190 lines
7.4 KiB
Python
190 lines
7.4 KiB
Python
##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020 #
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##############################################################################
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import os, sys, time, glob, random, argparse
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import numpy as np, collections
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from copy import deepcopy
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import torch
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import torch.nn as nn
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from pathlib import Path
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from xautodl.config_utils import load_config, dict2config, configure2str
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from xautodl.datasets import get_datasets, SearchDataset
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from xautodl.procedures import (
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prepare_seed,
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prepare_logger,
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save_checkpoint,
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copy_checkpoint,
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get_optim_scheduler,
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)
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from xautodl.utils import get_model_infos, obtain_accuracy
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from xautodl.log_utils import AverageMeter, time_string, convert_secs2time
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from xautodl.models import get_search_spaces
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from nas_201_api import NASBench201API as API
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from R_EA import train_and_eval, random_architecture_func
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def main(xargs, nas_bench):
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assert torch.cuda.is_available(), "CUDA is not available."
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torch.backends.cudnn.enabled = True
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torch.backends.cudnn.benchmark = False
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torch.backends.cudnn.deterministic = True
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torch.set_num_threads(xargs.workers)
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prepare_seed(xargs.rand_seed)
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logger = prepare_logger(args)
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if xargs.dataset == "cifar10":
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dataname = "cifar10-valid"
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else:
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dataname = xargs.dataset
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if xargs.data_path is not None:
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train_data, valid_data, xshape, class_num = get_datasets(
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xargs.dataset, xargs.data_path, -1
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)
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split_Fpath = "configs/nas-benchmark/cifar-split.txt"
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cifar_split = load_config(split_Fpath, None, None)
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train_split, valid_split = cifar_split.train, cifar_split.valid
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logger.log("Load split file from {:}".format(split_Fpath))
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config_path = "configs/nas-benchmark/algos/R-EA.config"
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config = load_config(
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config_path, {"class_num": class_num, "xshape": xshape}, logger
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)
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# To split data
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train_data_v2 = deepcopy(train_data)
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train_data_v2.transform = valid_data.transform
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valid_data = train_data_v2
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search_data = SearchDataset(xargs.dataset, train_data, train_split, valid_split)
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# data loader
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train_loader = torch.utils.data.DataLoader(
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train_data,
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batch_size=config.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(train_split),
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num_workers=xargs.workers,
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pin_memory=True,
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)
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valid_loader = torch.utils.data.DataLoader(
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valid_data,
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batch_size=config.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split),
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num_workers=xargs.workers,
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pin_memory=True,
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)
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logger.log(
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"||||||| {:10s} ||||||| Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}".format(
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xargs.dataset, len(train_loader), len(valid_loader), config.batch_size
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)
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)
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logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
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extra_info = {
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"config": config,
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"train_loader": train_loader,
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"valid_loader": valid_loader,
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}
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else:
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config_path = "configs/nas-benchmark/algos/R-EA.config"
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config = load_config(config_path, None, logger)
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logger.log("||||||| {:10s} ||||||| Config={:}".format(xargs.dataset, config))
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extra_info = {"config": config, "train_loader": None, "valid_loader": None}
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search_space = get_search_spaces("cell", xargs.search_space_name)
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random_arch = random_architecture_func(xargs.max_nodes, search_space)
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# x =random_arch() ; y = mutate_arch(x)
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x_start_time = time.time()
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logger.log("{:} use nas_bench : {:}".format(time_string(), nas_bench))
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best_arch, best_acc, total_time_cost, history = None, -1, 0, []
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# for idx in range(xargs.random_num):
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while total_time_cost < xargs.time_budget:
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arch = random_arch()
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accuracy, cost_time = train_and_eval(arch, nas_bench, extra_info, dataname)
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if total_time_cost + cost_time > xargs.time_budget:
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break
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else:
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total_time_cost += cost_time
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history.append(arch)
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if best_arch is None or best_acc < accuracy:
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best_acc, best_arch = accuracy, arch
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logger.log(
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"[{:03d}] : {:} : accuracy = {:.2f}%".format(len(history), arch, accuracy)
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)
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logger.log(
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"{:} best arch is {:}, accuracy = {:.2f}%, visit {:} archs with {:.1f} s (real-cost = {:.3f} s).".format(
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time_string(),
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best_arch,
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best_acc,
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len(history),
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total_time_cost,
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time.time() - x_start_time,
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)
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)
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info = nas_bench.query_by_arch(best_arch, "200")
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if info is None:
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logger.log("Did not find this architecture : {:}.".format(best_arch))
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else:
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logger.log("{:}".format(info))
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logger.log("-" * 100)
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logger.close()
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return logger.log_dir, nas_bench.query_index_by_arch(best_arch)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser("Random NAS")
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parser.add_argument("--data_path", type=str, help="Path to dataset")
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parser.add_argument(
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"--dataset",
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type=str,
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choices=["cifar10", "cifar100", "ImageNet16-120"],
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help="Choose between Cifar10/100 and ImageNet-16.",
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)
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# channels and number-of-cells
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parser.add_argument("--search_space_name", type=str, help="The search space name.")
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parser.add_argument("--max_nodes", type=int, help="The maximum number of nodes.")
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parser.add_argument("--channel", type=int, help="The number of channels.")
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parser.add_argument(
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"--num_cells", type=int, help="The number of cells in one stage."
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)
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# parser.add_argument('--random_num', type=int, help='The number of random selected architectures.')
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parser.add_argument(
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"--time_budget",
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type=int,
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help="The total time cost budge for searching (in seconds).",
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)
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# log
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parser.add_argument(
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"--workers",
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type=int,
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default=2,
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help="number of data loading workers (default: 2)",
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)
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parser.add_argument(
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"--save_dir", type=str, help="Folder to save checkpoints and log."
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)
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parser.add_argument(
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"--arch_nas_dataset",
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type=str,
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help="The path to load the architecture dataset (tiny-nas-benchmark).",
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)
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parser.add_argument("--print_freq", type=int, help="print frequency (default: 200)")
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parser.add_argument("--rand_seed", type=int, help="manual seed")
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args = parser.parse_args()
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# if args.rand_seed is None or args.rand_seed < 0: args.rand_seed = random.randint(1, 100000)
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if args.arch_nas_dataset is None or not os.path.isfile(args.arch_nas_dataset):
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nas_bench = None
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else:
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print(
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"{:} build NAS-Benchmark-API from {:}".format(
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time_string(), args.arch_nas_dataset
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)
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)
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nas_bench = API(args.arch_nas_dataset)
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if args.rand_seed < 0:
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save_dir, all_indexes, num = None, [], 500
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for i in range(num):
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print("{:} : {:03d}/{:03d}".format(time_string(), i, num))
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args.rand_seed = random.randint(1, 100000)
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save_dir, index = main(args, nas_bench)
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all_indexes.append(index)
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torch.save(all_indexes, save_dir / "results.pth")
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else:
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main(args, nas_bench)
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