2020-03-09 09:38:00 +01:00
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#####################################################
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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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2020-03-21 09:33:07 +01:00
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import sys, argparse
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2020-03-09 09:38:00 +01:00
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from pathlib import Path
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lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
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if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
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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('--api_path', type=str, default=None, help='The path to the NAS-Bench-201 benchmark file.')
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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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