################################################## # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 # ########################################################################## # Efficient Neural Architecture Search via Parameters Sharing, ICML 2018 # ########################################################################## import os, sys, time, glob, random, argparse import numpy as np from copy import deepcopy import torch import torch.nn as nn from xautodl.config_utils import load_config, dict2config, configure2str 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 train_shared_cnn( xloader, shared_cnn, controller, criterion, scheduler, optimizer, epoch_str, print_freq, logger, ): data_time, batch_time = AverageMeter(), AverageMeter() losses, top1s, top5s, xend = ( AverageMeter(), AverageMeter(), AverageMeter(), time.time(), ) shared_cnn.train() controller.eval() for step, (inputs, targets) in enumerate(xloader): scheduler.update(None, 1.0 * step / len(xloader)) targets = targets.cuda(non_blocking=True) # measure data loading time data_time.update(time.time() - xend) with torch.no_grad(): _, _, sampled_arch = controller() optimizer.zero_grad() shared_cnn.module.update_arch(sampled_arch) _, logits = shared_cnn(inputs) loss = criterion(logits, targets) loss.backward() torch.nn.utils.clip_grad_norm_(shared_cnn.parameters(), 5) optimizer.step() # record prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5)) losses.update(loss.item(), inputs.size(0)) top1s.update(prec1.item(), inputs.size(0)) top5s.update(prec5.item(), inputs.size(0)) # measure elapsed time batch_time.update(time.time() - xend) xend = time.time() if step % print_freq == 0 or step + 1 == len(xloader): Sstr = ( "*Train-Shared-CNN* " + 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 = "[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=losses, top1=top1s, top5=top5s ) logger.log(Sstr + " " + Tstr + " " + Wstr) return losses.avg, top1s.avg, top5s.avg def train_controller( xloader, shared_cnn, controller, criterion, optimizer, config, epoch_str, print_freq, logger, ): # config. (containing some necessary arg) # baseline: The baseline score (i.e. average val_acc) from the previous epoch data_time, batch_time = AverageMeter(), AverageMeter() ( GradnormMeter, LossMeter, ValAccMeter, EntropyMeter, BaselineMeter, RewardMeter, xend, ) = ( AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), time.time(), ) shared_cnn.eval() controller.train() controller.zero_grad() # for step, (inputs, targets) in enumerate(xloader): loader_iter = iter(xloader) for step in range(config.ctl_train_steps * config.ctl_num_aggre): try: inputs, targets = next(loader_iter) except: loader_iter = iter(xloader) inputs, targets = next(loader_iter) targets = targets.cuda(non_blocking=True) # measure data loading time data_time.update(time.time() - xend) log_prob, entropy, sampled_arch = controller() with torch.no_grad(): shared_cnn.module.update_arch(sampled_arch) _, logits = shared_cnn(inputs) val_top1, val_top5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5)) val_top1 = val_top1.view(-1) / 100 reward = val_top1 + config.ctl_entropy_w * entropy if config.baseline is None: baseline = val_top1 else: baseline = config.baseline - (1 - config.ctl_bl_dec) * ( config.baseline - reward ) loss = -1 * log_prob * (reward - baseline) # account RewardMeter.update(reward.item()) BaselineMeter.update(baseline.item()) ValAccMeter.update(val_top1.item() * 100) LossMeter.update(loss.item()) EntropyMeter.update(entropy.item()) # Average gradient over controller_num_aggregate samples loss = loss / config.ctl_num_aggre loss.backward(retain_graph=True) # measure elapsed time batch_time.update(time.time() - xend) xend = time.time() if (step + 1) % config.ctl_num_aggre == 0: grad_norm = torch.nn.utils.clip_grad_norm_(controller.parameters(), 5.0) GradnormMeter.update(grad_norm) optimizer.step() controller.zero_grad() if step % print_freq == 0: Sstr = ( "*Train-Controller* " + time_string() + " [{:}][{:03d}/{:03d}]".format( epoch_str, step, config.ctl_train_steps * config.ctl_num_aggre ) ) 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 = "[Loss {loss.val:.3f} ({loss.avg:.3f}) Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Reward {reward.val:.2f} ({reward.avg:.2f})] Baseline {basel.val:.2f} ({basel.avg:.2f})".format( loss=LossMeter, top1=ValAccMeter, reward=RewardMeter, basel=BaselineMeter, ) Estr = "Entropy={:.4f} ({:.4f})".format(EntropyMeter.val, EntropyMeter.avg) logger.log(Sstr + " " + Tstr + " " + Wstr + " " + Estr) return ( LossMeter.avg, ValAccMeter.avg, BaselineMeter.avg, RewardMeter.avg, baseline.item(), ) def get_best_arch(controller, shared_cnn, xloader, n_samples=10): with torch.no_grad(): controller.eval() shared_cnn.eval() archs, valid_accs = [], [] loader_iter = iter(xloader) for i in range(n_samples): try: inputs, targets = next(loader_iter) except: loader_iter = iter(xloader) inputs, targets = next(loader_iter) _, _, sampled_arch = controller() arch = shared_cnn.module.update_arch(sampled_arch) _, logits = shared_cnn(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 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, test_data, xshape, class_num = get_datasets( xargs.dataset, xargs.data_path, -1 ) logger.log("use config from : {:}".format(xargs.config_path)) config = load_config( xargs.config_path, {"class_num": class_num, "xshape": xshape}, logger ) _, train_loader, valid_loader = get_nas_search_loaders( train_data, test_data, xargs.dataset, "configs/nas-benchmark/", config.batch_size, xargs.workers, ) # since ENAS will train the controller on valid-loader, we need to use train transformation for valid-loader valid_loader.dataset.transform = deepcopy(train_loader.dataset.transform) if hasattr(valid_loader.dataset, "transforms"): valid_loader.dataset.transforms = deepcopy(train_loader.dataset.transforms) # data loader logger.log( "||||||| {:10s} ||||||| Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}".format( xargs.dataset, len(train_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": "ENAS", "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, ) shared_cnn = get_cell_based_tiny_net(model_config) controller = shared_cnn.create_controller() w_optimizer, w_scheduler, criterion = get_optim_scheduler( shared_cnn.parameters(), config ) a_optimizer = torch.optim.Adam( controller.parameters(), lr=config.controller_lr, betas=config.controller_betas, eps=config.controller_eps, ) 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(shared_cnn, xshape) # logger.log('{:}'.format(shared_cnn)) # logger.log('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param)) logger.log("search-space : {:}".format(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)) shared_cnn, controller, criterion = ( torch.nn.DataParallel(shared_cnn).cuda(), controller.cuda(), criterion.cuda(), ) last_info, model_base_path, model_best_path = ( logger.path("info"), logger.path("model"), logger.path("best"), ) 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"] baseline = checkpoint["baseline"] valid_accuracies = checkpoint["valid_accuracies"] shared_cnn.load_state_dict(checkpoint["shared_cnn"]) controller.load_state_dict(checkpoint["controller"]) 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, baseline = 0, {"best": -1}, {}, None # 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={:}, baseline={:}".format( epoch_str, need_time, min(w_scheduler.get_lr()), baseline ) ) cnn_loss, cnn_top1, cnn_top5 = train_shared_cnn( train_loader, shared_cnn, controller, criterion, w_scheduler, w_optimizer, epoch_str, xargs.print_freq, logger, ) logger.log( "[{:}] shared-cnn : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%".format( epoch_str, cnn_loss, cnn_top1, cnn_top5 ) ) ctl_loss, ctl_acc, ctl_baseline, ctl_reward, baseline = train_controller( valid_loader, shared_cnn, controller, criterion, a_optimizer, dict2config( { "baseline": baseline, "ctl_train_steps": xargs.controller_train_steps, "ctl_num_aggre": xargs.controller_num_aggregate, "ctl_entropy_w": xargs.controller_entropy_weight, "ctl_bl_dec": xargs.controller_bl_dec, }, None, ), epoch_str, xargs.print_freq, logger, ) search_time.update(time.time() - start_time) logger.log( "[{:}] controller : loss={:.2f}, accuracy={:.2f}%, baseline={:.2f}, reward={:.2f}, current-baseline={:.4f}, time-cost={:.1f} s".format( epoch_str, ctl_loss, ctl_acc, ctl_baseline, ctl_reward, baseline, search_time.sum, ) ) best_arch, _ = get_best_arch(controller, shared_cnn, valid_loader) shared_cnn.module.update_arch(best_arch) _, best_valid_acc, _ = valid_func(valid_loader, shared_cnn, criterion) genotypes[epoch] = best_arch # check the best accuracy valid_accuracies[epoch] = best_valid_acc if best_valid_acc > valid_accuracies["best"]: valid_accuracies["best"] = best_valid_acc genotypes["best"] = best_arch find_best = True else: find_best = False logger.log( "<<<--->>> The {:}-th epoch : {:}".format(epoch_str, genotypes[epoch]) ) # save checkpoint save_path = save_checkpoint( { "epoch": epoch + 1, "args": deepcopy(xargs), "baseline": baseline, "shared_cnn": shared_cnn.state_dict(), "controller": controller.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, best_valid_acc ) ) copy_checkpoint(model_base_path, model_best_path, logger) 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) logger.log( "During searching, the best architecture is {:}".format(genotypes["best"]) ) logger.log("Its accuracy is {:.2f}%".format(valid_accuracies["best"])) logger.log( "Randomly select {:} architectures and select the best.".format( xargs.controller_num_samples ) ) start_time = time.time() final_arch, _ = get_best_arch( controller, shared_cnn, valid_loader, xargs.controller_num_samples ) search_time.update(time.time() - start_time) shared_cnn.module.update_arch(final_arch) final_loss, final_top1, final_top5 = valid_func(valid_loader, shared_cnn, criterion) logger.log("The Selected Final Architecture : {:}".format(final_arch)) logger.log( "Loss={:.3f}, Accuracy@1={:.2f}%, Accuracy@5={:.2f}%".format( final_loss, final_top1, final_top5 ) ) logger.log( "ENAS : run {:} epochs, cost {:.1f} s, last-geno is {:}.".format( total_epoch, search_time.sum, final_arch ) ) if api is not None: logger.log("{:}".format(api.query_by_arch(final_arch))) logger.close() if __name__ == "__main__": parser = argparse.ArgumentParser("ENAS") 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( "--track_running_stats", type=int, choices=[0, 1], help="Whether use track_running_stats or not in the BN layer.", ) 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( "--config_path", type=str, help="The config file to train ENAS." ) parser.add_argument("--controller_train_steps", type=int, help=".") parser.add_argument("--controller_num_aggregate", type=int, help=".") parser.add_argument( "--controller_entropy_weight", type=float, help="The weight for the entropy of the controller.", ) parser.add_argument("--controller_bl_dec", type=float, help=".") parser.add_argument("--controller_num_samples", type=int, help=".") # 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 (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)