroot/System/Library/Frameworks/System.framework/PrivateHeaders -iwithsysroot/Applications/Xcode.app/Contents/Developer/Library/Frameworks/Python3.framework/Versions/3.8/Headers -arch arm64 -arch x86_64 -Werror=implicit-function-declaration -ftrapping-math -DNPY_INTERNAL_BUILD=1 -DHAVE_NPY_CONFIG_H=1 -D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -DNO_ATLAS_INFO=3 -DHAVE_CBLAS -Ibuild/src.macosx-10.14-x86_64-3.8/numpy/core/src/common -Ibuild/src.macosx-10.14-x86_64-3.8/numpy/core/src/umath -Inumpy/core/include -Ibuild/src.macosx-10.14-x86_64-3.8/numpy/core/include/numpy -Ibuild/src.macosx-10.14-x86_64-3.8/numpy/distutils/include -Inumpy/core/src/common -Inumpy/core/src -Inumpy/core -Inumpy/core/src/npymath -Inumpy/core/src/multiarray -Inumpy/core/src/umath -Inumpy/core/src/npysort -Inumpy/core/src/_simd -I<venv_path>/include -I/Applications/Xcode.app/Contents/Developer/Library/Frameworks/Python3.framework/Versions/3.8/include/python3.8 -Ibuild/src.macosx-10.14-x86_64-3.8/numpy/core/src/common -Ibuild/src.macosx-10.14-x86_64-3.8/numpy/core/src/npymath -c numpy/core/src/multiarray/dragon4.c -o build/temp.macosx-10.14-x86_64-3.8/numpy/core/src/multiarray/dragon4.o -MMD -MF build/temp.macosx-10.14-x86_64-3.8/numpy/core/src/multiarray/dragon4.o.d -msse3 -I/System/Library/Frameworks/vecLib.framework/Headers" failed with exit status 1
    ----------------------------------------
    ERROR: Failed building wheel for numpy

Workaround: use the Docker instructions

License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

CC BY-NC-SA 4.0

Owner
Arcturus Industries
Arcturus Industries
Pytorch code for our paper Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains)

Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains (ICLR'2022) This is the Pytorch code for our paper Beyond ImageNet

Alibaba-AAIG 37 Nov 23, 2022
(under submission) Bayesian Integration of a Generative Prior for Image Restoration

BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration Authors: Majed El Helou, and Sabine Süsstrunk {Note: p

Majed El Helou 22 Dec 17, 2022
Pytorch implementation of Integrating Tree Path in Transformer for Code Representation

This is an official Pytorch implementation of the approaches proposed in: Han Peng, Ge Li, Wenhan Wang, Yunfei Zhao, Zhi Jin “Integrating Tree Path in

Han Peng 16 Dec 23, 2022
Classify the disease status of a plant given an image of a passion fruit

Passion Fruit Disease Detection I tried to create an accurate machine learning models capable of localizing and identifying multiple Passion Fruits in

3 Nov 09, 2021
Tackling data scarcity in Speech Translation using zero-shot multilingual Machine Translation techniques

Tackling data scarcity in Speech Translation using zero-shot multilingual Machine Translation techniques This repository is derived from the NMTGMinor

Tu Anh Dinh 1 Sep 07, 2022
Official Repo for Ground-aware Monocular 3D Object Detection for Autonomous Driving

Visual 3D Detection Package: This repo aims to provide flexible and reproducible visual 3D detection on KITTI dataset. We expect scripts starting from

Yuxuan Liu 305 Dec 19, 2022
Matplotlib Image labeller for classifying images

mpl-image-labeller Use Matplotlib to label images for classification. Works anywhere Matplotlib does - from the notebook to a standalone gui! For more

Ian Hunt-Isaak 5 Sep 24, 2022
A novel framework to automatically learn high-quality scanning of non-planar, complex anisotropic appearance.

appearance-scanner About This repository is an implementation of the neural network proposed in Free-form Scanning of Non-planar Appearance with Neura

Xiaohe Ma 14 Oct 18, 2022
Fbone (Flask bone) is a Flask (Python microframework) starter/template/bootstrap/boilerplate application.

Fbone (Flask bone) is a Flask (Python microframework) starter/template/bootstrap/boilerplate application.

Wilson 1.7k Dec 30, 2022
Code for the Lovász-Softmax loss (CVPR 2018)

The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks Maxim Berman, Amal Ranne

Maxim Berman 1.3k Jan 04, 2023
nextPARS, a novel Illumina-based implementation of in-vitro parallel probing of RNA structures.

nextPARS, a novel Illumina-based implementation of in-vitro parallel probing of RNA structures. Here you will find the scripts necessary to produce th

Jesse Willis 0 Jan 20, 2022
An implementation of RetinaNet in PyTorch.

RetinaNet An implementation of RetinaNet in PyTorch. Installation Training COCO 2017 Pascal VOC Custom Dataset Evaluation Todo Credits Installation In

Conner Vercellino 297 Jan 04, 2023
Cycle Consistent Adversarial Domain Adaptation (CyCADA)

Cycle Consistent Adversarial Domain Adaptation (CyCADA) A pytorch implementation of CyCADA. If you use this code in your research please consider citi

Hyunwoo Ko 2 Jan 10, 2022
TensorFlow 101: Introduction to Deep Learning for Python Within TensorFlow

TensorFlow 101: Introduction to Deep Learning I have worked all my life in Machine Learning, and I've never seen one algorithm knock over its benchmar

Sefik Ilkin Serengil 896 Jan 04, 2023
DRLib:A concise deep reinforcement learning library, integrating HER and PER for almost off policy RL algos.

DRLib:A concise deep reinforcement learning library, integrating HER and PER for almost off policy RL algos A concise deep reinforcement learning libr

329 Jan 03, 2023
Run Effective Large Batch Contrastive Learning on Limited Memory GPU

Gradient Cache Gradient Cache is a simple technique for unlimitedly scaling contrastive learning batch far beyond GPU memory constraint. This means tr

Luyu Gao 198 Dec 29, 2022
Start-to-finish tutorial for interactive music co-creation in PyTorch and Tensorflow.js

Start-to-finish tutorial for interactive music co-creation in PyTorch and Tensorflow.js

Chris Donahue 98 Dec 14, 2022
Text mining project; Using distilBERT to predict authors in the classification task authorship attribution.

DistilBERT-Text-mining-authorship-attribution Dataset used: https://www.kaggle.com/azimulh/tweets-data-for-authorship-attribution-modelling/version/2

1 Jan 13, 2022
Python wrapper to access the amazon selling partner API

PYTHON-AMAZON-SP-API Amazon Selling-Partner API If you have questions, please join on slack Contributions very welcome! Installation pip install pytho

Michael Primke 330 Jan 06, 2023
Python project to take sound as input and output as RGB + Brightness values suitable for DMX

sound-to-light Python project to take sound as input and output as RGB + Brightness values suitable for DMX Current goals: Get one pixel working: Vary

Bobby Cox 1 Nov 17, 2021