92 lines
4.0 KiB
Python
92 lines
4.0 KiB
Python
import os
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import sys
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import random
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import numpy as np
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import argparse
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import torch
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import os
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from nag import NAG
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sys.path.append(os.getcwd())
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save_path = "results"
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data_path = os.path.join('MetaD2A_nas_bench_201', 'data')
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def str2bool(v):
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return v.lower() in ['t', 'true', True]
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def get_parser():
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parser = argparse.ArgumentParser()
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# general settings
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parser.add_argument('--seed', type=int, default=444)
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parser.add_argument('--gpu', type=str, default='0', help='set visible gpus')
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parser.add_argument('--save-path', type=str, default=save_path, help='the path of save directory')
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parser.add_argument('--data-path', type=str, default=data_path, help='the path of save directory')
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parser.add_argument('--model-load-path', type=str, default='', help='')
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parser.add_argument('--save-epoch', type=int, default=20, help='how many epochs to wait each time to save model states')
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parser.add_argument('--max-epoch', type=int, default=1000, help='number of epochs to train')
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parser.add_argument('--batch_size', type=int, default=1024, help='batch size for generator')
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parser.add_argument('--graph-data-name', default='nasbench201', help='graph dataset name')
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parser.add_argument('--nvt', type=int, default=7, help='number of different node types, 7: NAS-Bench-201 including in/out node')
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# set encoder
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parser.add_argument('--num-sample', type=int, default=20, help='the number of images as input for set encoder')
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# graph encoder
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parser.add_argument('--hs', type=int, default=512, help='hidden size of GRUs')
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parser.add_argument('--nz', type=int, default=56, help='the number of dimensions of latent vectors z')
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# test
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parser.add_argument('--test', action='store_true', default=True, help='turn on test mode')
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parser.add_argument('--load-epoch', type=int, default=100, help='checkpoint epoch loaded for meta-test')
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parser.add_argument('--data-name', type=str, default='pets', help='meta-test dataset name')
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parser.add_argument('--trials', type=int, default=20)
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parser.add_argument('--num-class', type=int, default=None, help='the number of class of dataset')
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parser.add_argument('--num-gen-arch', type=int, default=500, help='the number of candidate architectures generated by the generator')
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parser.add_argument('--train-arch', type=str2bool, default=True, help='whether to train the searched architecture')
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parser.add_argument('--n_init', type=int, default=10)
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parser.add_argument('--N', type=int, default=1)
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# DiffusionNAG
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parser.add_argument('--folder_name', type=str, default='debug')
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parser.add_argument('--exp_name', type=str, default='')
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parser.add_argument('--classifier_scale', type=float, default=10000., help='classifier scale')
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parser.add_argument('--scale_step', type=float, default=300.)
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parser.add_argument('--eval_batch_size', type=int, default=256)
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parser.add_argument('--predictor', type=str, default='euler_maruyama', choices=['euler_maruyama', 'reverse_diffusion', 'none'])
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parser.add_argument('--corrector', type=str, default='langevin', choices=['none', 'langevin'])
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parser.add_argument('--patient_factor', type=int, default=20)
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parser.add_argument('--n_gen_samples', type=int, default=10)
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parser.add_argument('--multi_proc', type=str2bool, default=True)
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args = parser.parse_args()
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return args
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def set_exp_name(args):
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exp_name = f'./results/transfer_nag/{args.folder_name}/{args.data_name}'
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os.makedirs(exp_name, exist_ok=True)
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args.exp_name = exp_name
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def main():
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## Get arguments
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args = get_parser()
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## Set gpus and seeds
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os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
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torch.cuda.manual_seed(args.seed)
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torch.manual_seed(args.seed)
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torch.cuda.manual_seed_all(args.seed)
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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np.random.seed(args.seed)
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random.seed(args.seed)
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## Set experiment name
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set_exp_name(args)
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## Run
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nag = NAG(args)
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nag.meta_test()
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if __name__ == '__main__':
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main()
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