################################################## # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 # ###################################################################################### # python ./exps/NATS-algos/search-size.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo tas --rand_seed 777 # python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo tas --rand_seed 777 # python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo tas --rand_seed 777 #### # python ./exps/NATS-algos/search-size.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo fbv2 --rand_seed 777 # python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo fbv2 --rand_seed 777 # python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo fbv2 --rand_seed 777 #### # python ./exps/NATS-algos/search-size.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo tunas --arch_weight_decay 0 --rand_seed 777 --use_api 0 # python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo tunas --arch_weight_decay 0 --rand_seed 777 # python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo tunas --arch_weight_decay 0 --rand_seed 777 ###################################################################################### import os, sys, time, 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, get_nas_search_loaders from procedures import prepare_seed, prepare_logger, save_checkpoint, copy_checkpoint, get_optim_scheduler from utils import count_parameters_in_MB, obtain_accuracy from log_utils import AverageMeter, time_string, convert_secs2time from models import get_cell_based_tiny_net, get_search_spaces from nats_bench import create # Ad-hoc for TuNAS class ExponentialMovingAverage(object): """Class that maintains an exponential moving average.""" def __init__(self, momentum): self._numerator = 0 self._denominator = 0 self._momentum = momentum def update(self, value): self._numerator = self._momentum * self._numerator + (1 - self._momentum) * value self._denominator = self._momentum * self._denominator + (1 - self._momentum) @property def value(self): """Return the current value of the moving average""" return self._numerator / self._denominator RL_BASELINE_EMA = ExponentialMovingAverage(0.95) def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer, algo, 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_inputs = base_inputs.cuda(non_blocking=True) arch_inputs = arch_inputs.cuda(non_blocking=True) 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.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.zero_grad() _, logits, log_probs = network(arch_inputs) arch_prec1, arch_prec5 = obtain_accuracy(logits.data, arch_targets.data, topk=(1, 5)) if algo == 'tunas': with torch.no_grad(): RL_BASELINE_EMA.update(arch_prec1.item()) rl_advantage = arch_prec1 - RL_BASELINE_EMA.value rl_log_prob = sum(log_probs) arch_loss = - rl_advantage * rl_log_prob elif algo == 'tas' or algo == 'fbv2': arch_loss = criterion(logits, arch_targets) else: raise ValueError('invalid algorightm name: {:}'.format(algo)) arch_loss.backward() a_optimizer.step() # record 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 valid_func(xloader, network, criterion, logger): 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.cuda(non_blocking=True)) 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.overwite_epochs is None: extra_info = {'class_num': class_num, 'xshape': xshape} else: extra_info = {'class_num': class_num, 'xshape': xshape, 'epochs': xargs.overwite_epochs} config = load_config(xargs.config_path, extra_info, logger) search_loader, train_loader, valid_loader = get_nas_search_loaders(train_data, valid_data, xargs.dataset, 'configs/nas-benchmark/', (config.batch_size, config.test_batch_size), xargs.workers) 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(xargs.search_space, 'nats-bench') model_config = dict2config( dict(name='generic', super_type='search-shape', candidate_Cs=search_space['candidates'], max_num_Cs=search_space['numbers'], num_classes=class_num, genotype=args.genotype, affine=bool(xargs.affine), track_running_stats=bool(xargs.track_running_stats)), None) logger.log('search space : {:}'.format(search_space)) logger.log('model config : {:}'.format(model_config)) search_model = get_cell_based_tiny_net(model_config) search_model.set_algo(xargs.algo) logger.log('{:}'.format(search_model)) w_optimizer, w_scheduler, criterion = get_optim_scheduler(search_model.weights, config) a_optimizer = torch.optim.Adam(search_model.alphas, lr=xargs.arch_learning_rate, betas=(0.5, 0.999), weight_decay=xargs.arch_weight_decay, eps=xargs.arch_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)) params = count_parameters_in_MB(search_model) logger.log('The parameters of the search model = {:.2f} MB'.format(params)) logger.log('search-space : {:}'.format(search_space)) if bool(xargs.use_api): api = create(None, 'size', fast_mode=True, verbose=False) else: api = None 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 = search_model.cuda(), criterion.cuda() # use a single GPU 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'] 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: network.random} # 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()))) if xargs.algo == 'fbv2' or xargs.algo == 'tas': network.set_tau( xargs.tau_max - (xargs.tau_max-xargs.tau_min) * epoch / (total_epoch-1) ) logger.log('[RESET tau as : {:}]'.format(network.tau)) 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, xargs.algo, epoch_str, xargs.print_freq, logger) search_time.update(time.time() - start_time) logger.log('[{:}] search [base] : 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('[{:}] search [arch] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, search_a_loss, search_a_top1, search_a_top5)) genotype = network.genotype logger.log('[{:}] - [get_best_arch] : {:}'.format(epoch_str, genotype)) valid_a_loss , valid_a_top1 , valid_a_top5 = valid_func(valid_loader, network, criterion, logger) logger.log('[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}% | {:}'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5, genotype)) 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('{:}'.format(search_model.show_alphas())) if api is not None: logger.log('{:}'.format(api.query_by_arch(genotypes[epoch], '90'))) # measure elapsed time epoch_time.update(time.time() - start_time) start_time = time.time() # the final post procedure : count the time start_time = time.time() genotype = network.genotype search_time.update(time.time() - start_time) valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(valid_loader, network, criterion, logger) logger.log('Last : the gentotype is : {:}, with the validation accuracy of {:.3f}%.'.format(genotype, valid_a_top1)) logger.log('\n' + '-'*100) # check the performance from the architecture dataset logger.log('[{:}] run {:} epochs, cost {:.1f} s, last-geno is {:}.'.format(xargs.algo, total_epoch, search_time.sum, genotype)) if api is not None: logger.log('{:}'.format(api.query_by_arch(genotype, '90') )) logger.close() if __name__ == '__main__': parser = argparse.ArgumentParser("Weight sharing NAS methods to search for cells.") 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.') parser.add_argument('--search_space', type=str, default='sss', choices=['sss'], help='The search space name.') parser.add_argument('--algo' , type=str, choices=['tas', 'fbv2', 'tunas'], help='The search space name.') parser.add_argument('--genotype' , type=str, default='|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|nor_conv_3x3~1|nor_conv_3x3~2|', help='The genotype.') parser.add_argument('--use_api' , type=int, default=1, choices=[0,1], help='Whether use API or not (which will cost much memory).') # FOR GDAS parser.add_argument('--tau_min', type=float, default=0.1, help='The minimum tau for Gumbel Softmax.') parser.add_argument('--tau_max', type=float, default=10, help='The maximum tau for Gumbel Softmax.') # parser.add_argument('--track_running_stats',type=int, default=0, choices=[0,1],help='Whether use track_running_stats or not in the BN layer.') parser.add_argument('--affine' , type=int, default=0, choices=[0,1],help='Whether use affine=True or False in the BN layer.') parser.add_argument('--config_path' , type=str, default='./configs/nas-benchmark/algos/weight-sharing.config', help='The path of configuration.') parser.add_argument('--overwite_epochs', type=int, help='The number of epochs to overwrite that value in config files.') # 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('--arch_eps' , type=float, default=1e-8, 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, default='./output/search', help='Folder to save checkpoints and log.') parser.add_argument('--print_freq', type=int, default=200, 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) dirname = '{:}-affine{:}_BN{:}-AWD{:}'.format(args.algo, args.affine, args.track_running_stats, args.arch_weight_decay) if args.overwite_epochs is not None: dirname = dirname + '-E{:}'.format(args.overwite_epochs) args.save_dir = os.path.join('{:}-{:}'.format(args.save_dir, args.search_space), args.dataset, dirname) main(args)