462 lines
18 KiB
Python
462 lines
18 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.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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api = NASBench201API(
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"{:}/.torch/NAS-Bench-201-v1_0-e61699.pth".format(os.environ["HOME"])
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)
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NATS_TSS_BASE_NAME = "NATS-tss-v1_0" # 2020.08.28
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def create_result_count(
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used_seed: int,
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dataset: Text,
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arch_config: Dict[Text, Any],
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results: Dict[Text, Any],
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dataloader_dict: Dict[Text, Any],
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) -> ResultsCount:
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xresult = ResultsCount(
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dataset,
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results["net_state_dict"],
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results["train_acc1es"],
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results["train_losses"],
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results["param"],
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results["flop"],
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arch_config,
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used_seed,
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results["total_epoch"],
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None,
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)
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net_config = dict2config(
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{
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"name": "infer.tiny",
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"C": arch_config["channel"],
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"N": arch_config["num_cells"],
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"genotype": CellStructure.str2structure(arch_config["arch_str"]),
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"num_classes": arch_config["class_num"],
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},
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None,
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)
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if "train_times" in results: # new version
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xresult.update_train_info(
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results["train_acc1es"],
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results["train_acc5es"],
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results["train_losses"],
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results["train_times"],
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)
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xresult.update_eval(
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results["valid_acc1es"], results["valid_losses"], results["valid_times"]
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)
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else:
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network = get_cell_based_tiny_net(net_config)
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network.load_state_dict(xresult.get_net_param())
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if dataset == "cifar10-valid":
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xresult.update_OLD_eval(
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"x-valid", results["valid_acc1es"], results["valid_losses"]
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)
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loss, top1, top5, latencies = pure_evaluate(
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dataloader_dict["{:}@{:}".format("cifar10", "test")], network.cuda()
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)
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xresult.update_OLD_eval(
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"ori-test",
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{results["total_epoch"] - 1: top1},
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{results["total_epoch"] - 1: loss},
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)
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xresult.update_latency(latencies)
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elif dataset == "cifar10":
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xresult.update_OLD_eval(
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"ori-test", results["valid_acc1es"], results["valid_losses"]
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)
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loss, top1, top5, latencies = pure_evaluate(
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dataloader_dict["{:}@{:}".format(dataset, "test")], network.cuda()
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)
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xresult.update_latency(latencies)
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elif dataset == "cifar100" or dataset == "ImageNet16-120":
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xresult.update_OLD_eval(
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"ori-test", results["valid_acc1es"], results["valid_losses"]
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)
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loss, top1, top5, latencies = pure_evaluate(
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dataloader_dict["{:}@{:}".format(dataset, "valid")], network.cuda()
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)
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xresult.update_OLD_eval(
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"x-valid",
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{results["total_epoch"] - 1: top1},
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{results["total_epoch"] - 1: loss},
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)
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loss, top1, top5, latencies = pure_evaluate(
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dataloader_dict["{:}@{:}".format(dataset, "test")], network.cuda()
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)
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xresult.update_OLD_eval(
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"x-test",
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{results["total_epoch"] - 1: top1},
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{results["total_epoch"] - 1: loss},
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)
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xresult.update_latency(latencies)
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else:
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raise ValueError("invalid dataset name : {:}".format(dataset))
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return xresult
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def account_one_arch(arch_index, arch_str, checkpoints, datasets, dataloader_dict):
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information = ArchResults(arch_index, arch_str)
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for checkpoint_path in checkpoints:
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checkpoint = torch.load(checkpoint_path, map_location="cpu")
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used_seed = checkpoint_path.name.split("-")[-1].split(".")[0]
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ok_dataset = 0
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for dataset in datasets:
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if dataset not in checkpoint:
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print(
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"Can not find {:} in arch-{:} from {:}".format(
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dataset, arch_index, checkpoint_path
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)
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)
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continue
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else:
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ok_dataset += 1
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results = checkpoint[dataset]
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assert results[
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"finish-train"
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], "This {:} arch seed={:} does not finish train on {:} ::: {:}".format(
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arch_index, used_seed, dataset, checkpoint_path
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)
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arch_config = {
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"channel": results["channel"],
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"num_cells": results["num_cells"],
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"arch_str": arch_str,
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"class_num": results["config"]["class_num"],
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}
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xresult = create_result_count(
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used_seed, dataset, arch_config, results, dataloader_dict
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)
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information.update(dataset, int(used_seed), xresult)
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if ok_dataset == 0:
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raise ValueError("{:} does not find any data".format(checkpoint_path))
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return information
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def correct_time_related_info(arch_index: int, arch_infos: Dict[Text, ArchResults]):
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# calibrate the latency based on NAS-Bench-201-v1_0-e61699.pth
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cifar010_latency = (
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api.get_latency(arch_index, "cifar10-valid", hp="200")
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+ api.get_latency(arch_index, "cifar10", hp="200")
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) / 2
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cifar100_latency = api.get_latency(arch_index, "cifar100", hp="200")
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image_latency = api.get_latency(arch_index, "ImageNet16-120", hp="200")
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for hp, arch_info in arch_infos.items():
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arch_info.reset_latency("cifar10-valid", None, cifar010_latency)
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arch_info.reset_latency("cifar10", None, cifar010_latency)
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arch_info.reset_latency("cifar100", None, cifar100_latency)
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arch_info.reset_latency("ImageNet16-120", None, image_latency)
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train_per_epoch_time = list(
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arch_infos["12"].query("cifar10-valid", 777).train_times.values()
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)
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train_per_epoch_time = sum(train_per_epoch_time) / len(train_per_epoch_time)
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eval_ori_test_time, eval_x_valid_time = [], []
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for key, value in arch_infos["12"].query("cifar10-valid", 777).eval_times.items():
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if key.startswith("ori-test@"):
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eval_ori_test_time.append(value)
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elif key.startswith("x-valid@"):
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eval_x_valid_time.append(value)
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else:
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raise ValueError("-- {:} --".format(key))
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eval_ori_test_time, eval_x_valid_time = float(np.mean(eval_ori_test_time)), float(
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np.mean(eval_x_valid_time)
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)
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nums = {
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"ImageNet16-120-train": 151700,
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"ImageNet16-120-valid": 3000,
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"ImageNet16-120-test": 6000,
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"cifar10-valid-train": 25000,
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"cifar10-valid-valid": 25000,
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"cifar10-train": 50000,
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"cifar10-test": 10000,
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"cifar100-train": 50000,
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"cifar100-test": 10000,
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"cifar100-valid": 5000,
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}
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eval_per_sample = (eval_ori_test_time + eval_x_valid_time) / (
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nums["cifar10-valid-valid"] + nums["cifar10-test"]
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)
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for hp, arch_info in arch_infos.items():
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arch_info.reset_pseudo_train_times(
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"cifar10-valid",
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None,
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train_per_epoch_time
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/ nums["cifar10-valid-train"]
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* nums["cifar10-valid-train"],
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)
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arch_info.reset_pseudo_train_times(
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"cifar10",
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None,
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train_per_epoch_time / nums["cifar10-valid-train"] * nums["cifar10-train"],
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)
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arch_info.reset_pseudo_train_times(
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"cifar100",
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None,
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train_per_epoch_time / nums["cifar10-valid-train"] * nums["cifar100-train"],
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)
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arch_info.reset_pseudo_train_times(
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"ImageNet16-120",
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None,
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train_per_epoch_time
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/ nums["cifar10-valid-train"]
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* nums["ImageNet16-120-train"],
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)
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arch_info.reset_pseudo_eval_times(
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"cifar10-valid",
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None,
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"x-valid",
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eval_per_sample * nums["cifar10-valid-valid"],
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)
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arch_info.reset_pseudo_eval_times(
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"cifar10-valid", None, "ori-test", eval_per_sample * nums["cifar10-test"]
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)
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arch_info.reset_pseudo_eval_times(
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"cifar10", None, "ori-test", eval_per_sample * nums["cifar10-test"]
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)
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arch_info.reset_pseudo_eval_times(
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"cifar100", None, "x-valid", eval_per_sample * nums["cifar100-valid"]
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)
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arch_info.reset_pseudo_eval_times(
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"cifar100", None, "x-test", eval_per_sample * nums["cifar100-valid"]
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)
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arch_info.reset_pseudo_eval_times(
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"cifar100", None, "ori-test", eval_per_sample * nums["cifar100-test"]
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)
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arch_info.reset_pseudo_eval_times(
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"ImageNet16-120",
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None,
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"x-valid",
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eval_per_sample * nums["ImageNet16-120-valid"],
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)
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arch_info.reset_pseudo_eval_times(
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"ImageNet16-120",
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None,
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"x-test",
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eval_per_sample * nums["ImageNet16-120-valid"],
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)
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arch_info.reset_pseudo_eval_times(
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"ImageNet16-120",
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None,
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"ori-test",
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eval_per_sample * nums["ImageNet16-120-test"],
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)
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return arch_infos
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def simplify(save_dir, save_name, nets, total, sup_config):
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dataloader_dict = get_nas_bench_loaders(6)
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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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temp_final_infos = {
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"meta_archs": nets,
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"total_archs": total,
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"arch2infos": None,
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"evaluated_indexes": set(),
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}
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pickle_save(temp_final_infos, str(full_save_dir / "meta.pickle"))
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pickle_save(temp_final_infos, str(simple_save_dir / "meta.pickle"))
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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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full_save_path = full_save_dir / "{:06d}.pickle".format(index)
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simple_save_path = simple_save_dir / "{:06d}.pickle".format(index)
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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 = [
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sub_save_dir / "arch-{:06d}-seed-{:}.pth".format(index, seed)
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for seed in seeds
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]
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ckps = [x for x in ckps if x.exists()]
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if len(ckps) == 0:
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raise ValueError("Invalid data : index={:}, hp={:}".format(index, hp))
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arch_info = account_one_arch(
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index, arch_str, ckps, datasets, dataloader_dict
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)
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hp2info[hp] = arch_info
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hp2info = correct_time_related_info(index, hp2info)
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evaluated_indexes.add(index)
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to_save_data = OrderedDict(
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{"12": hp2info["12"].state_dict(), "200": hp2info["200"].state_dict()}
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)
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pickle_save(to_save_data, str(full_save_path))
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for hp in hps:
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hp2info[hp].clear_params()
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to_save_data = OrderedDict(
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{"12": hp2info["12"].state_dict(), "200": hp2info["200"].state_dict()}
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)
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pickle_save(to_save_data, str(simple_save_path))
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arch2infos[index] = to_save_data
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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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# save the meta information for simple and full
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# final_infos['arch2infos'] = None
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# final_infos['evaluated_indexes'] = set()
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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(
|
|
"--max_node", type=int, default=4, help="The maximum node in a cell."
|
|
)
|
|
parser.add_argument(
|
|
"--channel", type=int, default=16, help="The number of channels."
|
|
)
|
|
parser.add_argument(
|
|
"--num_cells", type=int, default=5, help="The number of cells in one stage."
|
|
)
|
|
parser.add_argument("--check_N", type=int, default=15625, help="For safety.")
|
|
parser.add_argument(
|
|
"--save_name", type=str, default="process", help="The save directory."
|
|
)
|
|
args = parser.parse_args()
|
|
|
|
nets = traverse_net(args.max_node)
|
|
if len(nets) != args.check_N:
|
|
raise ValueError(
|
|
"Pre-num-check failed : {:} vs {:}".format(len(nets), args.check_N)
|
|
)
|
|
|
|
save_dir = Path(args.base_save_dir)
|
|
simplify(
|
|
save_dir,
|
|
args.save_name,
|
|
nets,
|
|
args.check_N,
|
|
{"name": "infer.tiny", "channel": args.channel, "num_cells": args.num_cells},
|
|
)
|