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
Motion Reconstruction Code and Data for Skills from Videos (SFV)

Motion Reconstruction Code and Data for Skills from Videos (SFV) This repo contains the data and the code for motion reconstruction component of the S

268 Dec 01, 2022
PyTorch implementation of MLP-Mixer

PyTorch implementation of MLP-Mixer MLP-Mixer: an all-MLP architecture composed of alternate token-mixing and channel-mixing operations. The token-mix

Duo Li 33 Nov 27, 2022
Parasite: a tool allowing you to compress and decompress files, to reduce their size

🦠 Parasite 🦠 Parasite is a tool written in Python3 allowing you to "compress" any file, reducing its size. ⭐ Features ⭐ + Fast + Good optimization,

Billy 30 Nov 25, 2022
NNR conformation conditional and global probabilities estimation and analysis in peptides or proteins fragments

NNR and global probabilities estimation and analysis in peptides or protein fragments This module calculates global and NNR conformation dependent pro

0 Jul 15, 2021
Multi-Person Extreme Motion Prediction

Multi-Person Extreme Motion Prediction Implementation for paper Wen Guo, Xiaoyu Bie, Xavier Alameda-Pineda, Francesc Moreno-Noguer, Multi-Person Extre

GUO-W 38 Nov 15, 2022
This package is for running the semantic SLAM algorithm using extracted planar surfaces from the received detection

Semantic SLAM This package can perform optimization of pose estimated from VO/VIO methods which tend to drift over time. It uses planar surfaces extra

Hriday Bavle 125 Dec 02, 2022
Filtering variational quantum algorithms for combinatorial optimization

Current gate-based quantum computers have the potential to provide a computational advantage if algorithms use quantum hardware efficiently.

1 Feb 09, 2022
COLMAP - Structure-from-Motion and Multi-View Stereo

COLMAP About COLMAP is a general-purpose Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipeline with a graphical and command-line interface.

4.7k Jan 07, 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
Official PyTorch implementation of "Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics".

Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics This repository is the official PyTorch implementation of "Physics-aware Differ

USC-Melady 46 Nov 20, 2022
Evaluating different engineering tricks that make RL work

Reinforcement Learning Tricks, Index This repository contains the code for the paper "Distilling Reinforcement Learning Tricks for Video Games". Short

Anssi 15 Dec 26, 2022
Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset

Semantic Segmentation on MIT ADE20K dataset in PyTorch This is a PyTorch implementation of semantic segmentation models on MIT ADE20K scene parsing da

MIT CSAIL Computer Vision 4.5k Jan 08, 2023
DAN: Unfolding the Alternating Optimization for Blind Super Resolution

DAN-Basd-on-Openmmlab DAN: Unfolding the Alternating Optimization for Blind Super Resolution We reproduce DAN via mmediting based on open-sourced code

AlexZou 72 Dec 13, 2022
make ASCII Art by Deep Learning

DeepAA This is convolutional neural networks generating ASCII art. This repository is under construction. This work is accepted by NIPS 2017 Workshop,

OsciiArt 1.4k Dec 28, 2022
Deep Reinforced Attention Regression for Partial Sketch Based Image Retrieval.

DARP-SBIR Intro This repository contains the source code implementation for ICDM submission paper Deep Reinforced Attention Regression for Partial Ske

2 Jan 09, 2022
Pytorch-Swin-Unet-V2 - a modified version of Swin Unet based on Swin Transfomer V2

Swin Unet V2 Swin Unet V2 is a modified version of Swin Unet arxiv based on Swin

Chenxu Peng 26 Dec 03, 2022
Pytorch implementation of Nueral Style transfer

Nueral Style Transfer Pytorch implementation of Nueral style transfer algorithm , it is used to apply artistic styles to content images . Content is t

Abhinav 9 Oct 15, 2022
Spatial-Temporal Transformer for Dynamic Scene Graph Generation, ICCV2021

Spatial-Temporal Transformer for Dynamic Scene Graph Generation Pytorch Implementation of our paper Spatial-Temporal Transformer for Dynamic Scene Gra

Yuren Cong 119 Jan 01, 2023
Cluttered MNIST Dataset

Cluttered MNIST Dataset A setup script will download MNIST and produce mnist/*.t7 files: luajit download_mnist.lua Example usage: local mnist_clutter

DeepMind 50 Jul 12, 2022
Revealing and Protecting Labels in Distributed Training

Revealing and Protecting Labels in Distributed Training

Google Interns 0 Nov 09, 2022