[ICLR2021oral] Rethinking Architecture Selection in Differentiable NAS

Related tags

Deep Learningdarts-pt
Overview

DARTS-PT

Code accompanying the paper ICLR'2021: Rethinking Architecture Selection in Differentiable NAS
Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, Cho-Jui Hsieh

Requirements

Python >= 3.7
PyTorch >= 1.5
tensorboard == 2.0.1
gpustat

Experiments on NAS-Bench-201

Dataset preparation

Download the NAS-Bench-201-v1_0-e61699.pth and save it under ./data folder.

Install NasBench201 via pip:

pip install nas-bench-201

Running DARTS-PT on NAS-Bench-201

Supernet training

The ckpts and logs will be saved to ./experiments/nasbench201/search-{script_name}-{seed}/. For example, the ckpt dir would be ./experiments/nasbench201/search-darts-201-1/ for the command below.

bash darts-201.sh

Architecture selection (projection)

The projection script loads ckpts from experiments/nasbench201/{resume_expid}

bash darts-proj-201.sh --resume_epoch 100 --resume_expid search-darts-201-1

Fix-alpha version (blank-pt):

bash blank-201.sh
bash blank-proj-201.sh --resume_expid search-blank-201-1

Experiments on S1-S4

Supernet training

The ckpts and logs will be saved to ./experiments/sota/{dataset}/search-{script_name}-{space_id}-{seed}/. For example, ./experiments/sota/cifar10/search-darts-sota-s3-1/ (script: darts-sota, space: s3, seed: 1).

bash darts-sota.sh --space [s1/s2/s3/s4] --dataset [cifar10/cifar100/svhn]

Architecture selection (projection)

bash darts-proj-sota.sh --space [s1/s2/s3/s4] --dataset [cifar10/cifar100/svhn] --resume_expid search-darts-sota-[s1/s2/s3/s4]-2

Fix-alpha version (blank-pt):

bash blank-sota.sh --space [s1/s2/s3/s4] --dataset [cifar10/cifar100/svhn]
bash blank-proj-201.sh --space [s1/s2/s3/s4] --dataset [cifar10/cifar100/svhn] --resume_expid search-blank-sota-[s1/s2/s3/s4]-2

Evaluation

bash eval.sh --arch [genotype_name]
bash eval-c100.sh --arch [genotype_name]
bash eval-svhn.sh --arch [genotype_name]

Expeirments on DARTS Space

Supernet training

bash darts-sota.sh

Archtiecture selection (projection)

bash darts-proj-sota.sh --resume_expid search-blank-sota-s5-2

Fix-alpha version (blank-pt)

bash blank-sota.sh
bash blank-proj-201.sh --resume_expid search-blank-sota-s5-2

Evaluation

bash eval.sh --arch [genotype_name]

Citation

@inproceedings{
  ruochenwang2021dartspt,
  title={{Rethinking Architecture Selection in Differentiable NAS},
  author={Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, Cho-Jui Hsieh},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2021}
}
Owner
Ruochen Wang
MSCS at UCLA. AutoML, GNN, Machine Learning
Ruochen Wang
Code for NeurIPS2021 submission "A Surrogate Objective Framework for Prediction+Programming with Soft Constraints"

This repository is the code for NeurIPS 2021 submission "A Surrogate Objective Framework for Prediction+Programming with Soft Constraints". Edit 2021/

10 Dec 20, 2022
Implementations for the ICLR-2021 paper: SEED: Self-supervised Distillation For Visual Representation.

Implementations for the ICLR-2021 paper: SEED: Self-supervised Distillation For Visual Representation.

Jacob 27 Oct 23, 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
PyTorch Connectomics: segmentation toolbox for EM connectomics

Introduction The field of connectomics aims to reconstruct the wiring diagram of the brain by mapping the neural connections at the level of individua

Zudi Lin 132 Dec 26, 2022
PRIME: A Few Primitives Can Boost Robustness to Common Corruptions

PRIME: A Few Primitives Can Boost Robustness to Common Corruptions This is the official repository of PRIME, the data agumentation method introduced i

Apostolos Modas 34 Oct 30, 2022
The implementation our EMNLP 2021 paper "Enhanced Language Representation with Label Knowledge for Span Extraction".

LEAR The implementation our EMNLP 2021 paper "Enhanced Language Representation with Label Knowledge for Span Extraction". **The code is in the "master

杨攀 93 Jan 07, 2023
Official Pytorch implementation of paper "Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Images"

Reverse_Engineering_GMs Official Pytorch implementation of paper "Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Gener

100 Dec 18, 2022
Learning Saliency Propagation for Semi-supervised Instance Segmentation

Learning Saliency Propagation for Semi-supervised Instance Segmentation PyTorch Implementation This repository contains: the PyTorch implementation of

Berkeley DeepDrive 68 Oct 18, 2022
This is the official implementation of "One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval".

CORA This is the official implementation of the following paper: Akari Asai, Xinyan Yu, Jungo Kasai and Hannaneh Hajishirzi. One Question Answering Mo

Akari Asai 59 Dec 28, 2022
A Python reference implementation of the CF data model

cfdm A Python reference implementation of the CF data model. References Compliance with FAIR principles Documentation https://ncas-cms.github.io/cfdm

NCAS CMS 25 Dec 13, 2022
Circuit Training: An open-source framework for generating chip floor plans with distributed deep reinforcement learning

Circuit Training: An open-source framework for generating chip floor plans with distributed deep reinforcement learning. Circuit Training is an open-s

Google Research 479 Dec 25, 2022
Open CV - Convert a picture to look like a cartoon sketch in python

Use the video https://www.youtube.com/watch?v=k7cVPGpnels for initial learning.

Sammith S Bharadwaj 3 Jan 29, 2022
A Lightweight Hyperparameter Optimization Tool 🚀

Lightweight Hyperparameter Optimization 🚀 The mle-hyperopt package provides a simple and intuitive API for hyperparameter optimization of your Machin

136 Jan 08, 2023
Styled text-to-drawing synthesis method. Featured at the 2021 NeurIPS Workshop on Machine Learning for Creativity and Design

Styled text-to-drawing synthesis method. Featured at the 2021 NeurIPS Workshop on Machine Learning for Creativity and Design

Peter Schaldenbrand 247 Dec 23, 2022
A booklet on machine learning systems design with exercises

Machine Learning Systems Design Read this booklet here. This booklet covers four main steps of designing a machine learning system: Project setup Data

Chip Huyen 7.6k Jan 08, 2023
Kaggle | 9th place (part of) solution for the Bristol-Myers Squibb – Molecular Translation challenge

Part of the 9th place solution for the Bristol-Myers Squibb – Molecular Translation challenge translating images containing chemical structures into I

Erdene-Ochir Tuguldur 22 Nov 30, 2022
Lightweight plotting to the terminal. 4x resolution via Unicode.

Uniplot Lightweight plotting to the terminal. 4x resolution via Unicode. When working with production data science code it can be handy to have plotti

Olav Stetter 203 Dec 29, 2022
Towards Debiasing NLU Models from Unknown Biases

Towards Debiasing NLU Models from Unknown Biases Abstract: NLU models often exploit biased features to achieve high dataset-specific performance witho

Ubiquitous Knowledge Processing Lab 22 Jun 14, 2022
Model-based Reinforcement Learning Improves Autonomous Racing Performance

Racing Dreamer: Model-based versus Model-free Deep Reinforcement Learning for Autonomous Racing Cars In this work, we propose to learn a racing contro

Cyber Physical Systems - TU Wien 38 Dec 06, 2022
Recovering Brain Structure Network Using Functional Connectivity

Recovering-Brain-Structure-Network-Using-Functional-Connectivity Framework: Papers: This repository provides a PyTorch implementation of the models ad

5 Nov 30, 2022