re-organize NATS-Bench
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		| @@ -7,11 +7,23 @@ 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. | ||||
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| **coming soon!** | ||||
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| ## How to Use NATS-Bench | ||||
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| ## The Procedure of Creating NATS-Bench | ||||
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| 1, train all architecture candidate in the size search space with 90 epochs and use the random seed of `777`. | ||||
| ``` | ||||
| bash ./scripts/NATS-Bench/train-shapes.sh 00000-32767 90 777 | ||||
| ``` | ||||
| The checkpoint of all candidates are located at `output/NATS-Bench-size` by default | ||||
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| ## To Reproduce 13 Baseline NAS Algorithms in NAS-Bench-201 | ||||
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| ### Reproduce NAS methods on the topology search space | ||||
| @@ -50,6 +62,7 @@ python ./exps/algos-v2/search-cell.py --dataset ImageNet16-120 --data_path $TORC | ||||
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| ### Reproduce NAS methods on the size search space | ||||
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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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