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Sync NATS-Bench's v1.0 and update algorithm names

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2020-10-15 21:56:10 +11:00
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@@ -7,6 +7,7 @@ We analyze the validity of our benchmark in terms of various criteria and perfor
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.
This facilitates a much larger community of researchers to focus on developing better NAS algorithms in a more comparable and computationally effective environment.
**You can use `pip install nats_bench` to install the library of NATS-Bench.**
The structure of this Markdown file:
- [How to use NATS-Bench?](#How-to-Use-NATS-Bench)
@@ -175,18 +176,18 @@ python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HO
python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo tas --rand_seed 777
Run the channel search strategy in FBNet-V2
Run the channel search strategy in FBNet-V2 -- masking + Gumbel-Softmax :
python ./exps/NATS-algos/search-size.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo fbv2 --rand_seed 777
python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo fbv2 --rand_seed 777
python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo fbv2 --rand_seed 777
python ./exps/NATS-algos/search-size.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo mask_gumbel --rand_seed 777
python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo mask_gumbel --rand_seed 777
python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo mask_gumbel --rand_seed 777
Run the channel search strategy in TuNAS:
Run the channel search strategy in TuNAS -- masking + sampling :
python ./exps/NATS-algos/search-size.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo tunas --arch_weight_decay 0 --rand_seed 777 --use_api 0
python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo tunas --arch_weight_decay 0 --rand_seed 777
python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo tunas --arch_weight_decay 0 --rand_seed 777
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
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
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
```
### Final Discovered Architectures for Each Algorithm
@@ -250,7 +251,7 @@ GDAS:
If you find that NATS-Bench helps your research, please consider citing it:
```
@article{dong2020nats,
title={NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size},
title={{NATS-Bench}: Benchmarking NAS algorithms for Architecture Topology and Size},
author={Dong, Xuanyi and Liu, Lu and Musial, Katarzyna and Gabrys, Bogdan},
journal={arXiv preprint arXiv:2009.00437},
year={2020}