120 lines
6.7 KiB
Python
120 lines
6.7 KiB
Python
##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
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##############################################################################
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import os, sys, time, glob, random, argparse
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import numpy as np, collections
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from copy import deepcopy
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import torch
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import torch.nn as nn
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from pathlib import Path
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lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
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if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
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from config_utils import load_config, dict2config, configure2str
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from datasets import get_datasets, SearchDataset
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from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler
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from utils import get_model_infos, obtain_accuracy
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from log_utils import AverageMeter, time_string, convert_secs2time
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from models import get_search_spaces
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from nas_201_api import NASBench201API as API
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from R_EA import train_and_eval, random_architecture_func
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def main(xargs, nas_bench):
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assert torch.cuda.is_available(), 'CUDA is not available.'
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torch.backends.cudnn.enabled = True
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torch.backends.cudnn.benchmark = False
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torch.backends.cudnn.deterministic = True
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torch.set_num_threads( xargs.workers )
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prepare_seed(xargs.rand_seed)
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logger = prepare_logger(args)
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if xargs.dataset == 'cifar10':
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dataname = 'cifar10-valid'
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else:
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dataname = xargs.dataset
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if xargs.data_path is not None:
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train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
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split_Fpath = 'configs/nas-benchmark/cifar-split.txt'
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cifar_split = load_config(split_Fpath, None, None)
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train_split, valid_split = cifar_split.train, cifar_split.valid
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logger.log('Load split file from {:}'.format(split_Fpath))
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config_path = 'configs/nas-benchmark/algos/R-EA.config'
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config = load_config(config_path, {'class_num': class_num, 'xshape': xshape}, logger)
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# To split data
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train_data_v2 = deepcopy(train_data)
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train_data_v2.transform = valid_data.transform
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valid_data = train_data_v2
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search_data = SearchDataset(xargs.dataset, train_data, train_split, valid_split)
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# data loader
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train_loader = torch.utils.data.DataLoader(train_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split) , num_workers=xargs.workers, pin_memory=True)
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valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split), num_workers=xargs.workers, pin_memory=True)
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logger.log('||||||| {:10s} ||||||| Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(train_loader), len(valid_loader), config.batch_size))
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logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config))
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extra_info = {'config': config, 'train_loader': train_loader, 'valid_loader': valid_loader}
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else:
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config_path = 'configs/nas-benchmark/algos/R-EA.config'
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config = load_config(config_path, None, logger)
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logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config))
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extra_info = {'config': config, 'train_loader': None, 'valid_loader': None}
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search_space = get_search_spaces('cell', xargs.search_space_name)
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random_arch = random_architecture_func(xargs.max_nodes, search_space)
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#x =random_arch() ; y = mutate_arch(x)
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x_start_time = time.time()
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logger.log('{:} use nas_bench : {:}'.format(time_string(), nas_bench))
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best_arch, best_acc, total_time_cost, history = None, -1, 0, []
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#for idx in range(xargs.random_num):
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while total_time_cost < xargs.time_budget:
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arch = random_arch()
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accuracy, cost_time = train_and_eval(arch, nas_bench, extra_info, dataname)
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if total_time_cost + cost_time > xargs.time_budget: break
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else: total_time_cost += cost_time
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history.append(arch)
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if best_arch is None or best_acc < accuracy:
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best_acc, best_arch = accuracy, arch
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logger.log('[{:03d}] : {:} : accuracy = {:.2f}%'.format(len(history), arch, accuracy))
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logger.log('{:} best arch is {:}, accuracy = {:.2f}%, visit {:} archs with {:.1f} s (real-cost = {:.3f} s).'.format(time_string(), best_arch, best_acc, len(history), total_time_cost, time.time()-x_start_time))
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info = nas_bench.query_by_arch( best_arch )
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if info is None: logger.log('Did not find this architecture : {:}.'.format(best_arch))
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else : logger.log('{:}'.format(info))
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logger.log('-'*100)
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logger.close()
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return logger.log_dir, nas_bench.query_index_by_arch( best_arch )
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if __name__ == '__main__':
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parser = argparse.ArgumentParser("Regularized Evolution Algorithm")
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parser.add_argument('--data_path', type=str, help='Path to dataset')
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parser.add_argument('--dataset', type=str, choices=['cifar10', 'cifar100', 'ImageNet16-120'], help='Choose between Cifar10/100 and ImageNet-16.')
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# channels and number-of-cells
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parser.add_argument('--search_space_name', type=str, help='The search space name.')
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parser.add_argument('--max_nodes', type=int, help='The maximum number of nodes.')
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parser.add_argument('--channel', type=int, help='The number of channels.')
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parser.add_argument('--num_cells', type=int, help='The number of cells in one stage.')
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#parser.add_argument('--random_num', type=int, help='The number of random selected architectures.')
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parser.add_argument('--time_budget', type=int, help='The total time cost budge for searching (in seconds).')
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# log
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parser.add_argument('--workers', type=int, default=2, help='number of data loading workers (default: 2)')
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parser.add_argument('--save_dir', type=str, help='Folder to save checkpoints and log.')
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parser.add_argument('--arch_nas_dataset', type=str, help='The path to load the architecture dataset (tiny-nas-benchmark).')
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parser.add_argument('--print_freq', type=int, help='print frequency (default: 200)')
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parser.add_argument('--rand_seed', type=int, help='manual seed')
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args = parser.parse_args()
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#if args.rand_seed is None or args.rand_seed < 0: args.rand_seed = random.randint(1, 100000)
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if args.arch_nas_dataset is None or not os.path.isfile(args.arch_nas_dataset):
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nas_bench = None
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else:
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print ('{:} build NAS-Benchmark-API from {:}'.format(time_string(), args.arch_nas_dataset))
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nas_bench = API(args.arch_nas_dataset)
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if args.rand_seed < 0:
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save_dir, all_indexes, num = None, [], 500
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for i in range(num):
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print ('{:} : {:03d}/{:03d}'.format(time_string(), i, num))
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args.rand_seed = random.randint(1, 100000)
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save_dir, index = main(args, nas_bench)
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all_indexes.append( index )
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torch.save(all_indexes, save_dir / 'results.pth')
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else:
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main(args, nas_bench)
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