A python/pytorch utility library

Related tags

Deep Learningpyutility
Overview

pyutils

A python/pytorch utility library

MIT License

News

  • v0.0.1 available. Feedbacks are highly welcomed!

Installation

git clone https://github.com/JeremieMelo/pyutility.git
cd pyutility
pip3 install --editable .

Features

  • Support pytorch training utility and datasets.

TODOs

  • Support lr_scheduler
  • Support trainer

Dependencies

  • Python >= 3.6
  • PyTorch >= 1.8.0
  • Tensorflow >= 2.5.0
  • Others are listed in requirements.txt

Files

File Description
datasets/ Defines different datasets and builder
optimizer/ Defines different optimizers
activation.py Activation functions
compute.py functions related to computing
config.py Hierarchical yaml configuration file parser
distribution_sampler.py Sample from customized distributions
general.py Common helper functions
initializer.py Initialization methods for PyTorch Parameters
loss.py Loss functions for PyTorch model training
quantize.py Quantization functions
torch_train.py Helper functions for torch training
typing.py Defines common types

Contact

Jiaqi Gu ([email protected])

Owner
Jiaqi Gu
PhD Student at UT Austin
Jiaqi Gu
GitHub Repository
ECLARE: Extreme Classification with Label Graph Correlations

ECLARE ECLARE: Extreme Classification with Label Graph Correlations @InProceedings{Mittal21b, author = "Mittal, A. and Sachdeva, N. and Agrawal

Extreme Classification 35 Nov 06, 2022
RepVGG: Making VGG-style ConvNets Great Again

This repository is the code that needs to be submitted for OpenMMLab Algorithm Ecological Challenge，the paper is RepVGG: Making VGG-style ConvNets Great Again

Ty Feng 62 May 21, 2022
Fuzzy Overclustering (FOC)

Fuzzy Overclustering (FOC) In real-world datasets, we need consistent annotations between annotators to give a certain ground-truth label. However, in

2 Nov 08, 2022
Spatial Transformer Nets in TensorFlow/ TensorLayer

MOVED TO HERE Spatial Transformer Networks Spatial Transformer Networks (STN) is a dynamic mechanism that produces transformations of input images (or

Hao 36 Nov 23, 2022
Code for the paper "TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks"

TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks This is a Python3 / Pytorch implementation of TadGAN paper. The associated

Arun 92 Dec 03, 2022
使用yolov5训练自己数据集(详细过程)并通过flask部署

使用yolov5训练自己的数据集（详细过程）并通过flask部署 依赖库 torch torchvision numpy opencv-python lxml tqdm flask pillow tensorboard matplotlib pycocotools Windows，请使用 pycoc

HB.com 19 Dec 28, 2022
Implementation for our ICCV 2021 paper: Dual-Camera Super-Resolution with Aligned Attention Modules

DCSR: Dual Camera Super-Resolution Implementation for our ICCV 2021 oral paper: Dual-Camera Super-Resolution with Aligned Attention Modules paper | pr

Tengfei Wang 110 Dec 20, 2022
Repository for the paper titled: "When is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfer"

When is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfer This repository contains code for our paper titled "When is BERT M

Princeton Natural Language Processing 9 Dec 23, 2022
Implementation detail for paper "Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet"

Multi-level-colonoscopy-malignant-tissue-detection-with-adversarial-CAC-UNet Implementation detail for our paper "Multi-level colonoscopy malignant ti

CVSM Group - email: <a href=[email protected]"> 84 Nov 22, 2022
PyTorch Implementation of NCSOFT's FastPitchFormant: Source-filter based Decomposed Modeling for Speech Synthesis

FastPitchFormant - PyTorch Implementation PyTorch Implementation of FastPitchFormant: Source-filter based Decomposed Modeling for Speech Synthesis. Qu

Keon Lee 63 Jan 02, 2023
Implementation of Pooling by Sliced-Wasserstein Embedding (NeurIPS 2021)

PSWE: Pooling by Sliced-Wasserstein Embedding (NeurIPS 2021) PSWE is a permutation-invariant feature aggregation/pooling method based on sliced-Wasser

Navid Naderializadeh 3 May 06, 2022
Official code for our CVPR '22 paper "Dataset Distillation by Matching Training Trajectories"

Dataset Distillation by Matching Training Trajectories Project Page | Paper This repo contains code for training expert trajectories and distilling sy

George Cazenavette 256 Jan 05, 2023
AFLNet: A Greybox Fuzzer for Network Protocols

AFLNet: A Greybox Fuzzer for Network Protocols AFLNet is a greybox fuzzer for protocol implementations. Unlike existing protocol fuzzers, it takes a m

626 Jan 06, 2023
Implementation of FSGNN

FSGNN Implementation of FSGNN. For more details, please refer to our paper Experiments were conducted with following setup: Pytorch: 1.6.0 Python: 3.8

19 Dec 05, 2022
[NeurIPS 2019] Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss

Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, Tengyu Ma This is the offi

Kaidi Cao 528 Jan 01, 2023
the code used for the preprint Embedding-based Instance Segmentation of Microscopy Images.

EmbedSeg Introduction This repository hosts the version of the code used for the preprint Embedding-based Instance Segmentation of Microscopy Images.

JugLab 88 Dec 25, 2022
📚 Papermill is a tool for parameterizing, executing, and analyzing Jupyter Notebooks.

papermill is a tool for parameterizing, executing, and analyzing Jupyter Notebooks. Papermill lets you: parameterize notebooks execute notebooks This

nteract 5.1k Jan 03, 2023
Sequence modeling benchmarks and temporal convolutional networks

Sequence Modeling Benchmarks and Temporal Convolutional Networks (TCN) This repository contains the experiments done in the work An Empirical Evaluati

CMU Locus Lab 3.5k Jan 01, 2023
Python Library for learning (Structure and Parameter) and inference (Statistical and Causal) in Bayesian Networks.

pgmpy pgmpy is a python library for working with Probabilistic Graphical Models. Documentation and list of algorithms supported is at our official sit

pgmpy 2.2k Jan 03, 2023
CRISCE: Automatically Generating Critical Driving Scenarios From Car Accident Sketches

CRISCE: Automatically Generating Critical Driving Scenarios From Car Accident Sketches This document describes how to install and use CRISCE (CRItical

Chair of Software Engineering II, Uni Passau 2 Feb 09, 2022