134 lines
4.8 KiB
Python
134 lines
4.8 KiB
Python
# Copyright 2021 Samsung Electronics Co., Ltd.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# =============================================================================
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import pickle
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import torch
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import argparse
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import json
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import numpy as np
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from thop import profile
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from foresight.models import *
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from foresight.pruners import *
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from foresight.dataset import *
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def get_num_classes(args):
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return 100 if args.dataset == 'cifar100' else 10 if args.dataset == 'cifar10' else 120
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def parse_arguments():
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parser = argparse.ArgumentParser(description='Zero-cost Metrics for NAS-Bench-101')
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parser.add_argument('--api_loc', default='../data/nasbench_only108.tfrecord',
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type=str, help='path to API')
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parser.add_argument('--json_loc', default='data/all_graphs.json',
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type=str, help='path to JSON database')
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parser.add_argument('--outdir', default='./',
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type=str, help='output directory')
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parser.add_argument('--outfname', default='test',
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type=str, help='output filename')
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parser.add_argument('--batch_size', default=256, type=int)
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parser.add_argument('--dataset', type=str, default='cifar10',
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help='dataset to use [cifar10, cifar100, ImageNet16-120]')
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parser.add_argument('--gpu', type=int, default=0, help='GPU index to work on')
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parser.add_argument('--num_data_workers', type=int, default=2, help='number of workers for dataloaders')
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parser.add_argument('--dataload', type=str, default='random', help='random or grasp supported')
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parser.add_argument('--dataload_info', type=int, default=1,
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help='number of batches to use for random dataload or number of samples per class for grasp dataload')
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parser.add_argument('--start', type=int, default=5, help='start index')
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parser.add_argument('--end', type=int, default=10, help='end index')
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parser.add_argument('--write_freq', type=int, default=100, help='frequency of write to file')
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args = parser.parse_args()
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args.device = torch.device("cuda:" + str(args.gpu) if torch.cuda.is_available() else "cpu")
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return args
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def get_op_names(v):
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o = []
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for op in v:
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if op == -1:
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o.append('input')
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elif op == -2:
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o.append('output')
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elif op == 0:
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o.append('conv3x3-bn-relu')
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elif op == 1:
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o.append('conv1x1-bn-relu')
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elif op == 2:
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o.append('maxpool3x3')
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return o
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if __name__ == '__main__':
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args = parse_arguments()
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# nasbench = api.NASBench(args.api_loc)
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models = json.load(open(args.json_loc))
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print(f'Running models {args.start} to {args.end} out of {len(models.keys())}')
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train_loader, val_loader = get_cifar_dataloaders(args.batch_size, args.batch_size, args.dataset,
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args.num_data_workers)
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all_points = []
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pre = 'cf' if 'cifar' in args.dataset else 'im'
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if args.outfname == 'test':
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fn = f'nb1_{pre}{get_num_classes(args)}.p'
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else:
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fn = f'{args.outfname}.p'
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op = os.path.join(args.outdir, fn)
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print('outfile =', op)
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first = True
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# loop over nasbench1 archs (k=hash, v=[adj_matrix, ops])
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idx = 0
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cached_res = []
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for k, v in models.items():
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if idx < args.start:
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idx += 1
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continue
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if idx >= args.end:
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break
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print(f'idx = {idx}')
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idx += 1
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res = {}
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res['hash'] = k
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# model
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spec = nasbench1_spec._ToModelSpec(v[0], get_op_names(v[1]))
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net = nasbench1.Network(spec, stem_out=128, num_stacks=3, num_mods=3, num_classes=get_num_classes(args))
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net.to(args.device)
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measures = predictive.find_measures(net,
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train_loader,
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(args.dataload, args.dataload_info, get_num_classes(args)),
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args.device)
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res['logmeasures'] = measures
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print(res)
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cached_res.append(res)
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# write to file
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if idx % args.write_freq == 0 or idx == args.end or idx == args.start + 10:
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print(f'writing {len(cached_res)} results to {op}')
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pf = open(op, 'ab')
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for cr in cached_res:
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pickle.dump(cr, pf)
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pf.close()
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cached_res = []
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