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HamsterMimi
2023-05-04 13:42:06 +08:00
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# 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
import json
import numpy as np
from thop import profile
from foresight.models import *
from foresight.pruners import *
from foresight.dataset import *
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-101')
parser.add_argument('--api_loc', default='../data/nasbench_only108.tfrecord',
type=str, help='path to API')
parser.add_argument('--json_loc', default='data/all_graphs.json',
type=str, help='path to JSON database')
parser.add_argument('--outdir', default='./',
type=str, help='output directory')
parser.add_argument('--outfname', default='test',
type=str, help='output filename')
parser.add_argument('--batch_size', default=256, 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('--start', type=int, default=5, help='start index')
parser.add_argument('--end', type=int, default=10, help='end index')
parser.add_argument('--write_freq', type=int, default=100, help='frequency of write to file')
args = parser.parse_args()
args.device = torch.device("cuda:" + str(args.gpu) if torch.cuda.is_available() else "cpu")
return args
def get_op_names(v):
o = []
for op in v:
if op == -1:
o.append('input')
elif op == -2:
o.append('output')
elif op == 0:
o.append('conv3x3-bn-relu')
elif op == 1:
o.append('conv1x1-bn-relu')
elif op == 2:
o.append('maxpool3x3')
return o
if __name__ == '__main__':
args = parse_arguments()
# nasbench = api.NASBench(args.api_loc)
models = json.load(open(args.json_loc))
print(f'Running models {args.start} to {args.end} out of {len(models.keys())}')
train_loader, val_loader = get_cifar_dataloaders(args.batch_size, args.batch_size, args.dataset,
args.num_data_workers)
all_points = []
pre = 'cf' if 'cifar' in args.dataset else 'im'
if args.outfname == 'test':
fn = f'nb1_{pre}{get_num_classes(args)}.p'
else:
fn = f'{args.outfname}.p'
op = os.path.join(args.outdir, fn)
print('outfile =', op)
first = True
# loop over nasbench1 archs (k=hash, v=[adj_matrix, ops])
idx = 0
cached_res = []
for k, v in models.items():
if idx < args.start:
idx += 1
continue
if idx >= args.end:
break
print(f'idx = {idx}')
idx += 1
res = {}
res['hash'] = k
# model
spec = nasbench1_spec._ToModelSpec(v[0], get_op_names(v[1]))
net = nasbench1.Network(spec, stem_out=128, num_stacks=3, num_mods=3, num_classes=get_num_classes(args))
net.to(args.device)
measures = predictive.find_measures(net,
train_loader,
(args.dataload, args.dataload_info, get_num_classes(args)),
args.device)
res['logmeasures'] = measures
print(res)
cached_res.append(res)
# write to file
if idx % args.write_freq == 0 or idx == args.end or idx == args.start + 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 = []

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import argparse
import os
import time
from foresight.dataset import *
from foresight.models import nasbench2
from foresight.pruners import predictive
from foresight.weight_initializers import init_net
from models import get_cell_based_tiny_net
import pickle
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, one]')
parser.add_argument('--init_b_type', type=str, default='none',
help='bias initialization (before pruning) type [none, xavier, kaiming, zero, one]')
parser.add_argument('--batch_size', default=64, type=int)
parser.add_argument('--dataset', type=str, default='ImageNet16-120',
help='dataset to use [cifar10, cifar100, ImageNet16-120]')
parser.add_argument('--gpu', type=int, default=5, help='GPU index to work on')
parser.add_argument('--data_size', type=int, default=32, help='data_size')
parser.add_argument('--num_data_workers', type=int, default=2, help='number of workers for dataloaders')
parser.add_argument('--dataload', type=str, default='appoint', help='random, grasp, appoint 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()
print(args.device)
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, resize=args.data_size)
x, y = next(iter(train_loader))
# random data
# x = torch.rand((args.batch_size, 3, args.data_size, args.data_size))
# y = 0
cached_res = []
pre = 'cf' if 'cifar' in args.dataset else 'im'
pfn = f'nb2_{args.search_space}_{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}_{args.batch_size}.p'
op = os.path.join(args.outdir, pfn)
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 >= end:
break
res = {'i': i, 'arch': arch_str}
# print(arch_str)
if args.search_space == 'tss':
net = nasbench2.get_model_from_arch_str(arch_str, get_num_classes(args))
arch_str2 = nasbench2.get_arch_str_from_model(net)
if arch_str != arch_str2:
print(arch_str)
print(arch_str2)
raise ValueError
elif args.search_space == 'sss':
config = api.get_net_config(i, args.dataset)
# print(config)
net = get_cell_based_tiny_net(config)
net.to(args.device)
# print(net)
init_net(net, args.init_w_type, args.init_b_type)
# print(x.size(), y)
measures = get_score(net, x, i, args.device)
res['meco'] = 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 = []