import argparse import nasspace import datasets import random import numpy as np import torch import os from scores import get_score_func from scipy import stats from pycls.models.nas.nas import Cell from utils import add_dropout, init_network parser = argparse.ArgumentParser(description='NAS Without Training') parser.add_argument('--data_loc', default='../cifardata/', type=str, help='dataset folder') parser.add_argument('--api_loc', default='../NAS-Bench-201-v1_0-e61699.pth', type=str, help='path to API') parser.add_argument('--save_loc', default='results', type=str, help='folder to save results') parser.add_argument('--save_string', default='naswot', type=str, help='prefix of results file') parser.add_argument('--score', default='hook_logdet', type=str, help='the score to evaluate') parser.add_argument('--nasspace', default='nasbench201', type=str, help='the nas search space to use') parser.add_argument('--batch_size', default=128, type=int) parser.add_argument('--repeat', default=1, type=int, help='how often to repeat a single image with a batch') parser.add_argument('--augtype', default='none', type=str, help='which perturbations to use') parser.add_argument('--sigma', default=0.05, type=float, help='noise level if augtype is "gaussnoise"') parser.add_argument('--GPU', default='0', type=str) parser.add_argument('--seed', default=1, type=int) parser.add_argument('--init', default='', type=str) parser.add_argument('--trainval', action='store_true') parser.add_argument('--dropout', action='store_true') parser.add_argument('--dataset', default='cifar10', type=str) parser.add_argument('--maxofn', default=1, type=int, help='score is the max of this many evaluations of the network') parser.add_argument('--n_samples', default=100, type=int) parser.add_argument('--n_runs', default=500, type=int) parser.add_argument('--stem_out_channels', default=16, type=int, help='output channels of stem convolution (nasbench101)') parser.add_argument('--num_stacks', default=3, type=int, help='#stacks of modules (nasbench101)') parser.add_argument('--num_modules_per_stack', default=3, type=int, help='#modules per stack (nasbench101)') parser.add_argument('--num_labels', default=1, type=int, help='#classes (nasbench101)') args = parser.parse_args() os.environ['CUDA_VISIBLE_DEVICES'] = args.GPU # Reproducibility torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) def get_batch_jacobian(net, x, target, device, args=None): net.zero_grad() x.requires_grad_(True) y, out = net(x) y.backward(torch.ones_like(y)) jacob = x.grad.detach() return jacob, target.detach(), y.detach(), out.detach() device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") savedataset = args.dataset dataset = 'fake' if 'fake' in args.dataset else args.dataset args.dataset = args.dataset.replace('fake', '') if args.dataset == 'cifar10': args.dataset = args.dataset + '-valid' searchspace = nasspace.get_search_space(args) if 'valid' in args.dataset: args.dataset = args.dataset.replace('-valid', '') train_loader = datasets.get_data(args.dataset, args.data_loc, args.trainval, args.batch_size, args.augtype, args.repeat, args) os.makedirs(args.save_loc, exist_ok=True) filename = f'{args.save_loc}/{args.save_string}_{args.score}_{args.nasspace}_{savedataset}{"_" + args.init + "_" if args.init != "" else args.init}_{"_dropout" if args.dropout else ""}_{args.augtype}_{args.sigma}_{args.repeat}_{args.trainval}_{args.batch_size}_{args.maxofn}_{args.seed}' accfilename = f'{args.save_loc}/{args.save_string}_accs_{args.nasspace}_{savedataset}_{args.trainval}' if args.dataset == 'cifar10': acc_type = 'ori-test' val_acc_type = 'x-valid' else: acc_type = 'x-test' val_acc_type = 'x-valid' scores = np.zeros(len(searchspace)) try: accs = np.load(accfilename + '.npy') except: accs = np.zeros(len(searchspace)) for i, (uid, network) in enumerate(searchspace): # Reproducibility try: if args.dropout: add_dropout(network, args.sigma) if args.init != '': init_network(network, args.init) if 'hook_' in args.score: network.K = np.zeros((args.batch_size, args.batch_size)) def counting_forward_hook(module, inp, out): try: if not module.visited_backwards: return if isinstance(inp, tuple): inp = inp[0] inp = inp.view(inp.size(0), -1) x = (inp > 0).float() K = x @ x.t() K2 = (1.-x) @ (1.-x.t()) network.K = network.K + K.cpu().numpy() + K2.cpu().numpy() except: pass def counting_backward_hook(module, inp, out): module.visited_backwards = True for name, module in network.named_modules(): if 'ReLU' in str(type(module)): #hooks[name] = module.register_forward_hook(counting_hook) module.register_forward_hook(counting_forward_hook) module.register_backward_hook(counting_backward_hook) network = network.to(device) random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) s = [] for j in range(args.maxofn): data_iterator = iter(train_loader) x, target = next(data_iterator) x2 = torch.clone(x) x2 = x2.to(device) x, target = x.to(device), target.to(device) jacobs, labels, y, out = get_batch_jacobian(network, x, target, device, args) if 'hook_' in args.score: network(x2.to(device)) s.append(get_score_func(args.score)(network.K, target)) else: s.append(get_score_func(args.score)(jacobs, labels)) scores[i] = np.mean(s) accs[i] = searchspace.get_final_accuracy(uid, acc_type, args.trainval) accs_ = accs[~np.isnan(scores)] scores_ = scores[~np.isnan(scores)] numnan = np.isnan(scores).sum() tau, p = stats.kendalltau(accs_[:max(i-numnan, 1)], scores_[:max(i-numnan, 1)]) print(f'{tau}') if i % 1000 == 0: np.save(filename, scores) np.save(accfilename, accs) except Exception as e: print(e) accs[i] = searchspace.get_final_accuracy(uid, acc_type, args.trainval) scores[i] = np.nan np.save(filename, scores) np.save(accfilename, accs)