November 25, 2021 Pytest

Pytest plugin that let you automate actions and assertions with test metrics reporting executing plain YAML files

pytest-play pytest-play is a codeless, generic, pluggable and extensible automation tool, not necessarily test automation only, based on the fantastic pytest test framework that let you define and execute YAML files containing scripts or test scenarios through actions and assertions that can be implemented and managed even by non technical users: automation (not necessarily test automation). You can build a set of actions on a single file (e.g, call a JSON based API endpoint, perform an action if a condition […]

November 25, 2021 Compiler

Nuitka : a Python compiler written in Python

This document is the recommended first read if you are interested in using Nuitka, understand its use cases, check what you can expect, license, requirements, credits, etc. Nuitka is the Python compiler. It is written in Python. It is a seamless replacement or extension to the Python interpreter and compiles every construct that CPython 2.6, 2.7, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9 have, when itself run with that Python version. It then executes uncompiled code and compiled code together […]

November 25, 2021 Baseline

SCU OlympicsRunning Baseline

Environment check details in Jidi Competition RLChina2021智能体竞赛做出的修改：奖励重塑：修改了环境，重新设置了奖励的分配，使得奖励组成不只有零和博弈，还有探索环境的奖励。算法微调：修改了官方PPO算法的actor loss部分，增加了对actor分布熵的约束，未来计划加入RND、ICM等辅助部件。 Dependency conda create -n olympics python=3.8.5 conda activate olympics pip install -r requirements.txt Run a game python olympics/main.py Train a baseline agent python rl_trainer/main.py By default parameters, the total reward of training is shown below. GitHub View Github

November 25, 2021 augmentation

Code for text augmentation method leveraging large-scale language models

Code for our paper GPT3Mix and conducting classification experiments using GPT-3 prompt-based data augmentation. Getting Started Installing Packages The main depedencies can be installed via pip install -r requirements.txt. Usage The main code is run through main.py. Check out –help for full list of commands. The code will automatically use the first GPU device, if detected. A typical command to run BERT-base 10 times on the 1% subsample set of the SST-2 dataset and computing the averageof all run is […]

November 25, 2021 Graph

PatchGraph: In-hand tactile tracking with learned surface normals

This repository contains the source code of the paper PatchGraph: In-hand tactile tracking with learned surface normals. Create a virtual python environment using Anaconda: conda create -n inhand python=3.7 conda activate inhand Install the inhandpy python package. From the base directory execute: cd inhandpy/ pip install -e . In inhandpy, download datasets, pre-trained models and other local resources by running: ./download_local_files.sh Stage 1: Tactile images to 3D point clouds To run the example:

November 25, 2021 Web

Nick Craig-Wood’s public website

This directory tree is used to build all the different docs forNick Craig-Wood’s website. The content here is (c) Nick Craig-Wood – if you’d like to use itelsewhere then please ask first: [email protected] See the content directory for the pages in markdown format. Use hugo to build the website. Changing the layout If you want to change the layout then the main files to edit are layouts/_default/baseof.html for the HTML template chrome/navbar.html for the navbar chrome/menu.html for the menu Running […]

November 25, 2021 PyTorch

Implementation of Pix2Seq in PyTorch

Unofficial implementation of the Pix2Seq paper Different from the paper image input size 1280 bin size 1280 LambdaLR scheduler used instead of LinearLR resnet50 instead of resnet50d or resnet101 etc. Download first the coco2017 dataset and put it under the dataset folder. – dataset – annotations – instances_train2017.json – instances_val2017.json – train2017 – 000000000000.jpg – … – val2017 python train.py –config configs/pix2seq.yaml GitHub View Github

November 25, 2021 PyTorch

Sharpness-aware Quantization for Deep Neural Networks

This is the official repository for our paper: Sharpness-aware Quantization for Deep Neural Networks by Jing Liu, Jianfei Cai, and Bohan Zhuang. Recent Update 2021.11.24: We release the source code of SAQ. Setup the environments Clone the repository locally: git clone https://github.com/zhuang-group/SAQ Install pytorch 1.8+, tensorboard and prettytable conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch pip install tensorboard pip install prettytable Data preparation

November 25, 2021 Challenge

L3DAS22 challenge supporting API

This repository supports the L3DAS22 IEEE ICASSP Grand Challenge and it is aimed at downloading the dataset, pre-processing the sound files and the metadata, training and evaluating the baseline models and validating the final results.We provide easy-to-use instruction to produce the results included in our paper.Moreover, we extensively commented our code for easy customization. For further information please refer to the challenge website and to the challenge documentation. Installation Our code is based on Python 3.7. To install all required […]

November 25, 2021 Document

PhD document for navlab

The project contains the relative software documents which I developped or used during my PhD period. It includes: FLVIS. A stereo-inertial pose estimation system. RW_SLAM. A tightly-coupled system fusing RGB-D camera and wheel odometer. ESKF. An ESKF algorithm to fuse IMU and GNSS data. 3D reconstruction demo based on pcl and Open3D. Qualisys manual. The steps to set the IP of qualisys, calibrate and define a body frame, and get the groudtruth using ROS. Evaluation tools. The usages of EVO […]

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