# Copyright 2021 Samsung Electronics Co., Ltd. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================= import pickle import torch import argparse from foresight.models import * from foresight.pruners import * from foresight.dataset import * from foresight.weight_initializers import init_net def get_num_classes(args): return 100 if args.dataset == 'cifar100' else 10 if args.dataset == 'cifar10' else 120 def parse_arguments(): parser = argparse.ArgumentParser(description='Zero-cost Metrics for NAS-Bench-201') parser.add_argument('--api_loc', default='data/NAS-Bench-201-v1_0-e61699.pth', type=str, help='path to API') parser.add_argument('--outdir', default='./', type=str, help='output directory') parser.add_argument('--init_w_type', type=str, default='none', help='weight initialization (before pruning) type [none, xavier, kaiming, zero]') parser.add_argument('--init_b_type', type=str, default='none', help='bias initialization (before pruning) type [none, xavier, kaiming, zero]') parser.add_argument('--batch_size', default=64, type=int) parser.add_argument('--dataset', type=str, default='cifar10', help='dataset to use [cifar10, cifar100, ImageNet16-120]') parser.add_argument('--gpu', type=int, default=0, help='GPU index to work on') parser.add_argument('--num_data_workers', type=int, default=2, help='number of workers for dataloaders') parser.add_argument('--dataload', type=str, default='random', help='random or grasp supported') parser.add_argument('--dataload_info', type=int, default=1, help='number of batches to use for random dataload or number of samples per class for grasp dataload') parser.add_argument('--seed', type=int, default=42, help='pytorch manual seed') parser.add_argument('--write_freq', type=int, default=1, help='frequency of write to file') parser.add_argument('--start', type=int, default=0, help='start index') parser.add_argument('--end', type=int, default=0, help='end index') parser.add_argument('--noacc', default=False, action='store_true', help='avoid loading NASBench2 api an instead load a pickle file with tuple (index, arch_str)') args = parser.parse_args() args.device = torch.device("cuda:"+str(args.gpu) if torch.cuda.is_available() else "cpu") return args if __name__ == '__main__': args = parse_arguments() if args.noacc: api = pickle.load(open(args.api_loc,'rb')) else: from nas_201_api import NASBench201API as API api = API(args.api_loc) torch.manual_seed(args.seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False train_loader, val_loader = get_cifar_dataloaders(args.batch_size, args.batch_size, args.dataset, args.num_data_workers) cached_res = [] pre='cf' if 'cifar' in args.dataset else 'im' pfn=f'nb2_{pre}{get_num_classes(args)}_seed{args.seed}_dl{args.dataload}_dlinfo{args.dataload_info}_initw{args.init_w_type}_initb{args.init_b_type}.p' op = os.path.join(args.outdir,pfn) args.end = len(api) if args.end == 0 else args.end #loop over nasbench2 archs for i, arch_str in enumerate(api): if i < args.start: continue if i >= args.end: break res = {'i':i, 'arch':arch_str} net = nasbench2.get_model_from_arch_str(arch_str, get_num_classes(args)) net.to(args.device) init_net(net, args.init_w_type, args.init_b_type) arch_str2 = nasbench2.get_arch_str_from_model(net) if arch_str != arch_str2: print(arch_str) print(arch_str2) raise ValueError measures = predictive.find_measures(net, train_loader, (args.dataload, args.dataload_info, get_num_classes(args)), args.device) res['logmeasures']= measures if not args.noacc: info = api.get_more_info(i, 'cifar10-valid' if args.dataset=='cifar10' else args.dataset, iepoch=None, hp='200', is_random=False) trainacc = info['train-accuracy'] valacc = info['valid-accuracy'] testacc = info['test-accuracy'] res['trainacc']=trainacc res['valacc']=valacc res['testacc']=testacc #print(res) cached_res.append(res) #write to file if i % args.write_freq == 0 or i == len(api)-1 or i == 10: print(f'writing {len(cached_res)} results to {op}') pf=open(op, 'ab') for cr in cached_res: pickle.dump(cr, pf) pf.close() cached_res = []