179 lines
7.3 KiB
Python
179 lines
7.3 KiB
Python
##############################################################################
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# NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size #
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##############################################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.08 #
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##############################################################################
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# This file is used to re-orangize all checkpoints (created by main-tss.py) #
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# into a single benchmark file. Besides, for each trial, we will merge the #
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# information of all its trials into a single file. #
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# #
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# Usage: #
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# python exps/NATS-Bench/tss-collect-patcher.py #
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##############################################################################
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import os, re, sys, time, shutil, random, argparse, collections
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import numpy as np
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from copy import deepcopy
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import torch
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from tqdm import tqdm
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from pathlib import Path
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from collections import defaultdict, OrderedDict
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from typing import Dict, Any, Text, List
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lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
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if str(lib_dir) not in sys.path:
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sys.path.insert(0, str(lib_dir))
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from log_utils import AverageMeter, time_string, convert_secs2time
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from config_utils import load_config, dict2config
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from datasets import get_datasets
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from models import CellStructure, get_cell_based_tiny_net, get_search_spaces
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from nats_bench import pickle_save, pickle_load, ArchResults, ResultsCount
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from procedures import bench_pure_evaluate as pure_evaluate, get_nas_bench_loaders
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from utils import get_md5_file
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from nas_201_api import NASBench201API
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NATS_TSS_BASE_NAME = "NATS-tss-v1_0" # 2020.08.28
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def simplify(save_dir, save_name, nets, total, sup_config):
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hps, seeds = ["12", "200"], set()
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for hp in hps:
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sub_save_dir = save_dir / "raw-data-{:}".format(hp)
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ckps = sorted(list(sub_save_dir.glob("arch-*-seed-*.pth")))
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seed2names = defaultdict(list)
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for ckp in ckps:
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parts = re.split("-|\.", ckp.name)
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seed2names[parts[3]].append(ckp.name)
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print("DIR : {:}".format(sub_save_dir))
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nums = []
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for seed, xlist in seed2names.items():
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seeds.add(seed)
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nums.append(len(xlist))
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print(" [seed={:}] there are {:} checkpoints.".format(seed, len(xlist)))
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assert (
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len(nets) == total == max(nums)
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), "there are some missed files : {:} vs {:}".format(max(nums), total)
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print("{:} start simplify the checkpoint.".format(time_string()))
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datasets = ("cifar10-valid", "cifar10", "cifar100", "ImageNet16-120")
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# Create the directory to save the processed data
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# full_save_dir contains all benchmark files with trained weights.
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# simplify_save_dir contains all benchmark files without trained weights.
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full_save_dir = save_dir / (save_name + "-FULL")
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simple_save_dir = save_dir / (save_name + "-SIMPLIFY")
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full_save_dir.mkdir(parents=True, exist_ok=True)
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simple_save_dir.mkdir(parents=True, exist_ok=True)
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# all data in memory
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arch2infos, evaluated_indexes = dict(), set()
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end_time, arch_time = time.time(), AverageMeter()
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# save the meta information
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for index in tqdm(range(total)):
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arch_str = nets[index]
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hp2info = OrderedDict()
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simple_save_path = simple_save_dir / "{:06d}.pickle".format(index)
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arch2infos[index] = pickle_load(simple_save_path)
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evaluated_indexes.add(index)
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# measure elapsed time
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arch_time.update(time.time() - end_time)
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end_time = time.time()
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need_time = "{:}".format(
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convert_secs2time(arch_time.avg * (total - index - 1), True)
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)
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# print('{:} {:06d}/{:06d} : still need {:}'.format(time_string(), index, total, need_time))
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print("{:} {:} done.".format(time_string(), save_name))
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final_infos = {
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"meta_archs": nets,
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"total_archs": total,
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"arch2infos": arch2infos,
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"evaluated_indexes": evaluated_indexes,
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}
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save_file_name = save_dir / "{:}.pickle".format(save_name)
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pickle_save(final_infos, str(save_file_name))
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# move the benchmark file to a new path
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hd5sum = get_md5_file(str(save_file_name) + ".pbz2")
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hd5_file_name = save_dir / "{:}-{:}.pickle.pbz2".format(NATS_TSS_BASE_NAME, hd5sum)
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shutil.move(str(save_file_name) + ".pbz2", hd5_file_name)
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print(
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"Save {:} / {:} architecture results into {:} -> {:}.".format(
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len(evaluated_indexes), total, save_file_name, hd5_file_name
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)
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)
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# move the directory to a new path
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hd5_full_save_dir = save_dir / "{:}-{:}-full".format(NATS_TSS_BASE_NAME, hd5sum)
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hd5_simple_save_dir = save_dir / "{:}-{:}-simple".format(NATS_TSS_BASE_NAME, hd5sum)
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shutil.move(full_save_dir, hd5_full_save_dir)
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shutil.move(simple_save_dir, hd5_simple_save_dir)
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def traverse_net(max_node):
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aa_nas_bench_ss = get_search_spaces("cell", "nats-bench")
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archs = CellStructure.gen_all(aa_nas_bench_ss, max_node, False)
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print(
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"There are {:} archs vs {:}.".format(
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len(archs), len(aa_nas_bench_ss) ** ((max_node - 1) * max_node / 2)
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)
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)
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random.seed(88) # please do not change this line for reproducibility
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random.shuffle(archs)
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assert (
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archs[0].tostr()
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== "|avg_pool_3x3~0|+|nor_conv_1x1~0|skip_connect~1|+|nor_conv_1x1~0|skip_connect~1|skip_connect~2|"
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), "please check the 0-th architecture : {:}".format(archs[0])
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assert (
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archs[9].tostr()
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== "|avg_pool_3x3~0|+|none~0|none~1|+|skip_connect~0|none~1|nor_conv_3x3~2|"
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), "please check the 9-th architecture : {:}".format(archs[9])
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assert (
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archs[123].tostr()
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== "|avg_pool_3x3~0|+|avg_pool_3x3~0|nor_conv_1x1~1|+|none~0|avg_pool_3x3~1|nor_conv_3x3~2|"
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), "please check the 123-th architecture : {:}".format(archs[123])
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return [x.tostr() for x in archs]
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="NATS-Bench (topology search space)",
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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)
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parser.add_argument(
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"--base_save_dir",
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type=str,
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default="./output/NATS-Bench-topology",
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help="The base-name of folder to save checkpoints and log.",
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)
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parser.add_argument(
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"--max_node", type=int, default=4, help="The maximum node in a cell."
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)
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parser.add_argument(
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"--channel", type=int, default=16, help="The number of channels."
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)
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parser.add_argument(
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"--num_cells", type=int, default=5, help="The number of cells in one stage."
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)
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parser.add_argument("--check_N", type=int, default=15625, help="For safety.")
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parser.add_argument(
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"--save_name", type=str, default="process", help="The save directory."
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)
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args = parser.parse_args()
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nets = traverse_net(args.max_node)
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if len(nets) != args.check_N:
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raise ValueError(
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"Pre-num-check failed : {:} vs {:}".format(len(nets), args.check_N)
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)
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save_dir = Path(args.base_save_dir)
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simplify(
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save_dir,
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args.save_name,
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nets,
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args.check_N,
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{"name": "infer.tiny", "channel": args.channel, "num_cells": args.num_cells},
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)
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