Move algos to NAS-Bench-201-algos

exps/NAS-Bench-201-algos/BOHB.py

@@ -0,0 +1,367 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
+###################################################################
+# BOHB: Robust and Efficient Hyperparameter Optimization at Scale #
+# required to install hpbandster ##################################
+# pip install hpbandster         ##################################
+###################################################################
+# bash ./scripts-search/algos/BOHB.sh -1         ##################
+###################################################################
+import os, sys, time, random, argparse
+from copy import deepcopy
+from pathlib import Path
+import torch
+
+from xautodl.config_utils import load_config
+from xautodl.datasets import get_datasets, SearchDataset
+from xautodl.procedures import prepare_seed, prepare_logger
+from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
+from xautodl.models import CellStructure, get_search_spaces
+from nas_201_api import NASBench201API as API
+
+# BOHB: Robust and Efficient Hyperparameter Optimization at Scale, ICML 2018
+import ConfigSpace
+from hpbandster.optimizers.bohb import BOHB
+import hpbandster.core.nameserver as hpns
+from hpbandster.core.worker import Worker
+
+
+def get_configuration_space(max_nodes, search_space):
+    cs = ConfigSpace.ConfigurationSpace()
+    # edge2index   = {}
+    for i in range(1, max_nodes):
+        for j in range(i):
+            node_str = "{:}<-{:}".format(i, j)
+            cs.add_hyperparameter(
+                ConfigSpace.CategoricalHyperparameter(node_str, search_space)
+            )
+    return cs
+
+
+def config2structure_func(max_nodes):
+    def config2structure(config):
+        genotypes = []
+        for i in range(1, max_nodes):
+            xlist = []
+            for j in range(i):
+                node_str = "{:}<-{:}".format(i, j)
+                op_name = config[node_str]
+                xlist.append((op_name, j))
+            genotypes.append(tuple(xlist))
+        return CellStructure(genotypes)
+
+    return config2structure
+
+
+class MyWorker(Worker):
+    def __init__(
+        self,
+        *args,
+        convert_func=None,
+        dataname=None,
+        nas_bench=None,
+        time_budget=None,
+        **kwargs
+    ):
+        super().__init__(*args, **kwargs)
+        self.convert_func = convert_func
+        self._dataname = dataname
+        self._nas_bench = nas_bench
+        self.time_budget = time_budget
+        self.seen_archs = []
+        self.sim_cost_time = 0
+        self.real_cost_time = 0
+        self.is_end = False
+
+    def get_the_best(self):
+        assert len(self.seen_archs) > 0
+        best_index, best_acc = -1, None
+        for arch_index in self.seen_archs:
+            info = self._nas_bench.get_more_info(
+                arch_index, self._dataname, None, hp="200", is_random=True
+            )
+            vacc = info["valid-accuracy"]
+            if best_acc is None or best_acc < vacc:
+                best_acc = vacc
+                best_index = arch_index
+        assert best_index != -1
+        return best_index
+
+    def compute(self, config, budget, **kwargs):
+        start_time = time.time()
+        structure = self.convert_func(config)
+        arch_index = self._nas_bench.query_index_by_arch(structure)
+        info = self._nas_bench.get_more_info(
+            arch_index, self._dataname, None, hp="200", is_random=True
+        )
+        cur_time = info["train-all-time"] + info["valid-per-time"]
+        cur_vacc = info["valid-accuracy"]
+        self.real_cost_time += time.time() - start_time
+        if self.sim_cost_time + cur_time <= self.time_budget and not self.is_end:
+            self.sim_cost_time += cur_time
+            self.seen_archs.append(arch_index)
+            return {
+                "loss": 100 - float(cur_vacc),
+                "info": {
+                    "seen-arch": len(self.seen_archs),
+                    "sim-test-time": self.sim_cost_time,
+                    "current-arch": arch_index,
+                },
+            }
+        else:
+            self.is_end = True
+            return {
+                "loss": 100,
+                "info": {
+                    "seen-arch": len(self.seen_archs),
+                    "sim-test-time": self.sim_cost_time,
+                    "current-arch": None,
+                },
+            }
+
+
+def main(xargs, nas_bench):
+    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)
+
+    if xargs.dataset == "cifar10":
+        dataname = "cifar10-valid"
+    else:
+        dataname = xargs.dataset
+    if xargs.data_path is not None:
+        train_data, valid_data, xshape, class_num = get_datasets(
+            xargs.dataset, xargs.data_path, -1
+        )
+        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))
+        config_path = "configs/nas-benchmark/algos/R-EA.config"
+        config = load_config(
+            config_path, {"class_num": class_num, "xshape": xshape}, logger
+        )
+        # To split data
+        train_data_v2 = deepcopy(train_data)
+        train_data_v2.transform = valid_data.transform
+        valid_data = train_data_v2
+        search_data = SearchDataset(xargs.dataset, train_data, train_split, valid_split)
+        # 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))
+        extra_info = {
+            "config": config,
+            "train_loader": train_loader,
+            "valid_loader": valid_loader,
+        }
+    else:
+        config_path = "configs/nas-benchmark/algos/R-EA.config"
+        config = load_config(config_path, None, logger)
+        logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
+        extra_info = {"config": config, "train_loader": None, "valid_loader": None}
+
+    # nas dataset load
+    assert xargs.arch_nas_dataset is not None and os.path.isfile(xargs.arch_nas_dataset)
+    search_space = get_search_spaces("cell", xargs.search_space_name)
+    cs = get_configuration_space(xargs.max_nodes, search_space)
+
+    config2structure = config2structure_func(xargs.max_nodes)
+    hb_run_id = "0"
+
+    NS = hpns.NameServer(run_id=hb_run_id, host="localhost", port=0)
+    ns_host, ns_port = NS.start()
+    num_workers = 1
+
+    # nas_bench = AANASBenchAPI(xargs.arch_nas_dataset)
+    # logger.log('{:} Create NAS-BENCH-API DONE'.format(time_string()))
+    workers = []
+    for i in range(num_workers):
+        w = MyWorker(
+            nameserver=ns_host,
+            nameserver_port=ns_port,
+            convert_func=config2structure,
+            dataname=dataname,
+            nas_bench=nas_bench,
+            time_budget=xargs.time_budget,
+            run_id=hb_run_id,
+            id=i,
+        )
+        w.run(background=True)
+        workers.append(w)
+
+    start_time = time.time()
+    bohb = BOHB(
+        configspace=cs,
+        run_id=hb_run_id,
+        eta=3,
+        min_budget=12,
+        max_budget=200,
+        nameserver=ns_host,
+        nameserver_port=ns_port,
+        num_samples=xargs.num_samples,
+        random_fraction=xargs.random_fraction,
+        bandwidth_factor=xargs.bandwidth_factor,
+        ping_interval=10,
+        min_bandwidth=xargs.min_bandwidth,
+    )
+
+    results = bohb.run(xargs.n_iters, min_n_workers=num_workers)
+
+    bohb.shutdown(shutdown_workers=True)
+    NS.shutdown()
+
+    real_cost_time = time.time() - start_time
+
+    id2config = results.get_id2config_mapping()
+    incumbent = results.get_incumbent_id()
+    logger.log(
+        "Best found configuration: {:} within {:.3f} s".format(
+            id2config[incumbent]["config"], real_cost_time
+        )
+    )
+    best_arch = config2structure(id2config[incumbent]["config"])
+
+    info = nas_bench.query_by_arch(best_arch, "200")
+    if info is None:
+        logger.log("Did not find this architecture : {:}.".format(best_arch))
+    else:
+        logger.log("{:}".format(info))
+    logger.log("-" * 100)
+
+    logger.log(
+        "workers : {:.1f}s with {:} archs".format(
+            workers[0].time_budget, len(workers[0].seen_archs)
+        )
+    )
+    logger.close()
+    return logger.log_dir, nas_bench.query_index_by_arch(best_arch), real_cost_time
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        "BOHB: Robust and Efficient Hyperparameter Optimization at Scale"
+    )
+    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(
+        "--time_budget",
+        type=int,
+        help="The total time cost budge for searching (in seconds).",
+    )
+    # BOHB
+    parser.add_argument(
+        "--strategy",
+        default="sampling",
+        type=str,
+        nargs="?",
+        help="optimization strategy for the acquisition function",
+    )
+    parser.add_argument(
+        "--min_bandwidth",
+        default=0.3,
+        type=float,
+        nargs="?",
+        help="minimum bandwidth for KDE",
+    )
+    parser.add_argument(
+        "--num_samples",
+        default=64,
+        type=int,
+        nargs="?",
+        help="number of samples for the acquisition function",
+    )
+    parser.add_argument(
+        "--random_fraction",
+        default=0.33,
+        type=float,
+        nargs="?",
+        help="fraction of random configurations",
+    )
+    parser.add_argument(
+        "--bandwidth_factor",
+        default=3,
+        type=int,
+        nargs="?",
+        help="factor multiplied to the bandwidth",
+    )
+    parser.add_argument(
+        "--n_iters",
+        default=100,
+        type=int,
+        nargs="?",
+        help="number of iterations for optimization method",
+    )
+    # 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)
+    if args.arch_nas_dataset is None or not os.path.isfile(args.arch_nas_dataset):
+        nas_bench = None
+    else:
+        print(
+            "{:} build NAS-Benchmark-API from {:}".format(
+                time_string(), args.arch_nas_dataset
+            )
+        )
+        nas_bench = API(args.arch_nas_dataset)
+    if args.rand_seed < 0:
+        save_dir, all_indexes, num, all_times = None, [], 500, []
+        for i in range(num):
+            print("{:} : {:03d}/{:03d}".format(time_string(), i, num))
+            args.rand_seed = random.randint(1, 100000)
+            save_dir, index, ctime = main(args, nas_bench)
+            all_indexes.append(index)
+            all_times.append(ctime)
+        print("\n average time : {:.3f} s".format(sum(all_times) / len(all_times)))
+        torch.save(all_indexes, save_dir / "results.pth")
+    else:
+        main(args, nas_bench)

exps/NAS-Bench-201-algos/DARTS-V1.py

@@ -0,0 +1,417 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
+########################################################
+# DARTS: Differentiable Architecture Search, ICLR 2019 #
+########################################################
+import sys, time, random, argparse
+from copy import deepcopy
+import torch
+from pathlib import Path
+
+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 search_func(
+    xloader,
+    network,
+    criterion,
+    scheduler,
+    w_optimizer,
+    a_optimizer,
+    epoch_str,
+    print_freq,
+    logger,
+    gradient_clip,
+):
+    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()
+        if gradient_clip > 0:
+            torch.nn.utils.clip_grad_norm_(network.parameters(), gradient_clip)
+        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
+
+
+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, valid_data, xshape, class_num = get_datasets(
+        xargs.dataset, xargs.data_path, -1
+    )
+    # config_path = 'configs/nas-benchmark/algos/DARTS.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={:}, 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("cell", xargs.search_space_name)
+    if xargs.model_config is None:
+        model_config = dict2config(
+            {
+                "name": "DARTS-V1",
+                "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))
+
+    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('{:}'.format(search_model))
+    logger.log("FLOP = {:.2f} M, Params = {:.2f} MB".format(flop, param))
+    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)
+        logger.log(
+            "\n[Search the {:}-th epoch] {:}, LR={:}".format(
+                epoch_str, need_time, min(w_scheduler.get_lr())
+            )
+        )
+
+        search_w_loss, search_w_top1, search_w_top5 = search_func(
+            search_loader,
+            network,
+            criterion,
+            w_scheduler,
+            w_optimizer,
+            a_optimizer,
+            epoch_str,
+            xargs.print_freq,
+            logger,
+            xargs.gradient_clip,
+        )
+        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
+            )
+        )
+        valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(
+            valid_loader, network, criterion
+        )
+        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('arch-parameters :\n{:}'.format( nn.functional.softmax(search_model.arch_parameters, dim=-1).cpu() ))
+            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)
+    logger.log(
+        "DARTS-V1 : 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("DARTS first order")
+    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(
+        "--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 config path.")
+    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.",
+    )
+    parser.add_argument("--gradient_clip", type=float, default=5, help="")
+    # 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",
+    )
+    # 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)

exps/NAS-Bench-201-algos/DARTS-V2.py

@@ -0,0 +1,496 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
+########################################################
+# DARTS: Differentiable Architecture Search, ICLR 2019 #
+########################################################
+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 _concat(xs):
+    return torch.cat([x.view(-1) for x in xs])
+
+
+def _hessian_vector_product(
+    vector, network, criterion, base_inputs, base_targets, r=1e-2
+):
+    R = r / _concat(vector).norm()
+    for p, v in zip(network.module.get_weights(), vector):
+        p.data.add_(R, v)
+    _, logits = network(base_inputs)
+    loss = criterion(logits, base_targets)
+    grads_p = torch.autograd.grad(loss, network.module.get_alphas())
+
+    for p, v in zip(network.module.get_weights(), vector):
+        p.data.sub_(2 * R, v)
+    _, logits = network(base_inputs)
+    loss = criterion(logits, base_targets)
+    grads_n = torch.autograd.grad(loss, network.module.get_alphas())
+
+    for p, v in zip(network.module.get_weights(), vector):
+        p.data.add_(R, v)
+    return [(x - y).div_(2 * R) for x, y in zip(grads_p, grads_n)]
+
+
+def backward_step_unrolled(
+    network,
+    criterion,
+    base_inputs,
+    base_targets,
+    w_optimizer,
+    arch_inputs,
+    arch_targets,
+):
+    # _compute_unrolled_model
+    _, logits = network(base_inputs)
+    loss = criterion(logits, base_targets)
+    LR, WD, momentum = (
+        w_optimizer.param_groups[0]["lr"],
+        w_optimizer.param_groups[0]["weight_decay"],
+        w_optimizer.param_groups[0]["momentum"],
+    )
+    with torch.no_grad():
+        theta = _concat(network.module.get_weights())
+        try:
+            moment = _concat(
+                w_optimizer.state[v]["momentum_buffer"]
+                for v in network.module.get_weights()
+            )
+            moment = moment.mul_(momentum)
+        except:
+            moment = torch.zeros_like(theta)
+        dtheta = (
+            _concat(torch.autograd.grad(loss, network.module.get_weights()))
+            + WD * theta
+        )
+        params = theta.sub(LR, moment + dtheta)
+    unrolled_model = deepcopy(network)
+    model_dict = unrolled_model.state_dict()
+    new_params, offset = {}, 0
+    for k, v in network.named_parameters():
+        if "arch_parameters" in k:
+            continue
+        v_length = np.prod(v.size())
+        new_params[k] = params[offset : offset + v_length].view(v.size())
+        offset += v_length
+    model_dict.update(new_params)
+    unrolled_model.load_state_dict(model_dict)
+
+    unrolled_model.zero_grad()
+    _, unrolled_logits = unrolled_model(arch_inputs)
+    unrolled_loss = criterion(unrolled_logits, arch_targets)
+    unrolled_loss.backward()
+
+    dalpha = unrolled_model.module.arch_parameters.grad
+    vector = [v.grad.data for v in unrolled_model.module.get_weights()]
+    [implicit_grads] = _hessian_vector_product(
+        vector, network, criterion, base_inputs, base_targets
+    )
+
+    dalpha.data.sub_(LR, implicit_grads.data)
+
+    if network.module.arch_parameters.grad is None:
+        network.module.arch_parameters.grad = deepcopy(dalpha)
+    else:
+        network.module.arch_parameters.grad.data.copy_(dalpha.data)
+    return unrolled_loss.detach(), unrolled_logits.detach()
+
+
+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 architecture-weight
+        a_optimizer.zero_grad()
+        arch_loss, arch_logits = backward_step_unrolled(
+            network,
+            criterion,
+            base_inputs,
+            base_targets,
+            w_optimizer,
+            arch_inputs,
+            arch_targets,
+        )
+        a_optimizer.step()
+        # record
+        arch_prec1, arch_prec5 = obtain_accuracy(
+            arch_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))
+
+        # 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))
+
+        # 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
+
+
+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, valid_data, xshape, class_num = get_datasets(
+        xargs.dataset, xargs.data_path, -1
+    )
+    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={:}, 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("cell", xargs.search_space_name)
+    model_config = dict2config(
+        {
+            "name": "DARTS-V2",
+            "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,
+    )
+    search_model = get_cell_based_tiny_net(model_config)
+    logger.log("search-model :\n{:}".format(search_model))
+
+    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('{:}'.format(search_model))
+    logger.log("FLOP = {:.2f} M, Params = {:.2f} MB".format(flop, param))
+    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)
+        min_LR = min(w_scheduler.get_lr())
+        logger.log(
+            "\n[Search the {:}-th epoch] {:}, LR={:}".format(
+                epoch_str, need_time, min_LR
+            )
+        )
+
+        search_w_loss, search_w_top1, search_w_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
+            )
+        )
+        valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(
+            valid_loader, network, criterion
+        )
+        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(
+                "arch-parameters :\n{:}".format(
+                    nn.functional.softmax(search_model.arch_parameters, dim=-1).cpu()
+                )
+            )
+        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(
+        "DARTS-V2 : 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("DARTS Second Order")
+    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("--config_path", type=str, help="The config path.")
+    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.",
+    )
+    # 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",
+    )
+    # 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)

exps/NAS-Bench-201-algos/ENAS.py

@@ -0,0 +1,578 @@
+##################################################
+# 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)

exps/NAS-Bench-201-algos/GDAS.py

@@ -0,0 +1,404 @@
+##################################################
+# 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)

exps/NAS-Bench-201-algos/RANDOM-NAS.py

@@ -0,0 +1,382 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
+##############################################################################
+# Random Search and Reproducibility for Neural Architecture Search, UAI 2019 #
+##############################################################################
+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 search_func(
+    xloader, network, criterion, scheduler, w_optimizer, epoch_str, print_freq, logger
+):
+    data_time, batch_time = AverageMeter(), AverageMeter()
+    base_losses, base_top1, base_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
+        network.module.random_genotype(True)
+        w_optimizer.zero_grad()
+        _, logits = network(base_inputs)
+        base_loss = criterion(logits, base_targets)
+        base_loss.backward()
+        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))
+
+        # 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
+            )
+            logger.log(Sstr + " " + Tstr + " " + Wstr)
+    return base_losses.avg, base_top1.avg, base_top5.avg
+
+
+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
+
+            network.module.random_genotype(True)
+            _, 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 search_find_best(xloader, network, n_samples):
+    with torch.no_grad():
+        network.eval()
+        archs, valid_accs = [], []
+        # print ('obtain the top-{:} architectures'.format(n_samples))
+        loader_iter = iter(xloader)
+        for i in range(n_samples):
+            arch = network.module.random_genotype(True)
+            try:
+                inputs, targets = next(loader_iter)
+            except:
+                loader_iter = iter(xloader)
+                inputs, targets = next(loader_iter)
+
+            _, logits = network(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 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 = 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, 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("cell", xargs.search_space_name)
+    model_config = dict2config(
+        {
+            "name": "RANDOM",
+            "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,
+    )
+    search_model = get_cell_based_tiny_net(model_config)
+
+    w_optimizer, w_scheduler, criterion = get_optim_scheduler(
+        search_model.parameters(), config
+    )
+    logger.log("w-optimizer : {:}".format(w_optimizer))
+    logger.log("w-scheduler : {:}".format(w_scheduler))
+    logger.log("criterion   : {:}".format(criterion))
+    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"])
+        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}, {}
+
+    # 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())
+            )
+        )
+
+        # selected_arch = search_find_best(valid_loader, network, criterion, xargs.select_num)
+        search_w_loss, search_w_top1, search_w_top5 = search_func(
+            search_loader,
+            network,
+            criterion,
+            w_scheduler,
+            w_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
+            )
+        )
+        valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(
+            valid_loader, network, criterion
+        )
+        logger.log(
+            "[{:}] evaluate  : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%".format(
+                epoch_str, valid_a_loss, valid_a_top1, valid_a_top5
+            )
+        )
+        cur_arch, cur_valid_acc = search_find_best(
+            valid_loader, network, xargs.select_num
+        )
+        logger.log(
+            "[{:}] find-the-best : {:}, accuracy@1={:.2f}%".format(
+                epoch_str, cur_arch, cur_valid_acc
+            )
+        )
+        genotypes[epoch] = cur_arch
+        # check the best accuracy
+        valid_accuracies[epoch] = valid_a_top1
+        if valid_a_top1 > valid_accuracies["best"]:
+            valid_accuracies["best"] = valid_a_top1
+            find_best = True
+        else:
+            find_best = False
+
+        # save checkpoint
+        save_path = save_checkpoint(
+            {
+                "epoch": epoch + 1,
+                "args": deepcopy(xargs),
+                "search_model": search_model.state_dict(),
+                "w_optimizer": w_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)
+        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" + "-" * 200)
+    logger.log("Pre-searching costs {:.1f} s".format(search_time.sum))
+    start_time = time.time()
+    best_arch, best_acc = search_find_best(valid_loader, network, xargs.select_num)
+    search_time.update(time.time() - start_time)
+    logger.log(
+        "RANDOM-NAS finds the best one : {:} with accuracy={:.2f}%, with {:.1f} s.".format(
+            best_arch, best_acc, search_time.sum
+        )
+    )
+    if api is not None:
+        logger.log("{:}".format(api.query_by_arch(best_arch, "200")))
+    logger.close()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser("Random search for NAS.")
+    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(
+        "--config_path", type=str, help="The path to the configuration."
+    )
+    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(
+        "--select_num",
+        type=int,
+        help="The number of selected architectures to evaluate.",
+    )
+    parser.add_argument(
+        "--track_running_stats",
+        type=int,
+        choices=[0, 1],
+        help="Whether use track_running_stats or not in the BN layer.",
+    )
+    # 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)

exps/NAS-Bench-201-algos/RANDOM.py

@@ -0,0 +1,189 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
+##############################################################################
+import os, sys, time, glob, random, argparse
+import numpy as np, collections
+from copy import deepcopy
+import torch
+import torch.nn as nn
+from pathlib import Path
+
+from xautodl.config_utils import load_config, dict2config, configure2str
+from xautodl.datasets import get_datasets, SearchDataset
+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_search_spaces
+
+from nas_201_api import NASBench201API as API
+from R_EA import train_and_eval, random_architecture_func
+
+
+def main(xargs, nas_bench):
+    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)
+
+    if xargs.dataset == "cifar10":
+        dataname = "cifar10-valid"
+    else:
+        dataname = xargs.dataset
+    if xargs.data_path is not None:
+        train_data, valid_data, xshape, class_num = get_datasets(
+            xargs.dataset, xargs.data_path, -1
+        )
+        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))
+        config_path = "configs/nas-benchmark/algos/R-EA.config"
+        config = load_config(
+            config_path, {"class_num": class_num, "xshape": xshape}, logger
+        )
+        # To split data
+        train_data_v2 = deepcopy(train_data)
+        train_data_v2.transform = valid_data.transform
+        valid_data = train_data_v2
+        search_data = SearchDataset(xargs.dataset, train_data, train_split, valid_split)
+        # 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))
+        extra_info = {
+            "config": config,
+            "train_loader": train_loader,
+            "valid_loader": valid_loader,
+        }
+    else:
+        config_path = "configs/nas-benchmark/algos/R-EA.config"
+        config = load_config(config_path, None, logger)
+        logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
+        extra_info = {"config": config, "train_loader": None, "valid_loader": None}
+    search_space = get_search_spaces("cell", xargs.search_space_name)
+    random_arch = random_architecture_func(xargs.max_nodes, search_space)
+    # x =random_arch() ; y = mutate_arch(x)
+    x_start_time = time.time()
+    logger.log("{:} use nas_bench : {:}".format(time_string(), nas_bench))
+    best_arch, best_acc, total_time_cost, history = None, -1, 0, []
+    # for idx in range(xargs.random_num):
+    while total_time_cost < xargs.time_budget:
+        arch = random_arch()
+        accuracy, cost_time = train_and_eval(arch, nas_bench, extra_info, dataname)
+        if total_time_cost + cost_time > xargs.time_budget:
+            break
+        else:
+            total_time_cost += cost_time
+        history.append(arch)
+        if best_arch is None or best_acc < accuracy:
+            best_acc, best_arch = accuracy, arch
+        logger.log(
+            "[{:03d}] : {:} : accuracy = {:.2f}%".format(len(history), arch, accuracy)
+        )
+    logger.log(
+        "{:} best arch is {:}, accuracy = {:.2f}%, visit {:} archs with {:.1f} s (real-cost = {:.3f} s).".format(
+            time_string(),
+            best_arch,
+            best_acc,
+            len(history),
+            total_time_cost,
+            time.time() - x_start_time,
+        )
+    )
+
+    info = nas_bench.query_by_arch(best_arch, "200")
+    if info is None:
+        logger.log("Did not find this architecture : {:}.".format(best_arch))
+    else:
+        logger.log("{:}".format(info))
+    logger.log("-" * 100)
+    logger.close()
+    return logger.log_dir, nas_bench.query_index_by_arch(best_arch)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser("Random NAS")
+    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('--random_num',         type=int,   help='The number of random selected architectures.')
+    parser.add_argument(
+        "--time_budget",
+        type=int,
+        help="The total time cost budge for searching (in seconds).",
+    )
+    # 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)
+    if args.arch_nas_dataset is None or not os.path.isfile(args.arch_nas_dataset):
+        nas_bench = None
+    else:
+        print(
+            "{:} build NAS-Benchmark-API from {:}".format(
+                time_string(), args.arch_nas_dataset
+            )
+        )
+        nas_bench = API(args.arch_nas_dataset)
+    if args.rand_seed < 0:
+        save_dir, all_indexes, num = None, [], 500
+        for i in range(num):
+            print("{:} : {:03d}/{:03d}".format(time_string(), i, num))
+            args.rand_seed = random.randint(1, 100000)
+            save_dir, index = main(args, nas_bench)
+            all_indexes.append(index)
+        torch.save(all_indexes, save_dir / "results.pth")
+    else:
+        main(args, nas_bench)

exps/NAS-Bench-201-algos/README.md

@@ -0,0 +1,7 @@
+# NAS Algorithms evaluated in NAS-Bench-201
+
+The Python files in this folder are used to re-produce the results in our NAS-Bench-201 paper.
+
+We have upgraded the codes to be more general and extendable at [NATS-algos](https://github.com/D-X-Y/AutoDL-Projects/tree/main/exps/NATS-algos).
+
+**Notice** On 24 May 2021, the codes in `AutoDL` repo have been re-organized. If you find `module not found` error, please let me know. I will fix them ASAP.

exps/NAS-Bench-201-algos/R_EA.py

@@ -0,0 +1,399 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
+##################################################################
+# Regularized Evolution for Image Classifier Architecture Search #
+##################################################################
+import os, sys, time, glob, random, argparse
+import numpy as np, collections
+from copy import deepcopy
+import torch
+import torch.nn as nn
+from pathlib import Path
+
+from xautodl.config_utils import load_config, dict2config, configure2str
+from xautodl.datasets import get_datasets, SearchDataset
+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 CellStructure, get_search_spaces
+from nas_201_api import NASBench201API as API
+
+
+class Model(object):
+    def __init__(self):
+        self.arch = None
+        self.accuracy = None
+
+    def __str__(self):
+        """Prints a readable version of this bitstring."""
+        return "{:}".format(self.arch)
+
+
+# This function is to mimic the training and evaluatinig procedure for a single architecture `arch`.
+# The time_cost is calculated as the total training time for a few (e.g., 12 epochs) plus the evaluation time for one epoch.
+# For use_012_epoch_training = True, the architecture is trained for 12 epochs, with LR being decaded from 0.1 to 0.
+#       In this case, the LR schedular is converged.
+# For use_012_epoch_training = False, the architecture is planed to be trained for 200 epochs, but we early stop its procedure.
+#
+def train_and_eval(
+    arch, nas_bench, extra_info, dataname="cifar10-valid", use_012_epoch_training=True
+):
+
+    if use_012_epoch_training and nas_bench is not None:
+        arch_index = nas_bench.query_index_by_arch(arch)
+        assert arch_index >= 0, "can not find this arch : {:}".format(arch)
+        info = nas_bench.get_more_info(
+            arch_index, dataname, iepoch=None, hp="12", is_random=True
+        )
+        valid_acc, time_cost = (
+            info["valid-accuracy"],
+            info["train-all-time"] + info["valid-per-time"],
+        )
+        # _, valid_acc = info.get_metrics('cifar10-valid', 'x-valid' , 25, True) # use the validation accuracy after 25 training epochs
+    elif not use_012_epoch_training and nas_bench is not None:
+        # Please contact me if you want to use the following logic, because it has some potential issues.
+        # Please use `use_012_epoch_training=False` for cifar10 only.
+        # It did return values for cifar100 and ImageNet16-120, but it has some potential issues. (Please email me for more details)
+        arch_index, nepoch = nas_bench.query_index_by_arch(arch), 25
+        assert arch_index >= 0, "can not find this arch : {:}".format(arch)
+        xoinfo = nas_bench.get_more_info(
+            arch_index, "cifar10-valid", iepoch=None, hp="12"
+        )
+        xocost = nas_bench.get_cost_info(arch_index, "cifar10-valid", hp="200")
+        info = nas_bench.get_more_info(
+            arch_index, dataname, nepoch, hp="200", is_random=True
+        )  # use the validation accuracy after 25 training epochs, which is used in our ICLR submission (not the camera ready).
+        cost = nas_bench.get_cost_info(arch_index, dataname, hp="200")
+        # The following codes are used to estimate the time cost.
+        # When we build NAS-Bench-201, architectures are trained on different machines and we can not use that time record.
+        # When we create checkpoints for converged_LR, we run all experiments on 1080Ti, and thus the time for each architecture can be fairly compared.
+        nums = {
+            "ImageNet16-120-train": 151700,
+            "ImageNet16-120-valid": 3000,
+            "cifar10-valid-train": 25000,
+            "cifar10-valid-valid": 25000,
+            "cifar100-train": 50000,
+            "cifar100-valid": 5000,
+        }
+        estimated_train_cost = (
+            xoinfo["train-per-time"]
+            / nums["cifar10-valid-train"]
+            * nums["{:}-train".format(dataname)]
+            / xocost["latency"]
+            * cost["latency"]
+            * nepoch
+        )
+        estimated_valid_cost = (
+            xoinfo["valid-per-time"]
+            / nums["cifar10-valid-valid"]
+            * nums["{:}-valid".format(dataname)]
+            / xocost["latency"]
+            * cost["latency"]
+        )
+        try:
+            valid_acc, time_cost = (
+                info["valid-accuracy"],
+                estimated_train_cost + estimated_valid_cost,
+            )
+        except:
+            valid_acc, time_cost = (
+                info["valtest-accuracy"],
+                estimated_train_cost + estimated_valid_cost,
+            )
+    else:
+        # train a model from scratch.
+        raise ValueError("NOT IMPLEMENT YET")
+    return valid_acc, time_cost
+
+
+def random_architecture_func(max_nodes, op_names):
+    # return a random architecture
+    def random_architecture():
+        genotypes = []
+        for i in range(1, max_nodes):
+            xlist = []
+            for j in range(i):
+                node_str = "{:}<-{:}".format(i, j)
+                op_name = random.choice(op_names)
+                xlist.append((op_name, j))
+            genotypes.append(tuple(xlist))
+        return CellStructure(genotypes)
+
+    return random_architecture
+
+
+def mutate_arch_func(op_names):
+    """Computes the architecture for a child of the given parent architecture.
+    The parent architecture is cloned and mutated to produce the child architecture. The child architecture is mutated by randomly switch one operation to another.
+    """
+
+    def mutate_arch_func(parent_arch):
+        child_arch = deepcopy(parent_arch)
+        node_id = random.randint(0, len(child_arch.nodes) - 1)
+        node_info = list(child_arch.nodes[node_id])
+        snode_id = random.randint(0, len(node_info) - 1)
+        xop = random.choice(op_names)
+        while xop == node_info[snode_id][0]:
+            xop = random.choice(op_names)
+        node_info[snode_id] = (xop, node_info[snode_id][1])
+        child_arch.nodes[node_id] = tuple(node_info)
+        return child_arch
+
+    return mutate_arch_func
+
+
+def regularized_evolution(
+    cycles,
+    population_size,
+    sample_size,
+    time_budget,
+    random_arch,
+    mutate_arch,
+    nas_bench,
+    extra_info,
+    dataname,
+):
+    """Algorithm for regularized evolution (i.e. aging evolution).
+
+    Follows "Algorithm 1" in Real et al. "Regularized Evolution for Image
+    Classifier Architecture Search".
+
+    Args:
+      cycles: the number of cycles the algorithm should run for.
+      population_size: the number of individuals to keep in the population.
+      sample_size: the number of individuals that should participate in each tournament.
+      time_budget: the upper bound of searching cost
+
+    Returns:
+      history: a list of `Model` instances, representing all the models computed
+          during the evolution experiment.
+    """
+    population = collections.deque()
+    history, total_time_cost = (
+        [],
+        0,
+    )  # Not used by the algorithm, only used to report results.
+
+    # Initialize the population with random models.
+    while len(population) < population_size:
+        model = Model()
+        model.arch = random_arch()
+        model.accuracy, time_cost = train_and_eval(
+            model.arch, nas_bench, extra_info, dataname
+        )
+        population.append(model)
+        history.append(model)
+        total_time_cost += time_cost
+
+    # Carry out evolution in cycles. Each cycle produces a model and removes
+    # another.
+    # while len(history) < cycles:
+    while total_time_cost < time_budget:
+        # Sample randomly chosen models from the current population.
+        start_time, sample = time.time(), []
+        while len(sample) < sample_size:
+            # Inefficient, but written this way for clarity. In the case of neural
+            # nets, the efficiency of this line is irrelevant because training neural
+            # nets is the rate-determining step.
+            candidate = random.choice(list(population))
+            sample.append(candidate)
+
+        # The parent is the best model in the sample.
+        parent = max(sample, key=lambda i: i.accuracy)
+
+        # Create the child model and store it.
+        child = Model()
+        child.arch = mutate_arch(parent.arch)
+        total_time_cost += time.time() - start_time
+        child.accuracy, time_cost = train_and_eval(
+            child.arch, nas_bench, extra_info, dataname
+        )
+        if total_time_cost + time_cost > time_budget:  # return
+            return history, total_time_cost
+        else:
+            total_time_cost += time_cost
+        population.append(child)
+        history.append(child)
+
+        # Remove the oldest model.
+        population.popleft()
+    return history, total_time_cost
+
+
+def main(xargs, nas_bench):
+    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)
+
+    if xargs.dataset == "cifar10":
+        dataname = "cifar10-valid"
+    else:
+        dataname = xargs.dataset
+    if xargs.data_path is not None:
+        train_data, valid_data, xshape, class_num = get_datasets(
+            xargs.dataset, xargs.data_path, -1
+        )
+        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))
+        config_path = "configs/nas-benchmark/algos/R-EA.config"
+        config = load_config(
+            config_path, {"class_num": class_num, "xshape": xshape}, logger
+        )
+        # To split data
+        train_data_v2 = deepcopy(train_data)
+        train_data_v2.transform = valid_data.transform
+        valid_data = train_data_v2
+        search_data = SearchDataset(xargs.dataset, train_data, train_split, valid_split)
+        # 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))
+        extra_info = {
+            "config": config,
+            "train_loader": train_loader,
+            "valid_loader": valid_loader,
+        }
+    else:
+        config_path = "configs/nas-benchmark/algos/R-EA.config"
+        config = load_config(config_path, None, logger)
+        logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
+        extra_info = {"config": config, "train_loader": None, "valid_loader": None}
+
+    search_space = get_search_spaces("cell", xargs.search_space_name)
+    random_arch = random_architecture_func(xargs.max_nodes, search_space)
+    mutate_arch = mutate_arch_func(search_space)
+    # x =random_arch() ; y = mutate_arch(x)
+    x_start_time = time.time()
+    logger.log("{:} use nas_bench : {:}".format(time_string(), nas_bench))
+    logger.log(
+        "-" * 30
+        + " start searching with the time budget of {:} s".format(xargs.time_budget)
+    )
+    history, total_cost = regularized_evolution(
+        xargs.ea_cycles,
+        xargs.ea_population,
+        xargs.ea_sample_size,
+        xargs.time_budget,
+        random_arch,
+        mutate_arch,
+        nas_bench if args.ea_fast_by_api else None,
+        extra_info,
+        dataname,
+    )
+    logger.log(
+        "{:} regularized_evolution finish with history of {:} arch with {:.1f} s (real-cost={:.2f} s).".format(
+            time_string(), len(history), total_cost, time.time() - x_start_time
+        )
+    )
+    best_arch = max(history, key=lambda i: i.accuracy)
+    best_arch = best_arch.arch
+    logger.log("{:} best arch is {:}".format(time_string(), best_arch))
+
+    info = nas_bench.query_by_arch(best_arch, "200")
+    if info is None:
+        logger.log("Did not find this architecture : {:}.".format(best_arch))
+    else:
+        logger.log("{:}".format(info))
+    logger.log("-" * 100)
+    logger.close()
+    return logger.log_dir, nas_bench.query_index_by_arch(best_arch)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser("Regularized Evolution Algorithm")
+    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("--ea_cycles", type=int, help="The number of cycles in EA.")
+    parser.add_argument("--ea_population", type=int, help="The population size in EA.")
+    parser.add_argument("--ea_sample_size", type=int, help="The sample size in EA.")
+    parser.add_argument(
+        "--ea_fast_by_api",
+        type=int,
+        help="Use our API to speed up the experiments or not.",
+    )
+    parser.add_argument(
+        "--time_budget",
+        type=int,
+        help="The total time cost budge for searching (in seconds).",
+    )
+    # 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, default=-1, 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)
+    args.ea_fast_by_api = args.ea_fast_by_api > 0
+
+    if args.arch_nas_dataset is None or not os.path.isfile(args.arch_nas_dataset):
+        nas_bench = None
+    else:
+        print(
+            "{:} build NAS-Benchmark-API from {:}".format(
+                time_string(), args.arch_nas_dataset
+            )
+        )
+        nas_bench = API(args.arch_nas_dataset)
+    if args.rand_seed < 0:
+        save_dir, all_indexes, num = None, [], 500
+        for i in range(num):
+            print("{:} : {:03d}/{:03d}".format(time_string(), i, num))
+            args.rand_seed = random.randint(1, 100000)
+            save_dir, index = main(args, nas_bench)
+            all_indexes.append(index)
+        torch.save(all_indexes, save_dir / "results.pth")
+    else:
+        main(args, nas_bench)

exps/NAS-Bench-201-algos/SETN.py

@@ -0,0 +1,476 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
+######################################################################################
+# One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 2019 #
+######################################################################################
+import sys, time, random, argparse
+import numpy as np
+from copy import deepcopy
+import torch
+import torch.nn as nn
+from pathlib import Path
+
+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 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()
+    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_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
+        sampled_arch = network.module.dync_genotype(True)
+        network.module.set_cal_mode("dynamic", sampled_arch)
+        # network.module.set_cal_mode( 'urs' )
+        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.module.set_cal_mode("joint")
+        network.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)
+            # print (nn.functional.softmax(network.module.arch_parameters, dim=-1))
+            # print (network.module.arch_parameters)
+    return (
+        base_losses.avg,
+        base_top1.avg,
+        base_top5.avg,
+        arch_losses.avg,
+        arch_top1.avg,
+        arch_top5.avg,
+    )
+
+
+def get_best_arch(xloader, network, n_samples):
+    with torch.no_grad():
+        network.eval()
+        archs, valid_accs = network.module.return_topK(n_samples), []
+        # print ('obtain the top-{:} architectures'.format(n_samples))
+        loader_iter = iter(xloader)
+        for i, sampled_arch in enumerate(archs):
+            network.module.set_cal_mode("dynamic", sampled_arch)
+            try:
+                inputs, targets = next(loader_iter)
+            except:
+                loader_iter = iter(xloader)
+                inputs, targets = next(loader_iter)
+
+            _, logits = network(inputs)
+            val_top1, val_top5 = obtain_accuracy(
+                logits.cpu().data, targets.data, topk=(1, 5)
+            )
+
+            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()
+    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)
+            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
+    )
+    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, 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("cell", xargs.search_space_name)
+    if xargs.model_config is None:
+        model_config = dict2config(
+            dict(
+                name="SETN",
+                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,
+            dict(
+                num_classes=class_num,
+                space=search_space,
+                affine=False,
+                track_running_stats=bool(xargs.track_running_stats),
+            ),
+            None,
+        )
+    logger.log("search space : {:}".format(search_space))
+    search_model = get_cell_based_tiny_net(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 : {:}".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))
+
+    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))
+        init_genotype, _ = get_best_arch(valid_loader, network, xargs.select_num)
+        start_epoch, valid_accuracies, genotypes = 0, {"best": -1}, {-1: init_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)
+        logger.log(
+            "\n[Search the {:}-th epoch] {:}, LR={:}".format(
+                epoch_str, need_time, min(w_scheduler.get_lr())
+            )
+        )
+
+        (
+            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,
+            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, temp_accuracy = get_best_arch(valid_loader, network, xargs.select_num)
+        network.module.set_cal_mode("dynamic", genotype)
+        valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(
+            valid_loader, network, criterion
+        )
+        logger.log(
+            "[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}% | {:}".format(
+                epoch_str, valid_a_loss, valid_a_top1, valid_a_top5, genotype
+            )
+        )
+        # search_model.set_cal_mode('urs')
+        # valid_a_loss , valid_a_top1 , valid_a_top5  = valid_func(valid_loader, network, criterion)
+        # logger.log('[{:}] URS---evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
+        # search_model.set_cal_mode('joint')
+        # valid_a_loss , valid_a_top1 , valid_a_top5  = valid_func(valid_loader, network, criterion)
+        # logger.log('[{:}] JOINT-evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
+        # search_model.set_cal_mode('select')
+        # valid_a_loss , valid_a_top1 , valid_a_top5  = valid_func(valid_loader, network, criterion)
+        # logger.log('[{:}] Selec-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
+
+        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], "200")))
+        # 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, temp_accuracy = get_best_arch(valid_loader, network, xargs.select_num)
+    search_time.update(time.time() - start_time)
+    network.module.set_cal_mode("dynamic", genotype)
+    valid_a_loss, valid_a_top1, valid_a_top5 = valid_func(
+        valid_loader, network, criterion
+    )
+    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(
+        "SETN : run {:} epochs, cost {:.1f} s, last-geno is {:}.".format(
+            total_epoch, search_time.sum, genotype
+        )
+    )
+    if api is not None:
+        logger.log("{:}".format(api.query_by_arch(genotype, "200")))
+    logger.close()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser("SETN")
+    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(
+        "--select_num",
+        type=int,
+        help="The number of selected architectures to evaluate.",
+    )
+    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."
+    )
+    # 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",
+    )
+    # 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)

exps/NAS-Bench-201-algos/reinforce.py

@@ -0,0 +1,294 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+#####################################################################################################
+# modified from https://github.com/pytorch/examples/blob/master/reinforcement_learning/reinforce.py #
+#####################################################################################################
+import os, sys, time, glob, random, argparse
+import numpy as np, collections
+from copy import deepcopy
+from pathlib import Path
+import torch
+import torch.nn as nn
+from torch.distributions import Categorical
+
+from xautodl.config_utils import load_config, dict2config, configure2str
+from xautodl.datasets import get_datasets, SearchDataset
+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 CellStructure, get_search_spaces
+from nas_201_api import NASBench201API as API
+from R_EA import train_and_eval
+
+
+class Policy(nn.Module):
+    def __init__(self, max_nodes, search_space):
+        super(Policy, self).__init__()
+        self.max_nodes = max_nodes
+        self.search_space = deepcopy(search_space)
+        self.edge2index = {}
+        for i in range(1, max_nodes):
+            for j in range(i):
+                node_str = "{:}<-{:}".format(i, j)
+                self.edge2index[node_str] = len(self.edge2index)
+        self.arch_parameters = nn.Parameter(
+            1e-3 * torch.randn(len(self.edge2index), len(search_space))
+        )
+
+    def generate_arch(self, actions):
+        genotypes = []
+        for i in range(1, self.max_nodes):
+            xlist = []
+            for j in range(i):
+                node_str = "{:}<-{:}".format(i, j)
+                op_name = self.search_space[actions[self.edge2index[node_str]]]
+                xlist.append((op_name, j))
+            genotypes.append(tuple(xlist))
+        return CellStructure(genotypes)
+
+    def genotype(self):
+        genotypes = []
+        for i in range(1, self.max_nodes):
+            xlist = []
+            for j in range(i):
+                node_str = "{:}<-{:}".format(i, j)
+                with torch.no_grad():
+                    weights = self.arch_parameters[self.edge2index[node_str]]
+                    op_name = self.search_space[weights.argmax().item()]
+                xlist.append((op_name, j))
+            genotypes.append(tuple(xlist))
+        return CellStructure(genotypes)
+
+    def forward(self):
+        alphas = nn.functional.softmax(self.arch_parameters, dim=-1)
+        return alphas
+
+
+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)
+
+    def value(self):
+        """Return the current value of the moving average"""
+        return self._numerator / self._denominator
+
+
+def select_action(policy):
+    probs = policy()
+    m = Categorical(probs)
+    action = m.sample()
+    # policy.saved_log_probs.append(m.log_prob(action))
+    return m.log_prob(action), action.cpu().tolist()
+
+
+def main(xargs, nas_bench):
+    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)
+
+    if xargs.dataset == "cifar10":
+        dataname = "cifar10-valid"
+    else:
+        dataname = xargs.dataset
+    if xargs.data_path is not None:
+        train_data, valid_data, xshape, class_num = get_datasets(
+            xargs.dataset, xargs.data_path, -1
+        )
+        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))
+        config_path = "configs/nas-benchmark/algos/R-EA.config"
+        config = load_config(
+            config_path, {"class_num": class_num, "xshape": xshape}, logger
+        )
+        # To split data
+        train_data_v2 = deepcopy(train_data)
+        train_data_v2.transform = valid_data.transform
+        valid_data = train_data_v2
+        search_data = SearchDataset(xargs.dataset, train_data, train_split, valid_split)
+        # 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))
+        extra_info = {
+            "config": config,
+            "train_loader": train_loader,
+            "valid_loader": valid_loader,
+        }
+    else:
+        config_path = "configs/nas-benchmark/algos/R-EA.config"
+        config = load_config(config_path, None, logger)
+        extra_info = {"config": config, "train_loader": None, "valid_loader": None}
+        logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
+
+    search_space = get_search_spaces("cell", xargs.search_space_name)
+    policy = Policy(xargs.max_nodes, search_space)
+    optimizer = torch.optim.Adam(policy.parameters(), lr=xargs.learning_rate)
+    # optimizer = torch.optim.SGD(policy.parameters(), lr=xargs.learning_rate)
+    eps = np.finfo(np.float32).eps.item()
+    baseline = ExponentialMovingAverage(xargs.EMA_momentum)
+    logger.log("policy    : {:}".format(policy))
+    logger.log("optimizer : {:}".format(optimizer))
+    logger.log("eps       : {:}".format(eps))
+
+    # nas dataset load
+    logger.log("{:} use nas_bench : {:}".format(time_string(), nas_bench))
+
+    # REINFORCE
+    # attempts = 0
+    x_start_time = time.time()
+    logger.log(
+        "Will start searching with time budget of {:} s.".format(xargs.time_budget)
+    )
+    total_steps, total_costs, trace = 0, 0, []
+    # for istep in range(xargs.RL_steps):
+    while total_costs < xargs.time_budget:
+        start_time = time.time()
+        log_prob, action = select_action(policy)
+        arch = policy.generate_arch(action)
+        reward, cost_time = train_and_eval(arch, nas_bench, extra_info, dataname)
+        trace.append((reward, arch))
+        # accumulate time
+        if total_costs + cost_time < xargs.time_budget:
+            total_costs += cost_time
+        else:
+            break
+
+        baseline.update(reward)
+        # calculate loss
+        policy_loss = (-log_prob * (reward - baseline.value())).sum()
+        optimizer.zero_grad()
+        policy_loss.backward()
+        optimizer.step()
+        # accumulate time
+        total_costs += time.time() - start_time
+        total_steps += 1
+        logger.log(
+            "step [{:3d}] : average-reward={:.3f} : policy_loss={:.4f} : {:}".format(
+                total_steps, baseline.value(), policy_loss.item(), policy.genotype()
+            )
+        )
+        # logger.log('----> {:}'.format(policy.arch_parameters))
+        # logger.log('')
+
+    # best_arch = policy.genotype() # first version
+    best_arch = max(trace, key=lambda x: x[0])[1]
+    logger.log(
+        "REINFORCE finish with {:} steps and {:.1f} s (real cost={:.3f}).".format(
+            total_steps, total_costs, time.time() - x_start_time
+        )
+    )
+    info = nas_bench.query_by_arch(best_arch, "200")
+    if info is None:
+        logger.log("Did not find this architecture : {:}.".format(best_arch))
+    else:
+        logger.log("{:}".format(info))
+    logger.log("-" * 100)
+    logger.close()
+    return logger.log_dir, nas_bench.query_index_by_arch(best_arch)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser("The REINFORCE Algorithm")
+    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(
+        "--learning_rate", type=float, help="The learning rate for REINFORCE."
+    )
+    # parser.add_argument('--RL_steps',           type=int,   help='The steps for REINFORCE.')
+    parser.add_argument(
+        "--EMA_momentum", type=float, help="The momentum value for EMA."
+    )
+    parser.add_argument(
+        "--time_budget",
+        type=int,
+        help="The total time cost budge for searching (in seconds).",
+    )
+    # 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, default=-1, 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)
+    if args.arch_nas_dataset is None or not os.path.isfile(args.arch_nas_dataset):
+        nas_bench = None
+    else:
+        print(
+            "{:} build NAS-Benchmark-API from {:}".format(
+                time_string(), args.arch_nas_dataset
+            )
+        )
+        nas_bench = API(args.arch_nas_dataset)
+    if args.rand_seed < 0:
+        save_dir, all_indexes, num = None, [], 500
+        for i in range(num):
+            print("{:} : {:03d}/{:03d}".format(time_string(), i, num))
+            args.rand_seed = random.randint(1, 100000)
+            save_dir, index = main(args, nas_bench)
+            all_indexes.append(index)
+        torch.save(all_indexes, save_dir / "results.pth")
+    else:
+        main(args, nas_bench)