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@ -61,7 +61,7 @@ At this moment, this project provides the following algorithms and scripts to ru
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<tr> <!-- (6-th row) -->
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<td align="center" valign="middle"> NATS-Bench </td>
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<td align="center" valign="middle"> <a href="https://xuanyidong.com/assets/projects/NATS-Bench"> NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size</a> </td>
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<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/NATS-Bench.md">NATS-Bench.md</a> </td>
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<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/NATS-Bench">NATS-Bench.md</a> </td>
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</tr>
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<td align="center" valign="middle"> ... </td>
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# [NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size](https://arxiv.org/pdf/2009.00437.pdf)
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Neural architecture search (NAS) has attracted a lot of attention and has been illustrated to bring tangible benefits in a large number of applications in the past few years. Network topology and network size have been regarded as two of the most important aspects for the performance of deep learning models and the community has spawned lots of searching algorithms for both of those aspects of the neural architectures. However, the performance gain from these searching algorithms is achieved under different search spaces and training setups. This makes the overall performance of the algorithms incomparable and the improvement from a sub-module of the searching model unclear.
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In this paper, we propose NATS-Bench, a unified benchmark on searching for both topology and size, for (almost) any up-to-date NAS algorithm.
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NATS-Bench includes the search space of 15,625 neural cell candidates for architecture topology and 32,768 for architecture size on three datasets.
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We analyze the validity of our benchmark in terms of various criteria and performance comparison of all candidates in the search space.
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We also show the versatility of NATS-Bench by benchmarking 13 recent state-of-the-art NAS algorithms on it. All logs and diagnostic information trained using the same setup for each candidate are provided.
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This facilitates a much larger community of researchers to focus on developing better NAS algorithms in a more comparable and computationally effective environment.
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**You can use `pip install nats_bench` to install the library of NATS-Bench.**
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or install from the [source codes](https://github.com/D-X-Y/NATS-Bench) via `python setup.py install`.
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The structure of this Markdown file:
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- [How to use NATS-Bench?](#How-to-Use-NATS-Bench)
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- [How to re-create NATS-Bench from scratch?](#how-to-re-create-nats-bench-from-scratch)
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- [How to reproduce benchmarked results?](#to-reproduce-13-baseline-nas-algorithms-in-nats-bench)
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## How to Use [NATS-Bench](https://arxiv.org/pdf/2009.00437.pdf)
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### Preparation and Download
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The **latest** benchmark file of NATS-Bench can be downloaded from [Google Drive](https://drive.google.com/drive/folders/1zjB6wMANiKwB2A1yil2hQ8H_qyeSe2yt?usp=sharing).
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After download `NATS-[tss/sss]-[version]-[md5sum]-simple.tar`, please uncompress it by using `tar xvf [file_name]`.
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We highly recommend to put the downloaded benchmark file (`NATS-sss-v1_0-50262.pickle.pbz2` / `NATS-tss-v1_0-3ffb9.pickle.pbz2`) or uncompressed archive (`NATS-sss-v1_0-50262-simple` / `NATS-tss-v1_0-3ffb9-simple`) into `$TORCH_HOME`.
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In this way, our api will automatically find the path for these benchmark files, which are convenient for the users. Otherwise, you need to indicate the file when creating the benchmark instance manually.
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The history of benchmark files is as follows, `tss` indicates the topology search space and `sss` indicates the size search space.
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The benchmark file is used when creating the NATS-Bench instance with `fast_mode=False`.
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The archive is used when `fast_mode=True`, where `archive` is a directory containing 15,625 files for tss or contains 32,768 files for sss.
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Each file contains all the information for a specific architecture candidate.
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The `full archive` is similar to `archive`, while each file in `full archive` contains **the trained weights**.
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Since the full archive is too large, we use `split -b 30G file_name file_name` to split it into multiple 30G chunks.
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To merge the chunks into the original full archive, you can use `cat file_name* > file_name`.
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| Date | benchmark file (tss) | archive (tss) | full archive (tss) | benchmark file (sss) | archive (sss) | full archive (sss) |
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|:-----------|:---------------------:|:-------------:|:------------------:|:-------------------------------:|:--------------------------:|:------------------:|
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| 2020.08.31 | [NATS-tss-v1_0-3ffb9.pickle.pbz2](https://drive.google.com/file/d/1vzyK0UVH2D3fTpa1_dSWnp1gvGpAxRul/view?usp=sharing) | [NATS-tss-v1_0-3ffb9-simple.tar](https://drive.google.com/file/d/17_saCsj_krKjlCBLOJEpNtzPXArMCqxU/view?usp=sharing) | [NATS-tss-v1_0-3ffb9-full](https://drive.google.com/drive/folders/17S2Xg_rVkUul4KuJdq0WaWoUuDbo8ZKB?usp=sharing) | [NATS-sss-v1_0-50262.pickle.pbz2](https://drive.google.com/file/d/1IabIvzWeDdDAWICBzFtTCMXxYWPIOIOX/view?usp=sharing) | [NATS-sss-v1_0-50262-simple.tar](https://drive.google.com/file/d/1scOMTUwcQhAMa_IMedp9lTzwmgqHLGgA/view?usp=sharing) | [NATS-sss-v1_0-50262-full](api.reload(index=12)) |
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These benchmark files (without pretrained weights) can also be downloaded from [Dropbox](https://www.dropbox.com/sh/ceeo70u1buow681/AAC2M-SbKOxiIqpB0UCgXNxja?dl=0) or [OneDrive](https://1drv.ms/u/s!Aqkc27lrowWDf6SvuIkSXx0UQaI?e=nfvM5r).
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1, create the benchmark instance:
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```
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from nats_bench import create
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# Create the API instance for the size search space in NATS
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api = create(None, 'sss', fast_mode=True, verbose=True)
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# Create the API instance for the topology search space in NATS
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api = create(None, 'tss', fast_mode=True, verbose=True)
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```
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2, query the performance:
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```
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# Query the loss / accuracy / time for 1234-th candidate architecture on CIFAR-10
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# info is a dict, where you can easily figure out the meaning by key
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info = api.get_more_info(1234, 'cifar10')
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# Query the flops, params, latency. info is a dict.
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info = api.get_cost_info(12, 'cifar10')
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# Simulate the training of the 1224-th candidate:
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validation_accuracy, latency, time_cost, current_total_time_cost = api.simulate_train_eval(1224, dataset='cifar10', hp='12')
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```
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3, others:
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```
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# Clear the parameters of the 12-th candidate.
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api.clear_params(12)
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# Reload all information of the 12-th candidate.
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api.reload(index=12)
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# Create the instance of th 12-th candidate for CIFAR-10.
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from models import get_cell_based_tiny_net
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config = api.get_net_config(12, 'cifar10')
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network = get_cell_based_tiny_net(config)
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# Load the pre-trained weights: params is a dict, where the key is the seed and value is the weights.
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params = api.get_net_param(12, 'cifar10', None)
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network.load_state_dict(next(iter(params.values())))
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```
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## How to Re-create NATS-Bench from Scratch
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You need to use the [AutoDL-Projects](https://github.com/D-X-Y/AutoDL-Projects) repo to re-create NATS-Bench from scratch.
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### The Size Search Space
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The following command will train all architecture candidate in the size search space with 90 epochs and use the random seed of `777`.
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If you want to use a different number of training epochs, please replace `90` with it, such as `01` or `12`.
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```
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bash ./scripts/NATS-Bench/train-shapes.sh 00000-32767 90 777
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```
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The checkpoint of all candidates are located at `output/NATS-Bench-size` by default.
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After training these candidate architectures, please use the following command to re-organize all checkpoints into the official benchmark file.
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```
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python exps/NATS-Bench/sss-collect.py
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```
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### The Topology Search Space
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The following command will train all architecture candidate in the topology search space with 200 epochs and use the random seed of `777`/`888`/`999`.
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If you want to use a different number of training epochs, please replace `200` with it, such as `12`.
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```
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bash scripts/NATS-Bench/train-topology.sh 00000-15624 200 '777 888 999'
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```
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The checkpoint of all candidates are located at `output/NATS-Bench-topology` by default.
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After training these candidate architectures, please use the following command to re-organize all checkpoints into the official benchmark file.
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```
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python exps/NATS-Bench/tss-collect.py
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```
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## To Reproduce 13 Baseline NAS Algorithms in NATS-Bench
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You need to use the [AutoDL-Projects](https://github.com/D-X-Y/AutoDL-Projects) repo to run 13 baseline NAS methods.
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### Reproduce NAS methods on the topology search space
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Please use the following commands to run different NAS methods on the topology search space:
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```
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Four multi-trial based methods:
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python ./exps/NATS-algos/reinforce.py --dataset cifar100 --search_space tss --learning_rate 0.01
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python ./exps/NATS-algos/regularized_ea.py --dataset cifar100 --search_space tss --ea_cycles 200 --ea_population 10 --ea_sample_size 3
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python ./exps/NATS-algos/random_wo_share.py --dataset cifar100 --search_space tss
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python ./exps/NATS-algos/bohb.py --dataset cifar100 --search_space tss --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3
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DARTS (first order):
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python ./exps/NATS-algos/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo darts-v1
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python ./exps/NATS-algos/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo darts-v1
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python ./exps/NATS-algos/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo darts-v1
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DARTS (second order):
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python ./exps/NATS-algos/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo darts-v2
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python ./exps/NATS-algos/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo darts-v2
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python ./exps/NATS-algos/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo darts-v2
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GDAS:
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python ./exps/NATS-algos/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo gdas
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python ./exps/NATS-algos/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo gdas
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python ./exps/NATS-algos/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16
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SETN:
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python ./exps/NATS-algos/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo setn
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python ./exps/NATS-algos/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo setn
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python ./exps/NATS-algos/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo setn
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Random Search with Weight Sharing:
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python ./exps/NATS-algos/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo random
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python ./exps/NATS-algos/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo random
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python ./exps/NATS-algos/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo random
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ENAS:
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python ./exps/NATS-algos/search-cell.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo enas --arch_weight_decay 0 --arch_learning_rate 0.001 --arch_eps 0.001
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python ./exps/NATS-algos/search-cell.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo enas --arch_weight_decay 0 --arch_learning_rate 0.001 --arch_eps 0.001
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python ./exps/NATS-algos/search-cell.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo enas --arch_weight_decay 0 --arch_learning_rate 0.001 --arch_eps 0.001
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```
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### Reproduce NAS methods on the size search space
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Please use the following commands to run different NAS methods on the size search space:
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```
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Four multi-trial based methods:
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python ./exps/NATS-algos/reinforce.py --dataset cifar100 --search_space sss --learning_rate 0.01
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python ./exps/NATS-algos/regularized_ea.py --dataset cifar100 --search_space sss --ea_cycles 200 --ea_population 10 --ea_sample_size 3
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python ./exps/NATS-algos/random_wo_share.py --dataset cifar100 --search_space sss
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python ./exps/NATS-algos/bohb.py --dataset cifar100 --search_space sss --num_samples 4 --random_fraction 0.0 --bandwidth_factor 3
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Run Transformable Architecture Search (TAS), proposed in Network Pruning via Transformable Architecture Search, NeurIPS 2019
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python ./exps/NATS-algos/search-size.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo tas --rand_seed 777
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python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo tas --rand_seed 777
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python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo tas --rand_seed 777
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Run the channel search strategy in FBNet-V2 -- masking + Gumbel-Softmax :
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python ./exps/NATS-algos/search-size.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo mask_gumbel --rand_seed 777
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python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo mask_gumbel --rand_seed 777
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python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo mask_gumbel --rand_seed 777
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Run the channel search strategy in TuNAS -- masking + sampling :
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python ./exps/NATS-algos/search-size.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo mask_rl --arch_weight_decay 0 --rand_seed 777 --use_api 0
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python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo mask_rl --arch_weight_decay 0 --rand_seed 777
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python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo mask_rl --arch_weight_decay 0 --rand_seed 777
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```
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### Final Discovered Architectures for Each Algorithm
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The architecture index can be found by use `api.query_index_by_arch(architecture_string)`.
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The final discovered architecture ID on CIFAR-10:
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```
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DARTS (first order):
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|skip_connect~0|+|skip_connect~0|skip_connect~1|+|skip_connect~0|skip_connect~1|skip_connect~2|
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|skip_connect~0|+|skip_connect~0|skip_connect~1|+|skip_connect~0|skip_connect~1|skip_connect~2|
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|skip_connect~0|+|skip_connect~0|skip_connect~1|+|skip_connect~0|skip_connect~1|skip_connect~2|
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DARTS (second order):
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|skip_connect~0|+|skip_connect~0|skip_connect~1|+|skip_connect~0|skip_connect~1|skip_connect~2|
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|skip_connect~0|+|skip_connect~0|skip_connect~1|+|skip_connect~0|skip_connect~1|skip_connect~2|
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|skip_connect~0|+|skip_connect~0|skip_connect~1|+|skip_connect~0|skip_connect~1|skip_connect~2|
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GDAS:
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|nor_conv_3x3~0|+|nor_conv_3x3~0|none~1|+|nor_conv_1x1~0|nor_conv_3x3~1|nor_conv_3x3~2|
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|nor_conv_3x3~0|+|nor_conv_3x3~0|none~1|+|nor_conv_3x3~0|nor_conv_3x3~1|nor_conv_3x3~2|
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|avg_pool_3x3~0|+|nor_conv_3x3~0|skip_connect~1|+|nor_conv_3x3~0|nor_conv_1x1~1|nor_conv_1x1~2|
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```
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The final discovered architecture ID on CIFAR-100:
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```
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DARTS (V1):
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|none~0|+|skip_connect~0|none~1|+|skip_connect~0|nor_conv_1x1~1|none~2|
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|none~0|+|skip_connect~0|none~1|+|skip_connect~0|nor_conv_1x1~1|none~2|
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|skip_connect~0|+|skip_connect~0|none~1|+|skip_connect~0|nor_conv_1x1~1|nor_conv_3x3~2|
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DARTS (V2):
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|none~0|+|skip_connect~0|none~1|+|skip_connect~0|nor_conv_1x1~1|skip_connect~2|
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|skip_connect~0|+|nor_conv_3x3~0|none~1|+|skip_connect~0|none~1|none~2|
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|skip_connect~0|+|nor_conv_1x1~0|none~1|+|nor_conv_3x3~0|skip_connect~1|none~2|
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GDAS:
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|nor_conv_3x3~0|+|nor_conv_1x1~0|none~1|+|avg_pool_3x3~0|nor_conv_3x3~1|nor_conv_3x3~2|
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|avg_pool_3x3~0|+|nor_conv_1x1~0|none~1|+|nor_conv_3x3~0|avg_pool_3x3~1|nor_conv_1x1~2|
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|avg_pool_3x3~0|+|nor_conv_3x3~0|none~1|+|nor_conv_3x3~0|nor_conv_1x1~1|nor_conv_1x1~2|
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```
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The final discovered architecture ID on ImageNet-16-120:
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```
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DARTS (V1):
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|none~0|+|skip_connect~0|none~1|+|skip_connect~0|none~1|nor_conv_3x3~2|
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|none~0|+|skip_connect~0|none~1|+|skip_connect~0|none~1|nor_conv_3x3~2|
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|none~0|+|skip_connect~0|none~1|+|skip_connect~0|none~1|nor_conv_1x1~2|
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DARTS (V2):
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|none~0|+|skip_connect~0|none~1|+|skip_connect~0|none~1|skip_connect~2|
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GDAS:
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|none~0|+|none~0|none~1|+|nor_conv_3x3~0|none~1|none~2|
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|none~0|+|none~0|none~1|+|nor_conv_3x3~0|none~1|none~2|
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|none~0|+|none~0|none~1|+|nor_conv_3x3~0|none~1|none~2|
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```
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## Citation
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If you find that NATS-Bench helps your research, please consider citing it:
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```
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@article{dong2021nats,
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title = {{NATS-Bench}: Benchmarking NAS Algorithms for Architecture Topology and Size},
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author = {Dong, Xuanyi and Liu, Lu and Musial, Katarzyna and Gabrys, Bogdan},
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doi = {10.1109/TPAMI.2021.3054824},
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journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
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year = {2021},
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note = {\mbox{doi}:\url{10.1109/TPAMI.2021.3054824}}
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}
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```
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