help='The path to the NAS-Bench-201 benchmark file.')
args = parser.parse_args()
meta_file = Path(args.api_path)
assert meta_file.exists(), 'invalid path for api : {:}'.format(meta_file)
api = API(str(meta_file))
# This will show the results of the best architecture based on the validation set of each dataset.
arch_index, accuracy = api.find_best('cifar10-valid', 'x-valid', None,
None, False)
print(
'FOR CIFAR-010, using the hyper-parameters with 200 training epochs :::'
)
print('arch-index={:5d}, arch={:}'.format(arch_index,
api.arch(arch_index)))
api.show(arch_index)
print('')
arch_index, accuracy = api.find_best('cifar100', 'x-valid', None, None,
False)
print(
'FOR CIFAR-100, using the hyper-parameters with 200 training epochs :::'
)
print('arch-index={:5d}, arch={:}'.format(arch_index,
api.arch(arch_index)))
api.show(arch_index)
print('')
arch_index, accuracy = api.find_best('ImageNet16-120', 'x-valid', None,
None, False)
from nas_201_api import NASBench201API as API
if __name__ == '__main__':
parser = argparse.ArgumentParser("Analysis of NAS-Bench-201")
parser.add_argument('--api_path', type=str, default=None, help='The path to the NAS-Bench-201 benchmark file.')
args = parser.parse_args()
meta_file = Path(args.api_path)
assert meta_file.exists(), 'invalid path for api : {:}'.format(meta_file)
api = API(str(meta_file))
# This will show the results of the best architecture based on the validation set of each dataset.
arch_index, accuracy = api.find_best('cifar10-valid', 'x-valid', None, None, False)
print('FOR CIFAR-010, using the hyper-parameters with 200 training epochs :::')
print('arch-index={:5d}, arch={:}'.format(arch_index, api.arch(arch_index)))
api.show(arch_index)
print('')
arch_index, accuracy = api.find_best('cifar100', 'x-valid', None, None, False)
print('FOR CIFAR-100, using the hyper-parameters with 200 training epochs :::')
print('arch-index={:5d}, arch={:}'.format(arch_index, api.arch(arch_index)))
api.show(arch_index)
print('')
arch_index, accuracy = api.find_best('ImageNet16-120', 'x-valid', None, None, False)
print('FOR ImageNet16-120, using the hyper-parameters with 200 training epochs :::')
print('arch-index={:5d}, arch={:}'.format(arch_index, api.arch(arch_index)))
api.show(arch_index)
print('')
