################################################## # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 # ########################################################################### # Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019 # ########################################################################### import sys, time, random, argparse from copy import deepcopy import torch from xautodl.config_utils import load_config, dict2config from xautodl.datasets import get_datasets, get_nas_search_loaders from xautodl.procedures import ( prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler, ) from xautodl.utils import get_model_infos, obtain_accuracy from xautodl.log_utils import AverageMeter, time_string, convert_secs2time from xautodl.models import get_cell_based_tiny_net, get_search_spaces from nas_201_api import NASBench201API as API 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() 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 w_optimizer.zero_grad() _, logits = network(base_inputs) base_loss = criterion(logits, base_targets) base_loss.backward() torch.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)) # update the architecture-weight a_optimizer.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) return ( base_losses.avg, base_top1.avg, base_top5.avg, 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 ) # config_path = 'configs/nas-benchmark/algos/GDAS.config' config = load_config( xargs.config_path, {"class_num": class_num, "xshape": xshape}, logger ) search_loader, _, valid_loader = get_nas_search_loaders( train_data, valid_data, xargs.dataset, "configs/nas-benchmark/", config.batch_size, xargs.workers, ) logger.log( "||||||| {:10s} ||||||| Search-Loader-Num={:}, batch size={:}".format( xargs.dataset, len(search_loader), config.batch_size ) ) logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config)) search_space = get_search_spaces("cell", xargs.search_space_name) if xargs.model_config is None: model_config = dict2config( { "name": "GDAS", "C": xargs.channel, "N": xargs.num_cells, "max_nodes": xargs.max_nodes, "num_classes": class_num, "space": search_space, "affine": False, "track_running_stats": bool(xargs.track_running_stats), }, None, ) else: model_config = load_config( xargs.model_config, { "num_classes": class_num, "space": search_space, "affine": False, "track_running_stats": bool(xargs.track_running_stats), }, None, ) search_model = get_cell_based_tiny_net(model_config) logger.log("search-model :\n{:}".format(search_model)) logger.log("model-config : {:}".format(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("FLOP = {:.2f} M, Params = {:.2f} MB".format(flop, param)) logger.log("search-space [{:} ops] : {:}".format(len(search_space), search_space)) 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"]) 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}, {-1: search_model.genotype()}, ) # 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) search_model.set_tau( xargs.tau_max - (xargs.tau_max - xargs.tau_min) * epoch / (total_epoch - 1) ) logger.log( "\n[Search the {:}-th epoch] {:}, tau={:}, LR={:}".format( epoch_str, need_time, search_model.get_tau(), min(w_scheduler.get_lr()) ) ) ( search_w_loss, search_w_top1, search_w_top5, valid_a_loss, valid_a_top1, valid_a_top5, ) = search_func( search_loader, network, criterion, w_scheduler, w_optimizer, a_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 ) ) logger.log( "[{:}] 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 if valid_a_top1 > valid_accuracies["best"]: valid_accuracies["best"] = valid_a_top1 genotypes["best"] = search_model.genotype() find_best = True else: find_best = False genotypes[epoch] = search_model.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, ) 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) with torch.no_grad(): logger.log("{:}".format(search_model.show_alphas())) if api is not None: logger.log("{:}".format(api.query_by_arch(genotypes[epoch], "200"))) # measure elapsed time epoch_time.update(time.time() - start_time) start_time = time.time() logger.log("\n" + "-" * 100) # check the performance from the architecture dataset logger.log( "GDAS : run {:} epochs, cost {:.1f} s, last-geno is {:}.".format( total_epoch, search_time.sum, genotypes[total_epoch - 1] ) ) if api is not None: logger.log("{:}".format(api.query_by_arch(genotypes[total_epoch - 1], "200"))) logger.close() if __name__ == "__main__": parser = argparse.ArgumentParser("GDAS") parser.add_argument("--data_path", type=str, help="The 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( "--track_running_stats", type=int, choices=[0, 1], help="Whether use track_running_stats or not in the BN layer.", ) parser.add_argument( "--config_path", type=str, help="The path of the configuration." ) parser.add_argument( "--model_config", type=str, help="The path of the model configuration. When this arg is set, it will cover max_nodes / channels / num_cells.", ) # 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", ) parser.add_argument("--tau_min", type=float, help="The minimum tau for Gumbel") parser.add_argument("--tau_max", type=float, help="The maximum tau for Gumbel") # 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)