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Neural Predictor Guided Evolution for Neural Architecture Search

This repository contains code for paper NPENAS: Neural Predictor Guided Evolution for Neural Architecture Search.

If you use the code please cite our paper.

@article{Wei2020NPENASNP,
    title={NPENAS: Neural Predictor Guided Evolution for Neural Architecture Search},
    author={Chen Wei and Chuang Niu and Yiping Tang and Ji-min Liang},
    journal={ArXiv},
    year={2020},
    volume={abs/2003.12857}
}

Prerequisites

• Python 3.7
• Pytorch 1.3
• Tensorflow 1.14.0
• ptflops pip install --upgrade git+https://github.com/sovrasov/flops-counter.pytorch.git
• torch-scatter pip install torch-scatter==1.4.0
• torch-sparse pip install torch-sparse==0.4.3
• torch-cluster pip install torch-cluster==1.4.5
• torch-spline-conv pip install torch-spline-conv==1.1.1

Searching Environment

• Ubuntu 18.04
• cuda 10.0
• cudnn 7.5.1

Usage

Clone this project

git clone https://github.com/auroua/NPENASv1
cd NPENASv1

Closed Domain Search

NASBench-101 Dataset

1. Down load NASBench-101 dataset first. We only use the nasbench_only108.tfrecord file.
2. Modify the tf_records_path variable in nas_lib/config.py to store the absolute path of nasbench_only108.tfrecord.
3. You can test the default sampling pipeline via running the following command. Change the save_dir to your directory before running.

# gpus: the number of gpus used to execute searching.
# save_dir: the output path.
python train_multiple_gpus_close_domain.py --trials 600 --search_budget 150 --search_space nasbench_case1 --algo_params nasbench101_case1 --gpus 1 --save_dir /home/albert_wei/Disk_A/train_output_2021/npenas_101/ --comparison_type algorithm --record_full_data F

4. You can test the new sampling pipeline via running the following command. Change the save_dir to your directory before running.

# gpus: the number of gpus used to execute searching.
# save_dir: the output path.
python train_multiple_gpus_close_domain.py --trials 600 --search_budget 150 --search_space nasbench_case2 --algo_params nasbench101_case2 --gpus 1 --save_dir /home/albert_wei/Disk_A/train_output_2021/npenas_101/ --comparison_type algorithm --record_full_data F

5. Run the following command to visualize the comparison of algorithms. Change the save_dir to the path of save_dir in step 3 or 4.

python tools_close_domain/visualize_results.py --search_space nasbench_101 --draw_type ERRORBAR --save_dir /home/albert_wei/Disk_A/train_output_npenas/close_domain_case1/ 

Visualize the results of comparing algorithms on NAS-Bench-101 search space via using the new sampling pipeline.

6. Scaling Factor Analysis

python train_multiple_gpus_close_domain.py --trials 200 --search_budget 150 --search_space nasbench_case2 --algo_params nasbench101_case2 --gpus 1 --save_dir /home/albert_wei/Disk_A/train_output_2021/npenas_101/ --comparison_type scalar_compare --record_full_data T --record_kt T 

7. ReLU CELU Analysis

python train_multiple_gpus_close_domain.py --trials 200 --search_budget 150 --search_space nasbench_case2 --algo_params nasbench101_case2 --gpus 1 --save_dir /home/albert_wei/Disk_A/train_output_2021/npenas_101/ --comparison_type relu_celu --record_full_data T --record_kt T --relu_celu_comparison_algo_type NPENAS_NP

NASBench-201 Dataset

We only compared algorithms using the new sampling pipeline on the NASBench-201 dataset.

1. Down load the NASBench-201 dataset first. In this experiment, we use the NASBench-201 dataset with version v1_1-096897, and the file name is NAS-Bench-201-v1_1-096897.pth.
2. Modify the nas_bench_201_path variable in nas_lib/config.py to store the absolute path of NAS-Bench-201-v1_1-096897.pth.
3. As the NASBench-201 dataset is too large. and the NASBench-201 utilizes edges as operates and nodes as features. In order to use the NAS-Bench-201 dataset, we have to convert this dataset first.
4. Modify the variable nas_bench_201_converted_path in nas_lib/config.py to the path that store the processed NAS-Bench-201 dataset.
5. Run the following command to execute convert. This step is memory consuming. The memory of my computer is 32G.

# dataset: the dataset used to train the architectures in NASBench-201, choices: ['cifar10-valid', 'cifar100', 'ImageNet16-120']
python tools_close_domain/train_init_dataset.py --dataset cifar10-valid

6. You can run the following command to compare algorithms on NAS-Bench-201 dataset. Change the save_dir to your directory before running.

# gpus: the number of gpus used to execute searching.
# save_dir: the output path.
python train_multiple_gpus_close_domain.py --trials 600 --search_budget 100 --search_space nasbench_201 --algo_params nasbench_201 --gpus 1 --multiprocessing-distributed False --save_dir /home/albert_wei/Disk_A/train_output_npenas/npenas_201/ --comparison_type algorithm --record_full_data F --dataset cifar100

7. Run the following command to visualize the comparison of algorithms. Change the save_dir to the path of save_dir in step 6.

python tools_close_domain/visualize_results.py --search_space nasbench_201 --save_dir /home/albert_wei/Disk_A/train_output_npenas/npenas_201/ --draw_type ERRORBAR

8. Scaling Factor Analysis

python train_multiple_gpus_close_domain.py --trials 200 --search_budget 100 --search_space nasbench_201 --algo_params nasbench_201 --gpus 1 --save_dir /home/albert_wei/Disk_A/train_output_2021/npenas_201/ --comparison_type scalar_compare --record_full_data T --record_kt T --dataset cifar100

9. ReLU CELU Analysis

python train_multiple_gpus_close_domain.py --trials 200 --search_budget 100 --search_space nasbench_201 --algo_params nasbench_201 --gpus 1 --save_dir /home/albert_wei/Disk_A/train_output_2021/npenas_201/ --comparison_type relu_celu --record_full_data T --record_kt T --relu_celu_comparison_algo_type NPENAS_NP --dataset cifar100

NASBench-NLP Dataset

We only compared algorithms using the new sampling pipeline on the NASBench-201 dataset.

1. Down load the NASBench-NLP dataset first.
2. Modify the nas_bench_nlp_path variable in nas_lib/config.py to store the folder directory of NASBench_NLP.
3. You can run the following command to compare algorithms on NAS-Bench-NLP dataset. Change the save_dir to your directory before running.

# gpus: the number of gpus used to execute searching.
# save_dir: the output path.
python train_multiple_gpus_close_domain.py --trials 600 --search_budget 100 --search_space nasbench_nlp --algo_params nasbench_nlp --gpus 1 --multiprocessing-distributed False --save_dir /home/albert_wei/Disk_A/train_output_npenas/nasbench_nlp/ --comparison_type algorithm --record_full_data F

4. Run the following command to visualize the comparison of algorithms. Change the save_dir to the path of save_dir in step 6.

python tools_close_domain/visualize_results.py --search_space nasbench_201 --save_dir /home/albert_wei/Disk_A/train_output_npenas/npenas_201/ --draw_type ERRORBAR

5. Scaling Factor Analysis

python train_multiple_gpus_close_domain.py --trials 200 --search_budget 100 --search_space nasbench_nlp --algo_params nasbench_nlp --gpus 1 --save_dir /home/albert_wei/Disk_A/train_output_2021/nasbench_nlp/ --comparison_type scalar_compare --record_full_data T --record_kt T

6. ReLU CELU Analysis

python train_multiple_gpus_close_domain.py --trials 200 --search_budget 100 --search_space nasbench_nlp --algo_params nasbench_nlp --gpus 1 --save_dir /home/albert_wei/Disk_A/train_output_2021/nasbench_nlp/ --comparison_type relu_celu --record_full_data T --record_kt T --relu_celu_comparison_algo_type NPENAS_BO

NASBench-ASR Dataset

We only compared algorithms using the new sampling pipeline on the NASBench-201 dataset.

1. Down load the NASBench-ASR dataset first.
2. Modify the nas_bench_asr_path variable in nas_lib/config.py to store the folder directory of NASBench_ASR.
3. You can run the following command to compare algorithms on NAS-Bench-ASR dataset. Change the save_dir to your directory before running.

# gpus: the number of gpus used to execute searching.
# save_dir: the output path.
python train_multiple_gpus_close_domain.py --trials 600 --search_budget 100 --search_space nasbench_asr --algo_params nasbench_asr --gpus 1 --multiprocessing-distributed False --save_dir /home/albert_wei/Disk_A/train_output_npenas/npenas_asr/ --comparison_type algorithm --record_full_data F

4. Run the following command to visualize the comparison of algorithms. Change the save_dir to the path of save_dir in step 6.

python tools_close_domain/visualize_results.py --search_space nasbench_asr --save_dir /home/albert_wei/Disk_A/train_output_npenas/npenas_201/ --draw_type ERRORBAR

5. Scaling Factor Analysis

python train_multiple_gpus_close_domain.py --trials 200 --search_budget 100 --search_space nasbench_asr --algo_params nasbench_asr --gpus 1 --save_dir /home/albert_wei/Disk_A/train_output_2021/npenas_asr/ --comparison_type scalar_compare --record_full_data T --record_kt T

6. ReLU CELU Analysis

python train_multiple_gpus_close_domain.py --trials 200 --search_budget 100 --search_space nasbench_asr --algo_params nasbench_asr --gpus 1 --save_dir /home/albert_wei/Disk_A/train_output_2021/npenas_asr/ --comparison_type relu_celu --record_full_data T --record_kt T --relu_celu_comparison_algo_type NPENAS_BO

Open Domain Search

1. Run the following command to search architecture in DARTS search space via algorithm NPENAS-BO. Change the save_dir to your directory before running.

python train_multiple_gpus_open_domain.py --gpus 1 --algorithm gin_uncertainty_predictor --budget 150 --save_dir /home/albert_wei/Disk_A/train_output_npenas/npenas_open_domain_darts_1/

2. Run the following command to search architecture in DARTS search space via algorithm NPENAS-NP. Change the save_dir to your directory before running.

python train_multiple_gpus_open_domain.py --gpus 1 --algorithm gin_predictor --budget 100 --save_dir /home/albert_wei/Disk_A/train_output_npenas/npenas_open_domain_darts_2/

3. Run the following command to rank the searched architectures, and select the best to retrain. Replace model_path with the real searched architectures' output path.

python tools_open_domain/rank_searched_darts_arch.py --model_path /home/albert_wei/Disk_A/train_output_npenas/npenas_open_domain_darts_2/model_pkl/

4. Retrain the selected architecture using the following command.

model_name: the id of select architecture
save_dir: the output path
python tools_open_domain/train_darts_cifar10.py --seed 1 --model_name ace85b6b1618a4e0ebdc0db40934f2982ac57a34ec9f31dcd8d209b3855dce1f.pkl  --save_dir /home/albert_wei/Disk_A/train_output_npenas/npenas_open_domain_darts_2/

5. Test the retrained architecture with the following command

model_name: the id of select architecture
save_dir: set with the save_dir in step 4
model_path: set with the save_dir in step 1 or 2 
python tools_open_domain/test_darts_cifar10.py  --model_name xxxx --save_dir xxxx  --model_path xxxx

6. Run the following command to visualize the normal cell and reduction cell of the searched best architecture.

python tools_open_domain/visualize_results.py --model_path xxxx --model_name xxxx

If you encounter the following problem please reference this link possible deadlock in dataloader

RuntimeError: unable to open shared memory object </torch_31124_2696026929> in read-write mode

Visualize the normal cell and the reduction cell searched by NPENAS-NP, and this architecture achieves a testing error 2.44%.

You can download the best architecture's genotype file from genotype with extract code itw9. The address of the retrained weight file is ckpt with extract code t9xq. You can use the command in step 5 to verify the model.

Compare the different evaluation strategies

There are two mutation strategies: one-parent-one-child, one-parent-multiple-children. Change the save_dir to your directory before running.

1. Run the following command.

python train_multiple_gpus_close_domain.py --trials 600 --search_budget 150 --search_space nasbench_case1 --algo_params evaluation_compare --gpus 1 --save_dir /home/albert_wei/Disk_A/train_output_npenas/evolutionary_compare/

2. Visualize the results. Set the save_dir with the save_dir in step 1.

python tools_close_domain/visualize_results.py --search_space evaluation_compare --draw_type ERRORBAR  --save_dir /home/albert_wei/Disk_A/train_output_npenas/evolutionary_compare/

Compare the paths distribution of different sampling pipeline

There are two different architecture sampling pipelines: the default sampling pipeline and the new sampling pipeline. Run the following code to compare the paths distribution of different sampling pipelines:

python tools_close_domain/visualize_sample_distribution.py --sample_num 5000 --seed 98765

Compare the prediction performance of different methods

1. Compare the four method mentioned in the paper.

python tools_close_domain/prediction_compare.py --trials 300 --seed 434 --search_space nasbench_case1 --save_path /home/albert_wei/Disk_A/train_output_npenas/prediction_compare/prediction_compare.pkl

2. Parse the results generate from the above step.

python tools_close_domain/prediction_compare_parse.py --save_path /home/albert_wei/Disk_A/train_output_npenas/prediction_compare/prediction_compare.pkl

Experiment Results

_{* modify the parameters of the visualization script to view results.}

Acknowledge

1. bananas
2. naszilla
3. pytorch_geometric
4. maskrcnn-benchmark
5. detectron2
6. NAS-Bench-101
7. NAS-Bench-201
8. darts
9. AlphaX
10. NAS-Bench-NLP
11. NAS-Bench-ASR

Contact

Chen Wei

email: weichen_3@stu.xidian.edu.cn