############################################################################## # NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size # ############################################################################## # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.07 # ############################################################################## # This file is used to train (all) architecture candidate in the size search # # space in NATS-Bench (sss) with different hyper-parameters. # # When use mode=new, it will automatically detect whether the checkpoint of # # a trial exists, if so, it will skip this trial. When use mode=cover, it # # will ignore the (possible) existing checkpoint, run each trial, and save. # # (NOTE): the topology for all candidates in sss is fixed as: ###################### # |nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|nor_conv_3x3~1|nor_conv_3x3~2| # ################################################################################################### # Please use the script of scripts/NATS-Bench/train-shapes.sh to run. # ############################################################################## import os, sys, time, torch, argparse from typing import List, Text, Dict, Any from PIL import ImageFile ImageFile.LOAD_TRUNCATED_IMAGES = True from copy import deepcopy from pathlib import Path lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve() if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir)) from config_utils import dict2config, load_config from procedures import bench_evaluate_for_seed from procedures import get_machine_info from datasets import get_datasets from log_utils import Logger, AverageMeter, time_string, convert_secs2time from utils import split_str2indexes def evaluate_all_datasets(channels: Text, datasets: List[Text], xpaths: List[Text], splits: List[Text], config_path: Text, seed: int, workers: int, logger): machine_info = get_machine_info() all_infos = {'info': machine_info} all_dataset_keys = [] # look all the dataset for dataset, xpath, split in zip(datasets, xpaths, splits): # the train and valid data train_data, valid_data, xshape, class_num = get_datasets(dataset, xpath, -1) # load the configuration if dataset == 'cifar10' or dataset == 'cifar100': split_info = load_config('configs/nas-benchmark/cifar-split.txt', None, None) elif dataset.startswith('ImageNet16'): split_info = load_config('configs/nas-benchmark/{:}-split.txt'.format(dataset), None, None) else: raise ValueError('invalid dataset : {:}'.format(dataset)) config = load_config(config_path, dict(class_num=class_num, xshape=xshape), logger) # check whether use the splitted validation set if bool(split): assert dataset == 'cifar10' ValLoaders = {'ori-test': torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, shuffle=False, num_workers=workers, pin_memory=True)} assert len(train_data) == len(split_info.train) + len(split_info.valid), 'invalid length : {:} vs {:} + {:}'.format(len(train_data), len(split_info.train), len(split_info.valid)) train_data_v2 = deepcopy(train_data) train_data_v2.transform = valid_data.transform valid_data = train_data_v2 # data loader train_loader = torch.utils.data.DataLoader(train_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.train), num_workers=workers, pin_memory=True) valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.valid), num_workers=workers, pin_memory=True) ValLoaders['x-valid'] = valid_loader else: # data loader train_loader = torch.utils.data.DataLoader(train_data, batch_size=config.batch_size, shuffle=True , num_workers=workers, pin_memory=True) valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, shuffle=False, num_workers=workers, pin_memory=True) if dataset == 'cifar10': ValLoaders = {'ori-test': valid_loader} elif dataset == 'cifar100': cifar100_splits = load_config('configs/nas-benchmark/cifar100-test-split.txt', None, None) ValLoaders = {'ori-test': valid_loader, 'x-valid' : torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xvalid), num_workers=workers, pin_memory=True), 'x-test' : torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xtest ), num_workers=workers, pin_memory=True) } elif dataset == 'ImageNet16-120': imagenet16_splits = load_config('configs/nas-benchmark/imagenet-16-120-test-split.txt', None, None) ValLoaders = {'ori-test': valid_loader, 'x-valid' : torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet16_splits.xvalid), num_workers=workers, pin_memory=True), 'x-test' : torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet16_splits.xtest ), num_workers=workers, pin_memory=True) } else: raise ValueError('invalid dataset : {:}'.format(dataset)) dataset_key = '{:}'.format(dataset) if bool(split): dataset_key = dataset_key + '-valid' logger.log('Evaluate ||||||| {:10s} ||||||| Train-Num={:}, Valid-Num={:}, Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(dataset_key, len(train_data), len(valid_data), len(train_loader), len(valid_loader), config.batch_size)) logger.log('Evaluate ||||||| {:10s} ||||||| Config={:}'.format(dataset_key, config)) for key, value in ValLoaders.items(): logger.log('Evaluate ---->>>> {:10s} with {:} batchs'.format(key, len(value))) # arch-index= 9930, arch=|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|nor_conv_3x3~1|nor_conv_3x3~2| # this genotype is the architecture with the highest accuracy on CIFAR-100 validation set genotype = '|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|nor_conv_3x3~1|nor_conv_3x3~2|' arch_config = dict2config(dict(name='infer.shape.tiny', channels=channels, genotype=genotype, num_classes=class_num), None) results = bench_evaluate_for_seed(arch_config, config, train_loader, ValLoaders, seed, logger) all_infos[dataset_key] = results all_dataset_keys.append( dataset_key ) all_infos['all_dataset_keys'] = all_dataset_keys return all_infos def main(save_dir: Path, workers: int, datasets: List[Text], xpaths: List[Text], splits: List[int], seeds: List[int], nets: List[str], opt_config: Dict[Text, Any], to_evaluate_indexes: tuple, cover_mode: bool): log_dir = save_dir / 'logs' log_dir.mkdir(parents=True, exist_ok=True) logger = Logger(str(log_dir), os.getpid(), False) logger.log('xargs : seeds = {:}'.format(seeds)) logger.log('xargs : cover_mode = {:}'.format(cover_mode)) logger.log('-' * 100) logger.log( 'Start evaluating range =: {:06d} - {:06d}'.format(min(to_evaluate_indexes), max(to_evaluate_indexes)) +'({:} in total) / {:06d} with cover-mode={:}'.format(len(to_evaluate_indexes), len(nets), cover_mode)) for i, (dataset, xpath, split) in enumerate(zip(datasets, xpaths, splits)): logger.log( '--->>> Evaluate {:}/{:} : dataset={:9s}, path={:}, split={:}'.format(i, len(datasets), dataset, xpath, split)) logger.log('--->>> optimization config : {:}'.format(opt_config)) start_time, epoch_time = time.time(), AverageMeter() for i, index in enumerate(to_evaluate_indexes): channelstr = nets[index] logger.log('\n{:} evaluate {:06d}/{:06d} ({:06d}/{:06d})-th arch [seeds={:}] {:}'.format(time_string(), i, len(to_evaluate_indexes), index, len(nets), seeds, '-' * 15)) logger.log('{:} {:} {:}'.format('-' * 15, channelstr, '-' * 15)) # test this arch on different datasets with different seeds has_continue = False for seed in seeds: to_save_name = save_dir / 'arch-{:06d}-seed-{:04d}.pth'.format(index, seed) if to_save_name.exists(): if cover_mode: logger.log('Find existing file : {:}, remove it before evaluation'.format(to_save_name)) os.remove(str(to_save_name)) else: logger.log('Find existing file : {:}, skip this evaluation'.format(to_save_name)) has_continue = True continue results = evaluate_all_datasets(channelstr, datasets, xpaths, splits, opt_config, seed, workers, logger) torch.save(results, to_save_name) logger.log('\n{:} evaluate {:06d}/{:06d} ({:06d}/{:06d})-th arch [seeds={:}] ===>>> {:}'.format(time_string(), i, len(to_evaluate_indexes), index, len(nets), seeds, to_save_name)) # measure elapsed time if not has_continue: epoch_time.update(time.time() - start_time) start_time = time.time() need_time = 'Time Left: {:}'.format(convert_secs2time(epoch_time.avg * (len(to_evaluate_indexes) - i - 1), True)) logger.log('This arch costs : {:}'.format(convert_secs2time(epoch_time.val, True))) logger.log('{:}'.format('*' * 100)) logger.log('{:} {:74s} {:}'.format('*' * 10, '{:06d}/{:06d} ({:06d}/{:06d})-th done, left {:}'.format(i, len( to_evaluate_indexes), index, len(nets), need_time), '*' * 10)) logger.log('{:}'.format('*' * 100)) logger.close() def traverse_net(candidates: List[int], N: int): nets = [''] for i in range(N): new_nets = [] for net in nets: for C in candidates: new_nets.append(str(C) if net == '' else "{:}:{:}".format(net,C)) nets = new_nets return nets def filter_indexes(xlist, mode, save_dir, seeds): all_indexes = [] for index in xlist: if mode == 'cover': all_indexes.append(index) else: for seed in seeds: temp_path = save_dir / 'arch-{:06d}-seed-{:04d}.pth'.format(index, seed) if not temp_path.exists(): all_indexes.append(index) break print('{:} [FILTER-INDEXES] : there are {:}/{:} architectures in total'.format(time_string(), len(all_indexes), len(xlist))) SLURM_PROCID, SLURM_NTASKS = 'SLURM_PROCID', 'SLURM_NTASKS' if SLURM_PROCID in os.environ and SLURM_NTASKS in os.environ: # run on the slurm proc_id, ntasks = int(os.environ[SLURM_PROCID]), int(os.environ[SLURM_NTASKS]) assert 0 <= proc_id < ntasks, 'invalid proc_id {:} vs ntasks {:}'.format(proc_id, ntasks) scales = [int(float(i)/ntasks*len(all_indexes)) for i in range(ntasks)] + [len(all_indexes)] per_job = [] for i in range(ntasks): xs, xe = min(max(scales[i],0), len(all_indexes)-1), min(max(scales[i+1]-1,0), len(all_indexes)-1) per_job.append((xs, xe)) for i, srange in enumerate(per_job): print(' -->> {:2d}/{:02d} : {:}'.format(i, ntasks, srange)) current_range = per_job[proc_id] all_indexes = [all_indexes[i] for i in range(current_range[0], current_range[1]+1)] # set the device id device = proc_id % torch.cuda.device_count() torch.cuda.set_device(device) print(' set the device id = {:}'.format(device)) print('{:} [FILTER-INDEXES] : after filtering there are {:} architectures in total'.format(time_string(), len(all_indexes))) return all_indexes if __name__ == '__main__': parser = argparse.ArgumentParser(description='NATS-Bench (size search space)', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('--mode', type=str, required=True, choices=['new', 'cover'], help='The script mode.') parser.add_argument('--save_dir', type=str, default='output/NATS-Bench-size', help='Folder to save checkpoints and log.') parser.add_argument('--candidateC', type=int, nargs='+', default=[8, 16, 24, 32, 40, 48, 56, 64], help='.') parser.add_argument('--num_layers', type=int, default=5, help='The number of layers in a network.') parser.add_argument('--check_N', type=int, default=32768, help='For safety.') # use for train the model parser.add_argument('--workers', type=int, default=8, help='The number of data loading workers (default: 2)') parser.add_argument('--srange' , type=str, required=True, help='The range of models to be evaluated') parser.add_argument('--datasets', type=str, nargs='+', help='The applied datasets.') parser.add_argument('--xpaths', type=str, nargs='+', help='The root path for this dataset.') parser.add_argument('--splits', type=int, nargs='+', help='The root path for this dataset.') parser.add_argument('--hyper', type=str, default='12', choices=['01', '12', '90'], help='The tag for hyper-parameters.') parser.add_argument('--seeds' , type=int, nargs='+', help='The range of models to be evaluated') args = parser.parse_args() nets = traverse_net(args.candidateC, args.num_layers) if len(nets) != args.check_N: raise ValueError('Pre-num-check failed : {:} vs {:}'.format(len(nets), args.check_N)) opt_config = './configs/nas-benchmark/hyper-opts/{:}E.config'.format(args.hyper) if not os.path.isfile(opt_config): raise ValueError('{:} is not a file.'.format(opt_config)) save_dir = Path(args.save_dir) / 'raw-data-{:}'.format(args.hyper) save_dir.mkdir(parents=True, exist_ok=True) to_evaluate_indexes = split_str2indexes(args.srange, args.check_N, 5) if not len(args.seeds): raise ValueError('invalid length of seeds args: {:}'.format(args.seeds)) if not (len(args.datasets) == len(args.xpaths) == len(args.splits)): raise ValueError('invalid infos : {:} vs {:} vs {:}'.format(len(args.datasets), len(args.xpaths), len(args.splits))) if args.workers <= 0: raise ValueError('invalid number of workers : {:}'.format(args.workers)) target_indexes = filter_indexes(to_evaluate_indexes, args.mode, save_dir, args.seeds) assert torch.cuda.is_available(), 'CUDA is not available.' torch.backends.cudnn.enabled = True torch.backends.cudnn.deterministic = True torch.set_num_threads(args.workers) main(save_dir, args.workers, args.datasets, args.xpaths, args.splits, tuple(args.seeds), nets, opt_config, target_indexes, args.mode == 'cover')