update the naswot code
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@ -49,7 +49,7 @@ def get_data(dataset, data_loc, trainval, batch_size, augtype, repeat, args, pin
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val_acc_type = 'x-valid'
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if trainval and 'cifar10' in dataset:
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cifar_split = load_config('config_utils/cifar-split.txt', None, None)
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cifar_split = load_config('/nfs/data3/hanzhang/nasbenchDiT/graph_dit/naswot/naswot/config_utils/cifardata/cifar-split.txt', None, None)
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train_split, valid_split = cifar_split.train, cifar_split.valid
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if repeat > 0:
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train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size,
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1
graph_dit/naswot/naswot/nasbench
Submodule
1
graph_dit/naswot/naswot/nasbench
Submodule
@ -0,0 +1 @@
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Subproject commit b94247037ee470418a3e56dcb83814e9be83f3a8
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@ -12,41 +12,41 @@ import time
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from naswot.models import get_cell_based_tiny_net
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from naswot.utils import add_dropout, init_network
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parser = argparse.ArgumentParser(description='NAS Without Training')
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parser.add_argument('--data_loc', default='../cifardata/', type=str, help='dataset folder')
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parser.add_argument('--api_loc', default='../NAS-Bench-201-v1_0-e61699.pth',
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type=str, help='path to API')
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parser.add_argument('--save_loc', default='results', type=str, help='folder to save results')
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parser.add_argument('--save_string', default='naswot', type=str, help='prefix of results file')
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parser.add_argument('--score', default='hook_logdet', type=str, help='the score to evaluate')
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parser.add_argument('--nasspace', default='nasbench201', type=str, help='the nas search space to use')
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parser.add_argument('--batch_size', default=128, type=int)
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parser.add_argument('--repeat', default=1, type=int, help='how often to repeat a single image with a batch')
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parser.add_argument('--augtype', default='none', type=str, help='which perturbations to use')
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parser.add_argument('--sigma', default=0.05, type=float, help='noise level if augtype is "gaussnoise"')
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parser.add_argument('--GPU', default='0', type=str)
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parser.add_argument('--seed', default=1, type=int)
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parser.add_argument('--init', default='', type=str)
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parser.add_argument('--trainval', action='store_true')
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parser.add_argument('--dropout', action='store_true')
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parser.add_argument('--dataset', default='cifar10', type=str)
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parser.add_argument('--maxofn', default=1, type=int, help='score is the max of this many evaluations of the network')
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parser.add_argument('--n_samples', default=100, type=int)
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parser.add_argument('--n_runs', default=500, type=int)
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parser.add_argument('--stem_out_channels', default=16, type=int, help='output channels of stem convolution (nasbench101)')
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parser.add_argument('--num_stacks', default=3, type=int, help='#stacks of modules (nasbench101)')
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parser.add_argument('--num_modules_per_stack', default=3, type=int, help='#modules per stack (nasbench101)')
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parser.add_argument('--num_labels', default=1, type=int, help='#classes (nasbench101)')
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# parser = argparse.ArgumentParser(description='NAS Without Training')
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# parser.add_argument('--data_loc', default='../cifardata/', type=str, help='dataset folder')
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# parser.add_argument('--api_loc', default='../NAS-Bench-201-v1_0-e61699.pth',
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# type=str, help='path to API')
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# parser.add_argument('--save_loc', default='results', type=str, help='folder to save results')
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# parser.add_argument('--save_string', default='naswot', type=str, help='prefix of results file')
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# parser.add_argument('--score', default='hook_logdet', type=str, help='the score to evaluate')
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# parser.add_argument('--nasspace', default='nasbench201', type=str, help='the nas search space to use')
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# parser.add_argument('--batch_size', default=128, type=int)
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# parser.add_argument('--repeat', default=1, type=int, help='how often to repeat a single image with a batch')
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# parser.add_argument('--augtype', default='none', type=str, help='which perturbations to use')
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# parser.add_argument('--sigma', default=0.05, type=float, help='noise level if augtype is "gaussnoise"')
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# parser.add_argument('--GPU', default='0', type=str)
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# parser.add_argument('--seed', default=1, type=int)
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# parser.add_argument('--init', default='', type=str)
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# parser.add_argument('--trainval', action='store_true')
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# parser.add_argument('--dropout', action='store_true')
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# parser.add_argument('--dataset', default='cifar10', type=str)
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# parser.add_argument('--maxofn', default=1, type=int, help='score is the max of this many evaluations of the network')
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# parser.add_argument('--n_samples', default=100, type=int)
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# parser.add_argument('--n_runs', default=500, type=int)
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# parser.add_argument('--stem_out_channels', default=16, type=int, help='output channels of stem convolution (nasbench101)')
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# parser.add_argument('--num_stacks', default=3, type=int, help='#stacks of modules (nasbench101)')
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# parser.add_argument('--num_modules_per_stack', default=3, type=int, help='#modules per stack (nasbench101)')
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# parser.add_argument('--num_labels', default=1, type=int, help='#classes (nasbench101)')
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args = parser.parse_args()
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os.environ['CUDA_VISIBLE_DEVICES'] = args.GPU
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# args = parser.parse_args()
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# os.environ['CUDA_VISIBLE_DEVICES'] = args.GPU
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# Reproducibility
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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random.seed(args.seed)
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np.random.seed(args.seed)
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torch.manual_seed(args.seed)
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# # Reproducibility
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# torch.backends.cudnn.deterministic = True
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# torch.backends.cudnn.benchmark = False
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# random.seed(args.seed)
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# np.random.seed(args.seed)
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# torch.manual_seed(args.seed)
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def get_batch_jacobian(net, x, target, device, args=None):
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@ -58,10 +58,16 @@ def get_batch_jacobian(net, x, target, device, args=None):
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return jacob, target.detach(), y.detach(), out.detach()
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def get_config_by_nodes(nodes):
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# check if the nodes[0] is a number
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if isinstance(nodes[0], int):
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num_to_op = ['input', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3', 'skip_connect', 'none', 'output']
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arch_str = '|' + num_to_op[nodes[1]] + '~0|+|' + \
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num_to_op[nodes[2]] + '~0|' + num_to_op[nodes[3]] + '~1|+|' + \
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num_to_op[nodes[4]] + '~0|' + num_to_op[nodes[5]] + '~1|' + num_to_op[nodes[6]] + '~2|'
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else:
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arch_str = '|' + nodes[1] + '~0|+|' + \
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nodes[2] + '~0|' + nodes[3] + '~1|+|' + \
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nodes[4] + '~0|' + nodes[5] + '~1|' + nodes[6] + '~2|'
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config = {
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'name': 'infer.tiny',
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'C': 16,
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@ -234,64 +240,64 @@ def get_nasbench201_idx_score(idx, train_loader, searchspace, args, device):
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print('final result')
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return np.nan
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class Args:
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pass
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args = Args()
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args.trainval = True
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args.augtype = 'none'
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args.repeat = 1
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args.score = 'hook_logdet'
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args.sigma = 0.05
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args.nasspace = 'nasbench201'
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args.batch_size = 128
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args.GPU = '0'
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args.dataset = 'cifar10-valid'
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args.api_loc = '/home/stud/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
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args.data_loc = '../cifardata/'
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args.seed = 777
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args.init = ''
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args.save_loc = 'results'
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args.save_string = 'naswot'
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args.dropout = False
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args.maxofn = 1
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args.n_samples = 100
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args.n_runs = 500
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args.stem_out_channels = 16
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args.num_stacks = 3
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args.num_modules_per_stack = 3
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args.num_labels = 1
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# class Args:
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# pass
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# args = Args()
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# args.trainval = True
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# args.augtype = 'none'
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# args.repeat = 1
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# args.score = 'hook_logdet'
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# args.sigma = 0.05
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# args.nasspace = 'nasbench201'
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# args.batch_size = 128
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# args.GPU = '0'
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# args.dataset = 'cifar10-valid'
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# args.api_loc = '/home/stud/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
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# args.data_loc = '../cifardata/'
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# args.seed = 777
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# args.init = ''
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# args.save_loc = 'results'
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# args.save_string = 'naswot'
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# args.dropout = False
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# args.maxofn = 1
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# args.n_samples = 100
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# args.n_runs = 500
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# args.stem_out_channels = 16
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# args.num_stacks = 3
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# args.num_modules_per_stack = 3
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# args.num_labels = 1
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if 'valid' in args.dataset:
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args.dataset = args.dataset.replace('-valid', '')
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print('start to get search space')
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start_time = time.time()
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print(get_config_by_nodes(nodes=[0,2,2,3,4,2,4,6]))
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end_time = time.time()
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start_time = time.time()
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searchspace = nasspace.get_search_space(args)
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end_time = time.time()
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print(f'search space time: {end_time - start_time}')
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train_loader = datasets.get_data(args.dataset, args.data_loc, args.trainval, args.batch_size, args.augtype, args.repeat, args)
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print('start to get score')
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print('5374')
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num_to_op = ['input', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3', 'skip_connect', 'none', 'output']
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start_time = time.time()
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print(get_nasbench201_nodes_score(nodes=[0,2,2,3,4,2,4,6],train_loader=train_loader, searchspace=searchspace, args=args, device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")))
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end_time = time.time()
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start_time = time.time()
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print(get_nasbench201_idx_score(5374,train_loader=train_loader, searchspace=searchspace, args=args, device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")))
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end_time = time.time()
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print(f'5374 time: {end_time - start_time}')
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print('5375')
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start_time = time.time()
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print(get_nasbench201_idx_score(5375,train_loader=train_loader, searchspace=searchspace, args=args, device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")))
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end_time = time.time()
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print(f'5375 time: {end_time - start_time}')
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print('5376')
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start_time = time.time()
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print(get_nasbench201_idx_score(5376,train_loader=train_loader, searchspace=searchspace, args=args, device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")))
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end_time = time.time()
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print(f'5376 time: {end_time - start_time}')
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# if 'valid' in args.dataset:
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# args.dataset = args.dataset.replace('-valid', '')
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# print('start to get search space')
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# start_time = time.time()
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# print(get_config_by_nodes(nodes=[0,2,2,3,4,2,4,6]))
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# end_time = time.time()
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# start_time = time.time()
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# searchspace = nasspace.get_search_space(args)
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# end_time = time.time()
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# print(f'search space time: {end_time - start_time}')
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# train_loader = datasets.get_data(args.dataset, args.data_loc, args.trainval, args.batch_size, args.augtype, args.repeat, args)
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# print('start to get score')
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# print('5374')
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# num_to_op = ['input', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3', 'skip_connect', 'none', 'output']
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# start_time = time.time()
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# print(get_nasbench201_nodes_score(nodes=[0,2,2,3,4,2,4,6],train_loader=train_loader, searchspace=searchspace, args=args, device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")))
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# end_time = time.time()
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# start_time = time.time()
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# print(get_nasbench201_idx_score(5374,train_loader=train_loader, searchspace=searchspace, args=args, device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")))
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# end_time = time.time()
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# print(f'5374 time: {end_time - start_time}')
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# print('5375')
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# start_time = time.time()
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# print(get_nasbench201_idx_score(5375,train_loader=train_loader, searchspace=searchspace, args=args, device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")))
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# end_time = time.time()
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# print(f'5375 time: {end_time - start_time}')
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# print('5376')
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# start_time = time.time()
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# print(get_nasbench201_idx_score(5376,train_loader=train_loader, searchspace=searchspace, args=args, device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")))
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# end_time = time.time()
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# print(f'5376 time: {end_time - start_time}')
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# device = "cuda:0"
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# dataset = dataset
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@ -3,5 +3,8 @@ from setuptools import setup, find_packages
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setup(
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name='naswot',
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version='0.1',
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packages=find_packages()
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packages=find_packages(),
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package_data={
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'naswot': ['config_utils/cifardata/*']
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}
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)
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