125 lines
5.3 KiB
Python
125 lines
5.3 KiB
Python
# 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 = []
|
|
|