################################################## # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # ################################################## import os, sys, time, glob, random, argparse import numpy as np from copy import deepcopy 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 from nas_102_api import NASBench102API as API def search_func(xloader, network, criterion, scheduler, w_optimizer, epoch_str, print_freq, logger): data_time, batch_time = AverageMeter(), AverageMeter() base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(), AverageMeter() network.train() end = time.time() for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(xloader): scheduler.update(None, 1.0 * step / len(xloader)) base_targets = base_targets.cuda(non_blocking=True) arch_targets = arch_targets.cuda(non_blocking=True) # measure data loading time data_time.update(time.time() - end) # update the weights network.module.random_genotype( True ) w_optimizer.zero_grad() _, logits = network(base_inputs) base_loss = criterion(logits, base_targets) base_loss.backward() nn.utils.clip_grad_norm_(network.parameters(), 5) w_optimizer.step() # record base_prec1, base_prec5 = obtain_accuracy(logits.data, base_targets.data, topk=(1, 5)) base_losses.update(base_loss.item(), base_inputs.size(0)) base_top1.update (base_prec1.item(), base_inputs.size(0)) base_top5.update (base_prec5.item(), base_inputs.size(0)) # measure elapsed time batch_time.update(time.time() - end) end = time.time() if step % print_freq == 0 or step + 1 == len(xloader): Sstr = '*SEARCH* ' + time_string() + ' [{:}][{:03d}/{:03d}]'.format(epoch_str, step, len(xloader)) Tstr = 'Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})'.format(batch_time=batch_time, data_time=data_time) Wstr = 'Base [Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]'.format(loss=base_losses, top1=base_top1, top5=base_top5) logger.log(Sstr + ' ' + Tstr + ' ' + Wstr) return base_losses.avg, base_top1.avg, base_top5.avg 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 network.module.random_genotype( True ) _, 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 search_find_best(xloader, network, n_samples): with torch.no_grad(): network.eval() archs, valid_accs = [], [] #print ('obtain the top-{:} architectures'.format(n_samples)) loader_iter = iter(xloader) for i in range(n_samples): arch = network.module.random_genotype( True ) try: inputs, targets = next(loader_iter) except: loader_iter = iter(xloader) inputs, targets = next(loader_iter) _, logits = network(inputs) val_top1, val_top5 = obtain_accuracy(logits.cpu().data, targets.data, topk=(1, 5)) archs.append( arch ) valid_accs.append( val_top1.item() ) best_idx = np.argmax(valid_accs) best_arch, best_valid_acc = archs[best_idx], valid_accs[best_idx] return best_arch, best_valid_acc 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'): # # all_indexes = list(range(len(train_data))) ; random.seed(111) ; random.shuffle(all_indexes) # # train_split, valid_split = sorted(all_indexes[: len(train_data)//2]), sorted(all_indexes[len(train_data)//2 :]) # # imagenet16_split = dict2config({'train': train_split, 'valid': valid_split}, None) # # _ = configure2str(imagenet16_split, 'temp.txt') # 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 = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, logger) logger.log('config : {:}'.format(config)) # 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.test_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': 'RANDOM', '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) w_optimizer, w_scheduler, criterion = get_optim_scheduler(search_model.parameters(), config) logger.log('w-optimizer : {:}'.format(w_optimizer)) logger.log('w-scheduler : {:}'.format(w_scheduler)) logger.log('criterion : {:}'.format(criterion)) 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() if last_info.exists(): # automatically resume from previous checkpoint logger.log("=> loading checkpoint of the last-info '{:}' start".format(last_info)) last_info = torch.load(last_info) start_epoch = last_info['epoch'] checkpoint = torch.load(last_info['last_checkpoint']) genotypes = checkpoint['genotypes'] valid_accuracies = checkpoint['valid_accuracies'] search_model.load_state_dict( checkpoint['search_model'] ) w_scheduler.load_state_dict ( checkpoint['w_scheduler'] ) w_optimizer.load_state_dict ( checkpoint['w_optimizer'] ) logger.log("=> loading checkpoint of the last-info '{:}' start with {:}-th epoch.".format(last_info, start_epoch)) else: logger.log("=> do not find the last-info file : {:}".format(last_info)) start_epoch, valid_accuracies, genotypes = 0, {'best': -1}, {} # start training start_time, search_time, epoch_time, total_epoch = time.time(), AverageMeter(), AverageMeter(), config.epochs + config.warmup for epoch in range(start_epoch, total_epoch): w_scheduler.update(epoch, 0.0) need_time = 'Time Left: {:}'.format( convert_secs2time(epoch_time.val * (total_epoch-epoch), True) ) epoch_str = '{:03d}-{:03d}'.format(epoch, total_epoch) logger.log('\n[Search the {:}-th epoch] {:}, LR={:}'.format(epoch_str, need_time, min(w_scheduler.get_lr()))) # selected_arch = search_find_best(valid_loader, network, criterion, xargs.select_num) search_w_loss, search_w_top1, search_w_top5 = search_func(search_loader, network, criterion, w_scheduler, w_optimizer, epoch_str, xargs.print_freq, logger) search_time.update(time.time() - start_time) logger.log('[{:}] searching : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%, time-cost={:.1f} s'.format(epoch_str, search_w_loss, search_w_top1, search_w_top5, search_time.sum)) valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion) logger.log('[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5)) cur_arch, cur_valid_acc = search_find_best(valid_loader, network, xargs.select_num) logger.log('[{:}] find-the-best : {:}, accuracy@1={:.2f}%'.format(epoch_str, cur_arch, cur_valid_acc)) genotypes[epoch] = cur_arch # check the best accuracy valid_accuracies[epoch] = valid_a_top1 if valid_a_top1 > valid_accuracies['best']: valid_accuracies['best'] = valid_a_top1 find_best = True else: find_best = False # save checkpoint save_path = save_checkpoint({'epoch' : epoch + 1, 'args' : deepcopy(xargs), 'search_model': search_model.state_dict(), 'w_optimizer' : w_optimizer.state_dict(), 'w_scheduler' : w_scheduler.state_dict(), 'genotypes' : genotypes, 'valid_accuracies' : valid_accuracies}, model_base_path, logger) last_info = save_checkpoint({ 'epoch': epoch + 1, 'args' : deepcopy(args), 'last_checkpoint': save_path, }, logger.path('info'), logger) if find_best: logger.log('<<<--->>> The {:}-th epoch : find the highest validation accuracy : {:.2f}%.'.format(epoch_str, valid_a_top1)) copy_checkpoint(model_base_path, model_best_path, logger) if api is not None: logger.log('{:}'.format(api.query_by_arch( genotypes[epoch] ))) # measure elapsed time epoch_time.update(time.time() - start_time) start_time = time.time() logger.log('\n' + '-'*200) logger.log('Pre-searching costs {:.1f} s'.format(search_time.sum)) start_time = time.time() best_arch, best_acc = search_find_best(valid_loader, network, xargs.select_num) search_time.update(time.time() - start_time) logger.log('RANDOM-NAS finds the best one : {:} with accuracy={:.2f}%, with {:.1f} s.'.format(best_arch, best_acc, search_time.sum)) if api is not None: logger.log('{:}'.format( api.query_by_arch(best_arch) )) logger.close() if __name__ == '__main__': parser = argparse.ArgumentParser("Random search for NAS.") parser.add_argument('--data_path', type=str, help='Path to dataset') parser.add_argument('--dataset', type=str, choices=['cifar10', 'cifar100', 'ImageNet16-120'], help='Choose between Cifar10/100 and ImageNet-16.') # channels and number-of-cells parser.add_argument('--search_space_name', type=str, help='The search space name.') parser.add_argument('--config_path', type=str, help='The path to the configuration.') parser.add_argument('--max_nodes', type=int, help='The maximum number of nodes.') parser.add_argument('--channel', type=int, help='The number of channels.') parser.add_argument('--num_cells', type=int, help='The number of cells in one stage.') parser.add_argument('--select_num', type=int, help='The number of selected architectures to evaluate.') # log parser.add_argument('--workers', type=int, default=2, help='number of data loading workers (default: 2)') parser.add_argument('--save_dir', type=str, help='Folder to save checkpoints and log.') parser.add_argument('--arch_nas_dataset', type=str, help='The path to load the architecture dataset (tiny-nas-benchmark).') parser.add_argument('--print_freq', type=int, help='print frequency (default: 200)') parser.add_argument('--rand_seed', type=int, help='manual seed') args = parser.parse_args() if args.rand_seed is None or args.rand_seed < 0: args.rand_seed = random.randint(1, 100000) main(args)