44 lines
1.9 KiB
Python
44 lines
1.9 KiB
Python
#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.01 #
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################################################################################################
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# python exps/NAS-Bench-201/show-best.py --api_path $HOME/.torch/NAS-Bench-201-v1_0-e61699.pth #
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################################################################################################
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import argparse
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from pathlib import Path
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from nas_201_api import NASBench201API as API
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if __name__ == "__main__":
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parser = argparse.ArgumentParser("Analysis of NAS-Bench-201")
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parser.add_argument(
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"--api_path",
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type=str,
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default=None,
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help="The path to the NAS-Bench-201 benchmark file.",
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)
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args = parser.parse_args()
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meta_file = Path(args.api_path)
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assert meta_file.exists(), "invalid path for api : {:}".format(meta_file)
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api = API(str(meta_file))
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# This will show the results of the best architecture based on the validation set of each dataset.
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arch_index, accuracy = api.find_best("cifar10-valid", "x-valid", None, None, False)
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print("FOR CIFAR-010, using the hyper-parameters with 200 training epochs :::")
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print("arch-index={:5d}, arch={:}".format(arch_index, api.arch(arch_index)))
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api.show(arch_index)
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print("")
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arch_index, accuracy = api.find_best("cifar100", "x-valid", None, None, False)
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print("FOR CIFAR-100, using the hyper-parameters with 200 training epochs :::")
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print("arch-index={:5d}, arch={:}".format(arch_index, api.arch(arch_index)))
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api.show(arch_index)
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print("")
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arch_index, accuracy = api.find_best("ImageNet16-120", "x-valid", None, None, False)
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print("FOR ImageNet16-120, using the hyper-parameters with 200 training epochs :::")
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print("arch-index={:5d}, arch={:}".format(arch_index, api.arch(arch_index)))
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api.show(arch_index)
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print("")
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