389 lines
16 KiB
Python
389 lines
16 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-sss.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/sss-collect.py #
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##############################################################################
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import os, re, sys, time, shutil, argparse, collections
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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 dict2config
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from models import CellStructure, get_cell_based_tiny_net
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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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NATS_SSS_BASE_NAME = "NATS-sss-v1_0" # 2020.08.28
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def account_one_arch(
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arch_index: int, arch_str: Text, checkpoints: List[Text], datasets: List[Text]
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) -> ArchResults:
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information = ArchResults(arch_index, arch_str)
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for checkpoint_path in checkpoints:
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try:
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checkpoint = torch.load(checkpoint_path, map_location="cpu")
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except:
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raise ValueError(
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"This checkpoint failed to be loaded : {:}".format(checkpoint_path)
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)
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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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"name": "infer.shape.tiny",
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"channels": arch_str,
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"arch_str": arch_str,
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"genotype": results["arch_config"]["genotype"],
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"class_num": results["arch_config"]["num_classes"],
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}
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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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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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information.update(dataset, int(used_seed), xresult)
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if ok_dataset < len(datasets):
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raise ValueError(
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"{:} does find enought data : {:} vs {:}".format(
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checkpoint_path, ok_dataset, len(datasets)
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)
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)
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return information
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def correct_time_related_info(hp2info: Dict[Text, ArchResults]):
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# calibrate the latency based on the number of epochs = 01, since they are trained on the same machine.
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x1 = hp2info["01"].get_metrics("cifar10-valid", "x-valid")["all_time"] / 98
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x2 = hp2info["01"].get_metrics("cifar10-valid", "ori-test")["all_time"] / 40
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cifar010_latency = (x1 + x2) / 2
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for hp, arch_info in hp2info.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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# hp2info['01'].get_latency('cifar10')
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x1 = hp2info["01"].get_metrics("cifar100", "ori-test")["all_time"] / 40
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x2 = hp2info["01"].get_metrics("cifar100", "x-test")["all_time"] / 20
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x3 = hp2info["01"].get_metrics("cifar100", "x-valid")["all_time"] / 20
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cifar100_latency = (x1 + x2 + x3) / 3
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for hp, arch_info in hp2info.items():
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arch_info.reset_latency("cifar100", None, cifar100_latency)
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x1 = hp2info["01"].get_metrics("ImageNet16-120", "ori-test")["all_time"] / 24
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x2 = hp2info["01"].get_metrics("ImageNet16-120", "x-test")["all_time"] / 12
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x3 = hp2info["01"].get_metrics("ImageNet16-120", "x-valid")["all_time"] / 12
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image_latency = (x1 + x2 + x3) / 3
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for hp, arch_info in hp2info.items():
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arch_info.reset_latency("ImageNet16-120", None, image_latency)
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# CIFAR10 VALID
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train_per_epoch_time = list(
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hp2info["01"].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 hp2info["01"].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 = sum(eval_ori_test_time) / len(eval_ori_test_time)
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eval_x_valid_time = sum(eval_x_valid_time) / len(eval_x_valid_time)
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for hp, arch_info in hp2info.items():
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arch_info.reset_pseudo_train_times("cifar10-valid", None, train_per_epoch_time)
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arch_info.reset_pseudo_eval_times(
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"cifar10-valid", None, "x-valid", eval_x_valid_time
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)
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arch_info.reset_pseudo_eval_times(
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"cifar10-valid", None, "ori-test", eval_ori_test_time
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)
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# CIFAR10
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train_per_epoch_time = list(
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hp2info["01"].query("cifar10", 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 = []
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for key, value in hp2info["01"].query("cifar10", 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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else:
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raise ValueError("-- {:} --".format(key))
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eval_ori_test_time = sum(eval_ori_test_time) / len(eval_ori_test_time)
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for hp, arch_info in hp2info.items():
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arch_info.reset_pseudo_train_times("cifar10", None, train_per_epoch_time)
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arch_info.reset_pseudo_eval_times(
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"cifar10", None, "ori-test", eval_ori_test_time
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)
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# CIFAR100
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train_per_epoch_time = list(
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hp2info["01"].query("cifar100", 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, eval_x_test_time = [], [], []
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for key, value in hp2info["01"].query("cifar100", 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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elif key.startswith("x-test@"):
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eval_x_test_time.append(value)
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else:
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raise ValueError("-- {:} --".format(key))
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eval_ori_test_time = sum(eval_ori_test_time) / len(eval_ori_test_time)
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eval_x_valid_time = sum(eval_x_valid_time) / len(eval_x_valid_time)
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eval_x_test_time = sum(eval_x_test_time) / len(eval_x_test_time)
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for hp, arch_info in hp2info.items():
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arch_info.reset_pseudo_train_times("cifar100", None, train_per_epoch_time)
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arch_info.reset_pseudo_eval_times(
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"cifar100", None, "x-valid", eval_x_valid_time
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)
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arch_info.reset_pseudo_eval_times("cifar100", None, "x-test", eval_x_test_time)
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arch_info.reset_pseudo_eval_times(
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"cifar100", None, "ori-test", eval_ori_test_time
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)
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# ImageNet16-120
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train_per_epoch_time = list(
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hp2info["01"].query("ImageNet16-120", 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, eval_x_test_time = [], [], []
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for key, value in hp2info["01"].query("ImageNet16-120", 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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elif key.startswith("x-test@"):
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eval_x_test_time.append(value)
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else:
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raise ValueError("-- {:} --".format(key))
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eval_ori_test_time = sum(eval_ori_test_time) / len(eval_ori_test_time)
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eval_x_valid_time = sum(eval_x_valid_time) / len(eval_x_valid_time)
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eval_x_test_time = sum(eval_x_test_time) / len(eval_x_test_time)
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for hp, arch_info in hp2info.items():
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arch_info.reset_pseudo_train_times("ImageNet16-120", None, train_per_epoch_time)
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arch_info.reset_pseudo_eval_times(
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"ImageNet16-120", None, "x-valid", eval_x_valid_time
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)
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arch_info.reset_pseudo_eval_times(
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"ImageNet16-120", None, "x-test", eval_x_test_time
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)
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arch_info.reset_pseudo_eval_times(
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"ImageNet16-120", None, "ori-test", eval_ori_test_time
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)
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return hp2info
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def simplify(save_dir, save_name, nets, total):
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hps, seeds = ["01", "12", "90"], 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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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(index, arch_str, ckps, datasets)
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hp2info[hp] = arch_info
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hp2info = correct_time_related_info(hp2info)
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evaluated_indexes.add(index)
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hp2info["01"].clear_params() # to save some spaces...
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to_save_data = OrderedDict(
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{
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"01": hp2info["01"].state_dict(),
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"12": hp2info["12"].state_dict(),
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"90": hp2info["90"].state_dict(),
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}
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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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{
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"01": hp2info["01"].state_dict(),
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"12": hp2info["12"].state_dict(),
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"90": hp2info["90"].state_dict(),
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}
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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_SSS_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_SSS_BASE_NAME, hd5sum)
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hd5_simple_save_dir = save_dir / "{:}-{:}-simple".format(NATS_SSS_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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pickle_save(final_infos, str(hd5_full_save_dir / "meta.pickle"))
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pickle_save(final_infos, str(hd5_simple_save_dir / "meta.pickle"))
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def traverse_net(candidates: List[int], N: int):
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nets = [""]
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for i in range(N):
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new_nets = []
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for net in nets:
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for C in candidates:
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new_nets.append(str(C) if net == "" else "{:}:{:}".format(net, C))
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nets = new_nets
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return nets
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="NATS-Bench (size 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-size",
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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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"--candidateC",
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type=int,
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nargs="+",
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default=[8, 16, 24, 32, 40, 48, 56, 64],
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help=".",
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)
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parser.add_argument(
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"--num_layers", type=int, default=5, help="The number of layers in a network."
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)
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parser.add_argument("--check_N", type=int, default=32768, 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.candidateC, args.num_layers)
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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(save_dir, args.save_name, nets, args.check_N)
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