################################################## # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 # ########################################################################################################################################### # # In this file, we aims to evaluate three kinds of channel searching strategies: # - channel-wise interpolation from "Network Pruning via Transformable Architecture Search, NeurIPS 2019" # - masking + Gumbel-Softmax (mask_gumbel) from "FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions, CVPR 2020" # - masking + sampling (mask_rl) from "Can Weight Sharing Outperform Random Architecture Search? An Investigation With TuNAS, CVPR 2020" # # For simplicity, we use tas, mask_gumbel, and mask_rl to refer these three strategies. Their official implementations are at the following links: # - TAS: https://github.com/D-X-Y/AutoDL-Projects/blob/main/docs/NeurIPS-2019-TAS.md # - FBNetV2: https://github.com/facebookresearch/mobile-vision # - TuNAS: https://github.com/google-research/google-research/tree/master/tunas #### # python ./exps/NATS-algos/search-size.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo mask_rl --arch_weight_decay 0 --warmup_ratio 0.25 #### # 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 mask_gumbel --rand_seed 777 # python ./exps/NATS-algos/search-size.py --dataset cifar100 --data_path $TORCH_HOME/cifar.python --algo mask_gumbel --rand_seed 777 # python ./exps/NATS-algos/search-size.py --dataset ImageNet16-120 --data_path $TORCH_HOME/cifar.python/ImageNet16 --algo mask_gumbel --rand_seed 777 #### # python ./exps/NATS-algos/search-size.py --dataset cifar10 --data_path $TORCH_HOME/cifar.python --algo mask_rl --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 mask_rl --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 mask_rl --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 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 count_parameters_in_MB, 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 nats_bench import create # Ad-hoc for RL algorithms. 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, enable_controller, 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() a_optimizer.zero_grad() _, logits, log_probs = network(arch_inputs) arch_prec1, arch_prec5 = obtain_accuracy( logits.data, arch_targets.data, topk=(1, 5) ) if algo == "mask_rl": 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 == "mask_gumbel": arch_loss = criterion(logits, arch_targets) else: raise ValueError("invalid algorightm name: {:}".format(algo)) if enable_controller: 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) if ( xargs.warmup_ratio is None or xargs.warmup_ratio <= float(epoch) / total_epoch ): enable_controller = True network.set_warmup_ratio(None) else: enable_controller = False network.set_warmup_ratio( 1.0 - float(epoch) / total_epoch / xargs.warmup_ratio ) logger.log( "\n[Search the {:}-th epoch] {:}, LR={:}, controller-warmup={:}, enable_controller={:}".format( epoch_str, need_time, min(w_scheduler.get_lr()), network.warmup_ratio, enable_controller, ) ) if xargs.algo == "mask_gumbel" 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, enable_controller, 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", "mask_gumbel", "mask_rl"], 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." ) # FOR ALL parser.add_argument( "--warmup_ratio", type=float, help="The warmup ratio, if None, not use warmup." ) # 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{:}-WARM{:}".format( args.algo, args.affine, args.track_running_stats, args.arch_weight_decay, args.warmup_ratio, ) 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)