################################################## # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # ######################################################## # python exps/NAS-Bench-201/test-correlation.py --api_path $HOME/.torch/NAS-Bench-201-v1_0-e61699.pth ######################################################## import os, sys, time, glob, random, argparse import numpy as np from copy import deepcopy from tqdm import tqdm import torch import torch.nn as nn 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 load_config, dict2config, configure2str from datasets import get_datasets, SearchDataset from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler from utils import get_model_infos, obtain_accuracy from log_utils import AverageMeter, time_string, convert_secs2time from models import get_cell_based_tiny_net, get_search_spaces, CellStructure from nas_201_api import NASBench201API as API def valid_func(xloader, network, criterion): data_time, batch_time = AverageMeter(), AverageMeter() arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter() network.eval() end = time.time() with torch.no_grad(): for step, (arch_inputs, arch_targets) in enumerate(xloader): arch_targets = arch_targets.cuda(non_blocking=True) # measure data loading time data_time.update(time.time() - end) # prediction _, logits = network(arch_inputs) arch_loss = criterion(logits, arch_targets) # record arch_prec1, arch_prec5 = obtain_accuracy(logits.data, arch_targets.data, topk=(1, 5)) arch_losses.update(arch_loss.item(), arch_inputs.size(0)) arch_top1.update (arch_prec1.item(), arch_inputs.size(0)) arch_top5.update (arch_prec5.item(), arch_inputs.size(0)) # measure elapsed time batch_time.update(time.time() - end) end = time.time() return arch_losses.avg, arch_top1.avg, arch_top5.avg def main(xargs): assert torch.cuda.is_available(), 'CUDA is not available.' torch.backends.cudnn.enabled = True torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True torch.set_num_threads( xargs.workers ) prepare_seed(xargs.rand_seed) logger = prepare_logger(args) train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1) if xargs.dataset == 'cifar10' or xargs.dataset == 'cifar100': split_Fpath = 'configs/nas-benchmark/cifar-split.txt' cifar_split = load_config(split_Fpath, None, None) train_split, valid_split = cifar_split.train, cifar_split.valid logger.log('Load split file from {:}'.format(split_Fpath)) elif xargs.dataset.startswith('ImageNet16'): split_Fpath = 'configs/nas-benchmark/{:}-split.txt'.format(xargs.dataset) imagenet16_split = load_config(split_Fpath, None, None) train_split, valid_split = imagenet16_split.train, imagenet16_split.valid logger.log('Load split file from {:}'.format(split_Fpath)) else: raise ValueError('invalid dataset : {:}'.format(xargs.dataset)) config_path = 'configs/nas-benchmark/algos/DARTS.config' config = load_config(config_path, {'class_num': class_num, 'xshape': xshape}, logger) # To split data train_data_v2 = deepcopy(train_data) train_data_v2.transform = valid_data.transform valid_data = train_data_v2 search_data = SearchDataset(xargs.dataset, train_data, train_split, valid_split) # data loader search_loader = torch.utils.data.DataLoader(search_data, batch_size=config.batch_size, shuffle=True , num_workers=xargs.workers, pin_memory=True) 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) logger.log('||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(search_loader), len(valid_loader), config.batch_size)) logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config)) search_space = get_search_spaces('cell', xargs.search_space_name) model_config = dict2config({'name': 'DARTS-V2', 'C': xargs.channel, 'N': xargs.num_cells, 'max_nodes': xargs.max_nodes, 'num_classes': class_num, 'space' : search_space}, None) search_model = get_cell_based_tiny_net(model_config) logger.log('search-model :\n{:}'.format(search_model)) w_optimizer, w_scheduler, criterion = get_optim_scheduler(search_model.get_weights(), config) a_optimizer = torch.optim.Adam(search_model.get_alphas(), lr=xargs.arch_learning_rate, betas=(0.5, 0.999), weight_decay=xargs.arch_weight_decay) logger.log('w-optimizer : {:}'.format(w_optimizer)) logger.log('a-optimizer : {:}'.format(a_optimizer)) logger.log('w-scheduler : {:}'.format(w_scheduler)) logger.log('criterion : {:}'.format(criterion)) flop, param = get_model_infos(search_model, xshape) #logger.log('{:}'.format(search_model)) logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param)) if xargs.arch_nas_dataset is None: api = None else: api = API(xargs.arch_nas_dataset) logger.log('{:} create API = {:} done'.format(time_string(), api)) last_info, model_base_path, model_best_path = logger.path('info'), logger.path('model'), logger.path('best') network, criterion = torch.nn.DataParallel(search_model).cuda(), criterion.cuda() logger.close() def check_unique_arch(meta_file): api = API(str(meta_file)) arch_strs = deepcopy(api.meta_archs) xarchs = [CellStructure.str2structure(x) for x in arch_strs] def get_unique_matrix(archs, consider_zero): UniquStrs = [arch.to_unique_str(consider_zero) for arch in archs] print ('{:} create unique-string ({:}/{:}) done'.format(time_string(), len(set(UniquStrs)), len(UniquStrs))) Unique2Index = dict() for index, xstr in enumerate(UniquStrs): if xstr not in Unique2Index: Unique2Index[xstr] = list() Unique2Index[xstr].append( index ) sm_matrix = torch.eye(len(archs)).bool() for _, xlist in Unique2Index.items(): for i in xlist: for j in xlist: sm_matrix[i,j] = True unique_ids, unique_num = [-1 for _ in archs], 0 for i in range(len(unique_ids)): if unique_ids[i] > -1: continue neighbours = sm_matrix[i].nonzero().view(-1).tolist() for nghb in neighbours: assert unique_ids[nghb] == -1, 'impossible' unique_ids[nghb] = unique_num unique_num += 1 return sm_matrix, unique_ids, unique_num print ('There are {:} valid-archs'.format( sum(arch.check_valid() for arch in xarchs) )) sm_matrix, uniqueIDs, unique_num = get_unique_matrix(xarchs, None) print ('{:} There are {:} unique architectures (considering nothing).'.format(time_string(), unique_num)) sm_matrix, uniqueIDs, unique_num = get_unique_matrix(xarchs, False) print ('{:} There are {:} unique architectures (not considering zero).'.format(time_string(), unique_num)) sm_matrix, uniqueIDs, unique_num = get_unique_matrix(xarchs, True) print ('{:} There are {:} unique architectures (considering zero).'.format(time_string(), unique_num)) def check_cor_for_bandit(meta_file, test_epoch, use_less_or_not, is_rand=True, need_print=False): if isinstance(meta_file, API): api = meta_file else: api = API(str(meta_file)) cifar10_currs = [] cifar10_valid = [] cifar10_test = [] cifar100_valid = [] cifar100_test = [] imagenet_test = [] imagenet_valid = [] for idx, arch in enumerate(api): results = api.get_more_info(idx, 'cifar10-valid' , test_epoch-1, use_less_or_not, is_rand) cifar10_currs.append( results['valid-accuracy'] ) # --->>>>> results = api.get_more_info(idx, 'cifar10-valid' , None, False, is_rand) cifar10_valid.append( results['valid-accuracy'] ) results = api.get_more_info(idx, 'cifar10' , None, False, is_rand) cifar10_test.append( results['test-accuracy'] ) results = api.get_more_info(idx, 'cifar100' , None, False, is_rand) cifar100_test.append( results['test-accuracy'] ) cifar100_valid.append( results['valid-accuracy'] ) results = api.get_more_info(idx, 'ImageNet16-120', None, False, is_rand) imagenet_test.append( results['test-accuracy'] ) imagenet_valid.append( results['valid-accuracy'] ) def get_cor(A, B): return float(np.corrcoef(A, B)[0,1]) cors = [] for basestr, xlist in zip(['C-010-V', 'C-010-T', 'C-100-V', 'C-100-T', 'I16-V', 'I16-T'], [cifar10_valid, cifar10_test, cifar100_valid, cifar100_test, imagenet_valid, imagenet_test]): correlation = get_cor(cifar10_currs, xlist) if need_print: print ('With {:3d}/{:}-epochs-training, the correlation between cifar10-valid and {:} is : {:}'.format(test_epoch, '012' if use_less_or_not else '200', basestr, correlation)) cors.append( correlation ) #print ('With {:3d}/200-epochs-training, the correlation between cifar10-valid and {:} is : {:}'.format(test_epoch, basestr, get_cor(cifar10_valid_200, xlist))) #print('-'*200) #print('*'*230) return cors def check_cor_for_bandit_v2(meta_file, test_epoch, use_less_or_not, is_rand): corrs = [] for i in tqdm(range(100)): x = check_cor_for_bandit(meta_file, test_epoch, use_less_or_not, is_rand, False) corrs.append( x ) #xstrs = ['CIFAR-010', 'C-100-V', 'C-100-T', 'I16-V', 'I16-T'] xstrs = ['C-010-V', 'C-010-T', 'C-100-V', 'C-100-T', 'I16-V', 'I16-T'] correlations = np.array(corrs) print('------>>>>>>>> {:03d}/{:} >>>>>>>> ------'.format(test_epoch, '012' if use_less_or_not else '200')) for idx, xstr in enumerate(xstrs): print ('{:8s} ::: mean={:.4f}, std={:.4f} :: {:.4f}\\pm{:.4f}'.format(xstr, correlations[:,idx].mean(), correlations[:,idx].std(), correlations[:,idx].mean(), correlations[:,idx].std())) print('') if __name__ == '__main__': parser = argparse.ArgumentParser("Analysis of NAS-Bench-201") parser.add_argument('--save_dir', type=str, default='./output/search-cell-nas-bench-201/visuals', help='The base-name of folder to save checkpoints and log.') parser.add_argument('--api_path', type=str, default=None, help='The path to the NAS-Bench-201 benchmark file.') args = parser.parse_args() vis_save_dir = Path(args.save_dir) vis_save_dir.mkdir(parents=True, exist_ok=True) meta_file = Path(args.api_path) assert meta_file.exists(), 'invalid path for api : {:}'.format(meta_file) #check_unique_arch(meta_file) api = API(str(meta_file)) #for iepoch in [11, 25, 50, 100, 150, 175, 200]: # check_cor_for_bandit(api, 6, iepoch) # check_cor_for_bandit(api, 12, iepoch) check_cor_for_bandit_v2(api, 6, True, True) check_cor_for_bandit_v2(api, 12, True, True) check_cor_for_bandit_v2(api, 12, False, True) check_cor_for_bandit_v2(api, 24, False, True) check_cor_for_bandit_v2(api, 100, False, True) check_cor_for_bandit_v2(api, 150, False, True) check_cor_for_bandit_v2(api, 175, False, True) check_cor_for_bandit_v2(api, 200, False, True) print('----')