One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking

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Deep Learning nas
Overview

One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking

This is an official implementation for NEAS presented in CVPR 2021.

Environment Setup

To set up the enviroment you can easily run the following command:

git clone https://github.com/researchmm/NEAS.git
cd NEAS
conda create -n NEAS python=3.6
conda activate NEAS
sh ./install.sh
# (required) install apex to accelerate the training, a little bit faster than pytorch DistributedDataParallel
cd lib
git clone https://github.com/NVIDIA/apex.git
python ./apex/setup.py install --cpp_ext --cuda_ext

Data Preparation

You need to first download the ImageNet-2012 to the folder ./data/imagenet and move the validation set to the subfolder ./data/imagenet/val. To move the validation set, you cloud use the following script: https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh

The directory structure is the standard layout as following.

/path/to/imagenet/
  train/
    class1/
      img1.jpeg
    class2/
      img2.jpeg
  val/
    class1/
      img3.jpeg
    class/2
      img4.jpeg

Model Zoo

For evaluation, we provide the checkpoints of our models in Google Drive.

After downloading the models, you can do the evaluation following the description in Quick Start - Test).

Model download links:

Model FLOPs Top-1 Acc. % Top-5 Acc. % Link
NEAS-S 314M 77.9 93.9 Google Drive
NEAS-M 472M 79.5 94.6 Google Drive
NEAS-L 574M 80.0 94.8 Google Drive

Quick Start

We provide test code of NEAS as follows.

Test

To test our trained models, you need to put the downloaded model in PATH_TO_CKP (the default path is ./CKP in root directory.). After that you need to specify the model path in the corresponding config file by changing the intitial-checkpoint argument in ./configs/subnets/[SELECTED_MODEL_SIZE].yaml.

Then, you could use the following command to test the model.

sh ./tools/distributed_test.sh ./configs/subnets/[SELECTED_MODEL_SIZE].yaml

The test result will be saved in ./experiments. You can also add [--output OUTPUT_PATH] in ./tools/distribution_test.sh to specify a path for it as well.

To Do List

  • Test code
  • Retrain code
  • Search code

BibTex

@article{NEAS,
  title={One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking},
  author={Chen, Minghao and Peng, Houwen and Fu, Jianlong and Ling, Haibin},
  journal={arXiv preprint arXiv:2104.00597},
  year={2021}
}
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Comments
  • Is there any plan on when to release the train and search code?

    Is there any plan on when to release the train and search code?

    Hi awsome authors of NEAS, I noticed that you have to-do plan for train and search code, I am just curious do you have any plan on when to release those code? I am very interested in your paper, and want to have a try with the search code!

    opened by HeYDwane3 1
Owner
Multimedia Research
Multimedia Research at Microsoft Research Asia
Multimedia Research
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