Open source implementation of AceNAS: Learning to Rank Ace Neural Architectures with Weak Supervision of Weight Sharing

Related tags

Deep Learningnasautoml
Overview

AceNAS

This repo is the experiment code of AceNAS, and is not considered as an official release. We are working on integrating AceNAS as a built-in strategy provided in NNI.

Data Preparation

  1. Download our prepared data from Google Drive. The directory should look like this:
data
├── checkpoints
│   ├── acenas-m1.pth.tar
│   ├── acenas-m2.pth.tar
│   └── acenas-m3.pth.tar
├── gcn
│   ├── nasbench101_gt_all.pkl
│   ├── nasbench201cifar10_gt_all.pkl
│   ├── nasbench201_gt_all.pkl
│   ├── nasbench201imagenet_gt_all.pkl
│   ├── nds_amoeba_gt_all.pkl
│   ├── nds_amoebaim_gt_all.pkl
│   ├── nds_dartsfixwd_gt_all.pkl
│   ├── nds_darts_gt_all.pkl
│   ├── nds_dartsim_gt_all.pkl
│   ├── nds_enasfixwd_gt_all.pkl
│   ├── nds_enas_gt_all.pkl
│   ├── nds_enasim_gt_all.pkl
│   ├── nds_nasnet_gt_all.pkl
│   ├── nds_nasnetim_gt_all.pkl
│   ├── nds_pnasfixwd_gt_all.pkl
│   ├── nds_pnas_gt_all.pkl
│   ├── nds_pnasim_gt_all.pkl
│   ├── nds_supernet_evaluate_all_test1_amoeba.json
│   ├── nds_supernet_evaluate_all_test1_dartsfixwd.json
│   ├── nds_supernet_evaluate_all_test1_darts.json
│   ├── nds_supernet_evaluate_all_test1_enasfixwd.json
│   ├── nds_supernet_evaluate_all_test1_enas.json
│   ├── nds_supernet_evaluate_all_test1_nasnet.json
│   ├── nds_supernet_evaluate_all_test1_pnasfixwd.json
│   ├── nds_supernet_evaluate_all_test1_pnas.json
│   ├── supernet_evaluate_all_test1_nasbench101.json
│   ├── supernet_evaluate_all_test1_nasbench201cifar10.json
│   ├── supernet_evaluate_all_test1_nasbench201imagenet.json
│   └── supernet_evaluate_all_test1_nasbench201.json
├── nb201
│   ├── split-cifar100.txt
│   ├── split-cifar10-valid.txt
│   └── split-imagenet-16-120.txt
├── proxyless
│   ├── imagenet
│   │   ├── augment_files.txt
│   │   ├── test_files.txt
│   │   ├── train_files.txt
│   │   └── val_files.txt
│   ├── proxyless-84ms-train.csv
│   ├── proxyless-ws-results.csv
│   └── tunas-proxylessnas-search.csv
└── tunas
    ├── imagenet_valid_split_filenames.txt
    ├── random_architectures.csv
    └── searched_architectures.csv
  1. (Required for benchmark experiments) Download CIFAR-10, CIFAR-100, ImageNet-16-120 dataset and also put them under data.
data
├── cifar10
│   └── cifar-10-batches-py
│       ├── batches.meta
│       ├── data_batch_1
│       ├── data_batch_2
│       ├── data_batch_3
│       ├── data_batch_4
│       ├── data_batch_5
│       ├── readme.html
│       └── test_batch
├── cifar100
│   └── cifar-100-python
│       ├── meta
│       ├── test
│       └── train
└── imagenet16
    ├── train_data_batch_1
    ├── train_data_batch_10
    ├── train_data_batch_2
    ├── train_data_batch_3
    ├── train_data_batch_4
    ├── train_data_batch_5
    ├── train_data_batch_6
    ├── train_data_batch_7
    ├── train_data_batch_8
    ├── train_data_batch_9
    └── val_data

  1. (Required for ImageNet experiments) Prepare ImageNet. You can put it anywhere.

  2. (Optional) Copy tunas (https://github.com/google-research/google-research/tree/master/tunas) to a folder named tunas.

Evaluate pre-trained models.

We provide 3 checkpoints obtained from 3 different runs in data/checkpoints. Please evaluate them via the following command.

python -m tools.standalone.imagenet_eval acenas-m1 /path/to/your/imagenet
python -m tools.standalone.imagenet_eval acenas-m2 /path/to/your/imagenet
python -m tools.standalone.imagenet_eval acenas-m3 /path/to/your/imagenet

Train supernet

python -m tools.supernet.nasbench101 experiments/supernet/nasbench101.yml
python -m tools.supernet.nasbench201 experiments/supernet/nasbench201.yml
python -m tools.supernet.nds experiments/supernet/darts.yml
python -m tools.supernet.proxylessnas experiments/supernet/proxylessnas.yml

Please refer to experiments/supernet folder for more configurations.

Benchmark experiments

We've already provided weight-sharing results from supernet so that you do not have to train you own. The provided files can be found in json files located under data/gcn.

# pretrain
python -m gcn.benchmarks.pretrain data/gcn/supernet_evaluate_all_test1_${SEARCHSPACE}.json data/gcn/${SEARCHSPACE}_gt_all.pkl --metric_keys top1 flops params
# finetune
python -m gcn.benchmarks.train --use_train_samples --budget {budget} --test_dataset data/gcn/${SEARCHSPACE}_gt_all.pkl --iteration 5 \
    --loss lambdarank --gnn_type gcn --early_stop_patience 50 --learning_rate 0.005 --opt_type adam --wd 5e-4 --epochs 300 --bs 20 \
    --resume /path/to/previous/output.pt

Running baselines

BRP-NAS:

# pretrain
python -m gcn.benchmarks.pretrain data/gcn/supernet_evaluate_all_test1_${SEARCHSPACE}.json data/gcn/${SEARCHSPACE}_gt_all.pkl --metric_keys flops
# finetune
python -m gcn.benchmarks.train --use_train_samples --budget ${BUDGET} --test_dataset data/gcn/${SEARCHSPACE}_gt_all.pkl --iteration 5 \
    --loss brp --gnn_type brp --early_stop_patience 35 --learning_rate 0.00035 \
    --opt_type adamw --wd 5e-4 --epochs 250 --bs 64 --resume /path/to/previous/output.pt

Vanilla:

python -m gcn.benchmarks.train --use_train_samples --budget ${BUDGET} --test_dataset data/gcn/${SEARCHSPACE}_gt_all.pkl --iteration 1 \
    --loss mse --gnn_type vanilla --n_hidden 144 --learning_rate 2e-4 --opt_type adam --wd 1e-3 --epochs 300 --bs 10

ProxylessNAS search space

Train GCN

python -m gcn.proxyless.pretrain --metric_keys ws_accuracy simulated_pixel1_time_ms flops params
python -m gcn.proxyless.train --loss lambdarank --early_stop_patience 50 --learning_rate 0.002 --opt_type adam --wd 5e-4 --epochs 300 --bs 20 \
    --resume /path/to/previous/output.pth

Train final model

Validation set:

python -m torch.distributed.launch --nproc_per_node=16 \
    --use_env --module \
    tools.standalone.imagenet_train \
    --output "$OUTPUT_DIR" "$ARCH" "$IMAGENET_DIR" \
    -b 256 --lr 2.64 --warmup-lr 0.1 \
    --warmup-epochs 5 --epochs 90 --sched cosine --num-classes 1000 \
    --opt rmsproptf --opt-eps 1. --weight-decay 4e-5 -j 8 --dist-bn reduce \
    --bn-momentum 0.01 --bn-eps 0.001 --drop 0. --no-held-out-val

Test set:

python -m torch.distributed.launch --nproc_per_node=16 \
    --use_env --module \
    tools.standalone.imagenet_train \
    --output "$OUTPUT_DIR" "$ARCH" "$IMAGENET_DIR" \
    -b 256 --lr 2.64 --warmup-lr 0.1 \
    --warmup-epochs 9 --epochs 360 --sched cosine --num-classes 1000 \
    --opt rmsproptf --opt-eps 1. --weight-decay 4e-5 -j 8 --dist-bn reduce \
    --bn-momentum 0.01 --bn-eps 0.001 --drop 0.15
Owner
Yuge Zhang
Yuge Zhang
GitHub Repository
Code for our SIGCOMM'21 paper "Network Planning with Deep Reinforcement Learning".

0. Introduction This repository contains the source code for our SIGCOMM'21 paper "Network Planning with Deep Reinforcement Learning". Notes The netwo

NetX Group 68 Nov 24, 2022
An adaptive hierarchical energy management strategy for hybrid electric vehicles

An adaptive hierarchical energy management strategy This project contains the source code of an adaptive hierarchical EMS combining heuristic equivale

19 Dec 13, 2022
Optimizing synthesizer parameters using gradient approximation

Optimizing synthesizer parameters using gradient approximation NASH 2021 Hackathon! These are some experiments I conducted during NASH 2021, the Neura

Jordie Shier 10 Feb 10, 2022
Using multidimensional LSTM neural networks to create a forecast for Bitcoin price

Multidimensional LSTM BitCoin Time Series Using multidimensional LSTM neural networks to create a forecast for Bitcoin price. For notes around this co

Jakob Aungiers 318 Dec 14, 2022
Deploy optimized transformer based models on Nvidia Triton server

🤗 Hugging Face Transformer submillisecond inference 🤯 and deployment on Nvidia Triton server Yes, you can perfom inference with transformer based mo

Lefebvre Sarrut Services 1.2k Jan 05, 2023
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
Allele-specific pipeline for unbiased read mapping(WIP), QTL discovery(WIP), and allelic-imbalance analysis

WASP2 (Currently in pre-development): Allele-specific pipeline for unbiased read mapping(WIP), QTL discovery(WIP), and allelic-imbalance analysis Requ

McVicker Lab 2 Aug 11, 2022
PyTorch Implementation for AAAI'21 "Do Response Selection Models Really Know What's Next? Utterance Manipulation Strategies for Multi-turn Response Selection"

UMS for Multi-turn Response Selection Implements the model described in the following paper Do Response Selection Models Really Know What's Next? Utte

Taesun Whang 47 Nov 22, 2022
Weakly Supervised Learning of Rigid 3D Scene Flow

Weakly Supervised Learning of Rigid 3D Scene Flow This repository provides code and data to train and evaluate a weakly supervised method for rigid 3D

Zan Gojcic 124 Dec 27, 2022
This is the official code for the paper "Learning with Nested Scene Modeling and Cooperative Architecture Search for Low-Light Vision"

RUAS This is the official code for the paper "Learning with Nested Scene Modeling and Cooperative Architecture Search for Low-Light Vision" A prelimin

Vision & Optimization Group (VOG) 2 May 05, 2022
Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark

This dataset is a large-scale dataset for moving object detection and tracking in satellite videos, which consists of 40 satellite videos captured by Jilin-1 satellite platforms.

Qingyong 87 Dec 22, 2022
A repository with exploration into using transformers to predict DNA ↔ transcription factor binding

Transcription Factor binding predictions with Attention and Transformers A repository with exploration into using transformers to predict DNA ↔ transc

Phil Wang 62 Dec 20, 2022
Official repo for QHack—the quantum machine learning hackathon

Note: This repository has been frozen while we consider the submissions for the QHack Open Hackathon. We hope you enjoyed the event! Welcome to QHack,

Xanadu 118 Jan 05, 2023
This code provides various models combining dilated convolutions with residual networks

Overview This code provides various models combining dilated convolutions with residual networks. Our models can achieve better performance with less

Fisher Yu 1.1k Dec 30, 2022
RealTime Emotion Recognizer for Machine Learning Study Jam's demo

Emotion recognizer Table of contents Clone project Dataset Install dependencies Main program Demo 1. Clone project git clone https://github.com/GDSC20

Google Developer Student Club - UIT 1 Oct 05, 2021
DeepLearning Anomalies Detection with Bluetooth Sensor Data

Final Year Project. Constructing models to create offline anomalies detection using Travel Time Data collected from Bluetooth sensors along the route.

1 Jan 10, 2022
Good Classification Measures and How to Find Them

Good Classification Measures and How to Find Them This repository contains supplementary materials for the paper "Good Classification Measures and How

Yandex Research 7 Nov 13, 2022
Serve TensorFlow ML models with TF-Serving and then create a Streamlit UI to use them

TensorFlow Serving + Streamlit! ✨ 🖼️ Serve TensorFlow ML models with TF-Serving and then create a Streamlit UI to use them! This is a pretty simple S

Álvaro Bartolomé 18 Jan 07, 2023
Official PyTorch implementation of U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation

U-GAT-IT — Official PyTorch Implementation : Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Imag

Hyeonwoo Kang 2.4k Jan 04, 2023
Fairness Metrics: All you need to know

Fairness Metrics: All you need to know Testing machine learning software for ethical bias has become a pressing current concern. Recent research has p

Anonymous2020 1 Jan 17, 2022