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2020-10-13 00:06:31 +11:00
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docs/NATS-Bench.md
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@@ -11,7 +11,7 @@ This facilitates a much larger community of researchers to focus on developing b
The structure of this Markdown file:
- [How to use NATS-Bench?](#How-to-Use-NATS-Bench)
- [How to re-create NATS-Bench from scratch?](#how-to-re-create-nats-bench-from-scratch)
- [How to reproduce benchmarked results?](#to-reproduce-13-baseline-nas-algorithms-in-nas-bench-201)
- [How to reproduce benchmarked results?](#to-reproduce-13-baseline-nas-algorithms-in-nats-bench)
## How to Use [NATS-Bench](https://arxiv.org/pdf/2009.00437.pdf)
@@ -77,8 +77,12 @@ params = api.get_net_param(12, 'cifar10', None)
network.load_state_dict(next(iter(params.values())))
```
## How to Re-create NATS-Bench from Scratch
You need to use the [AutoDL-Projects](https://github.com/D-X-Y/AutoDL-Projects) repo to re-create NATS-Bench from scratch.
### The Size Search Space
The following command will train all architecture candidate in the size search space with 90 epochs and use the random seed of `777`.
@@ -108,7 +112,9 @@ python exps/NATS-Bench/tss-collect.py
```
## To Reproduce 13 Baseline NAS Algorithms in NAS-Bench-201
## To Reproduce 13 Baseline NAS Algorithms in NATS-Bench
You need to use the [AutoDL-Projects](https://github.com/D-X-Y/AutoDL-Projects) repo to run 13 baseline NAS methods.
### Reproduce NAS methods on the topology search space
@@ -169,14 +175,14 @@ 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 search strategy in FBNet-V2
Run the channel search strategy in FBNet-V2
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
Run the search strategy in TuNAS:
Run the channel search strategy in TuNAS:
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