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 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) 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)) logger.log('use config from : {:}'.format(xargs.config_path)) 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 = test_data.transform valid_data = train_data_v2 # data loader train_loader = torch.utils.data.DataLoader(train_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split), 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} ||||||| 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}, 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)) 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, 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={:}, 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) logger.log('[{:}] controller : loss={:.2f}, accuracy={:.2f}%, baseline={:.2f}, reward={:.2f}, current-baseline={:.4f}'.format(epoch_str, ctl_loss, ctl_acc, ctl_baseline, ctl_reward, baseline)) 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) # 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)) final_arch, _ = get_best_arch(controller, shared_cnn, valid_loader, xargs.controller_num_samples) 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)) # 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 = NASBenchmarkAPI(xargs.arch_nas_dataset) # geno = genotypes[total_epoch-1] # 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) # geno = genotypes['best'] # logger.log('The best 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.close() if __name__ == '__main__': parser = argparse.ArgumentParser("ENAS") 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('--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)