TF-MOPNAS: Training-free multi-objective pruning-based neural architecture search

Quan Minh Phan, Ngoc Hoang Luong.

In ICCCI 2022.

Setup

• Clone this repo.
• Install necessary packages.

$ pip install -r requirements.txt

• If you meet a problem involving to fvcore and nats-bench packages, please re-install it.

$ pip install nats-bench
$ pip install fvcore==0.1.5post20220119

• Download the database in here, unzip and put all folders into benchmark_data folder for building benchmarks' APIs when conducting NAS runs.

Usage

Note: To experiment without changing too much in the source code, we recommend that the benchmarks' databases should be stored in the default folder (i.e., benchmark_data)

$ python <running_file> --n_runs <the_number_of_experiment_runs, default: 31> --seed <initial_random_seed, default: 0>

To conduct searches on a specific search space, we just run the corresponding file.

running_file	Benchmark	Algorithm
run_101.py	NAS-Bench-101	TF-MOPNAS (random cell topologies + edges pruning)
run_1shot1.py	NAS-Bench-1shot1	TF-MOPNAS (cell topologies + edges pruning)
run_1shot1_random.py	NAS-Bench-1shot1	TF-MOPNAS (random cell topologies + edges pruning)
run_201.py	NAS-Bench-201	TF-MOPNAS (edges pruning)

For example, we want to search on NAS-Bench-201 benchmark:

$ python run_201.py

Results in paper

NAS-Bench-101

NAS-Bench-1shot1

NAS-Bench-201

Acknowledgement

Our source code is inspired by:

• pymoo: Multi-objective Optimization in Python
• NSGA-Net: Neural Architecture Search using Multi-Objective Genetic Algorithm
• NAS-Bench-101: Towards Reproducible Neural Architecture Search
• NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search
• NAS-Bench-1shot1: Benchmarking and Dissecting One-shot Neural Architecture Search
• How Powerful are Performance Predictors in Neural Architecture Search?