################################################## # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 # ###################################################################################### # One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 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 def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer, epoch_str, print_freq, logger): data_time, batch_time = AverageMeter(), AverageMeter() base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(), AverageMeter() arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter() end = time.time() network.train() 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 sampled_arch = network.module.dync_genotype(True) network.module.set_cal_mode('dynamic', sampled_arch) #network.module.set_cal_mode( 'urs' ) network.zero_grad() _, logits = network(base_inputs) base_loss = criterion(logits, base_targets) base_loss.backward() 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)) # update the architecture-weight network.module.set_cal_mode( 'joint' ) network.zero_grad() _, logits = network(arch_inputs) arch_loss = criterion(logits, arch_targets) arch_loss.backward() a_optimizer.step() # 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() 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) Astr = 'Arch [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=arch_losses, top1=arch_top1, top5=arch_top5) logger.log(Sstr + ' ' + Tstr + ' ' + Wstr + ' ' + Astr) #print (nn.functional.softmax(network.module.arch_parameters, dim=-1)) #print (network.module.arch_parameters) return base_losses.avg, base_top1.avg, base_top5.avg, arch_losses.avg, arch_top1.avg, arch_top5.avg def get_best_arch(xloader, network, n_samples): with torch.no_grad(): network.eval() archs, valid_accs = network.module.return_topK(n_samples), [] #print ('obtain the top-{:} architectures'.format(n_samples)) loader_iter = iter(xloader) for i, sampled_arch in enumerate(archs): network.module.set_cal_mode('dynamic', sampled_arch) 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)) valid_accs.append( val_top1.item() ) #print ('--- {:}/{:} : {:} : {:}'.format(i, len(archs), sampled_arch, val_top1)) 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 valid_func(xloader, network, criterion): data_time, batch_time = AverageMeter(), AverageMeter() arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter() end = time.time() with torch.no_grad(): network.eval() 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/SETN.config' config = load_config(xargs.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.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': 'SETN', 'C': xargs.channel, 'N': xargs.num_cells, 'max_nodes': xargs.max_nodes, 'num_classes': class_num, 'space' : search_space}, None) logger.log('search space : {:}'.format(search_space)) search_model = get_cell_based_tiny_net(model_config) 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)) 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'] ) a_optimizer.load_state_dict ( checkpoint['a_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, epoch_time, total_epoch = time.time(), 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()))) search_w_loss, search_w_top1, search_w_top5, search_a_loss, search_a_top1, search_a_top5 \ = search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str, xargs.print_freq, logger) logger.log('[{:}] search [base] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, search_w_loss, search_w_top1, search_w_top5)) logger.log('[{:}] search [arch] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, search_a_loss, search_a_top1, search_a_top5)) genotype, temp_accuracy = get_best_arch(valid_loader, network, xargs.select_num) network.module.set_cal_mode('dynamic', genotype) 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, genotype)) #search_model.set_cal_mode('urs') #valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion) #logger.log('[{:}] URS---evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5)) #search_model.set_cal_mode('joint') #valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion) #logger.log('[{:}] JOINT-evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5)) #search_model.set_cal_mode('select') #valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion) #logger.log('[{:}] Selec-evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5)) # check the best accuracy valid_accuracies[epoch] = valid_a_top1 genotypes[epoch] = genotype logger.log('<<<--->>> The {:}-th epoch : {:}'.format(epoch_str, genotypes[epoch])) # save checkpoint save_path = save_checkpoint({'epoch' : epoch + 1, 'args' : deepcopy(xargs), 'search_model': search_model.state_dict(), 'w_optimizer' : w_optimizer.state_dict(), 'a_optimizer' : a_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) with torch.no_grad(): logger.log('arch-parameters :\n{:}'.format( nn.functional.softmax(search_model.arch_parameters, dim=-1).cpu() )) # measure elapsed time epoch_time.update(time.time() - start_time) start_time = time.time() #logger.log('During searching, the best gentotype is : {:} , with the validation accuracy of {:.3f}%.'.format(genotypes['best'], valid_accuracies['best'])) genotype, temp_accuracy = get_best_arch(valid_loader, network, xargs.select_num) network.module.set_cal_mode('dynamic', genotype) valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion) logger.log('Last : the gentotype is : {:}, with the validation accuracy of {:.3f}%.'.format(genotype, valid_a_top1)) # sampling """ with torch.no_grad(): logger.log('arch-parameters :\n{:}'.format( nn.functional.softmax(search_model.arch_parameters, dim=-1).cpu() )) selected_archs = set() while len(selected_archs) < xargs.select_num: architecture = search_model.dync_genotype() selected_archs.add( architecture ) logger.log('select {:} architectures based on the learned arch-parameters'.format( len(selected_archs) )) best_arch, best_acc = None, -1 state_dict = deepcopy( network.state_dict() ) for index, arch in enumerate(selected_archs): with torch.no_grad(): search_model.set_cal_mode('dynamic', arch) network.load_state_dict( deepcopy(state_dict) ) valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion) logger.log('{:} [{:03d}/{:03d}] : {:125s}, loss={:.3f}, accuracy={:.3f}%'.format(time_string(), index, len(selected_archs), str(arch), valid_a_loss , valid_a_top1)) if best_arch is None or best_acc < valid_a_top1: best_arch, best_acc = arch, valid_a_top1 logger.log('Find the best one : {:} with accuracy={:.2f}%'.format(best_arch, best_acc)) """ logger.log('\n' + '-'*100) # check the performance from the architecture dataset """ if xargs.arch_nas_dataset is None or not os.path.isfile(xargs.arch_nas_dataset): logger.log('Can not find the architecture dataset : {:}.'.format(xargs.arch_nas_dataset)) else: nas_bench = TinyNASBenchmarkAPI(xargs.arch_nas_dataset) geno = best_arch logger.log('The last model is {:}'.format(geno)) info = nas_bench.query_by_arch( geno ) if info is None: logger.log('Did not find this architecture : {:}.'.format(geno)) else : logger.log('{:}'.format(info)) logger.log('-'*100) """ logger.close() if __name__ == '__main__': parser = argparse.ArgumentParser("SETN") 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('--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.') parser.add_argument('--config_path', type=str, help='.') # architecture leraning rate parser.add_argument('--arch_learning_rate', type=float, default=3e-4, help='learning rate for arch encoding') parser.add_argument('--arch_weight_decay', type=float, default=1e-3, help='weight decay for arch encoding') # 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)