700 lines
26 KiB
Python
700 lines
26 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], 2020.07 #
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##############################################################################
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# This file is used to train (all) architecture candidate in the topology #
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# search space in NATS-Bench (tss) with different hyper-parameters. #
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# When use mode=new, it will automatically detect whether the checkpoint of #
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# a trial exists, if so, it will skip this trial. When use mode=cover, it #
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# will ignore the (possible) existing checkpoint, run each trial, and save. #
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##############################################################################
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# Please use the script of scripts/NATS-Bench/train-topology.sh to run. #
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# bash scripts/NATS-Bench/train-topology.sh 00000-15624 12 777 #
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# bash scripts/NATS-Bench/train-topology.sh 00000-15624 200 '777 888 999' #
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# #
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################ #
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# [Deprecated Function: Generate the meta information] #
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# python ./exps/NATS-Bench/main-tss.py --mode meta #
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##############################################################################
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import os, sys, time, torch, random, argparse
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from typing import List, Text, Dict, Any
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from PIL import ImageFile
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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from copy import deepcopy
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from pathlib import Path
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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 config_utils import dict2config, load_config
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from procedures import bench_evaluate_for_seed
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from procedures import get_machine_info
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from datasets import get_datasets
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from log_utils import Logger, AverageMeter, time_string, convert_secs2time
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from models import CellStructure, CellArchitectures, get_search_spaces
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from utils import split_str2indexes
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def evaluate_all_datasets(
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arch: Text,
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datasets: List[Text],
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xpaths: List[Text],
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splits: List[Text],
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config_path: Text,
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seed: int,
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raw_arch_config,
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workers,
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logger,
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):
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machine_info, raw_arch_config = get_machine_info(), deepcopy(raw_arch_config)
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all_infos = {"info": machine_info}
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all_dataset_keys = []
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# look all the datasets
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for dataset, xpath, split in zip(datasets, xpaths, splits):
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# train valid data
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train_data, valid_data, xshape, class_num = get_datasets(dataset, xpath, -1)
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# load the configuration
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if dataset == "cifar10" or dataset == "cifar100":
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split_info = load_config(
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"configs/nas-benchmark/cifar-split.txt", None, None
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)
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elif dataset.startswith("ImageNet16"):
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split_info = load_config(
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"configs/nas-benchmark/{:}-split.txt".format(dataset), None, None
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)
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else:
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raise ValueError("invalid dataset : {:}".format(dataset))
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config = load_config(
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config_path, dict(class_num=class_num, xshape=xshape), logger
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)
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# check whether use splited validation set
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if bool(split):
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assert dataset == "cifar10"
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ValLoaders = {
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"ori-test": torch.utils.data.DataLoader(
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valid_data,
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batch_size=config.batch_size,
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shuffle=False,
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num_workers=workers,
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pin_memory=True,
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)
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}
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assert len(train_data) == len(split_info.train) + len(
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split_info.valid
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), "invalid length : {:} vs {:} + {:}".format(
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len(train_data), len(split_info.train), len(split_info.valid)
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)
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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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# 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(split_info.train),
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num_workers=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(split_info.valid),
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num_workers=workers,
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pin_memory=True,
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)
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ValLoaders["x-valid"] = valid_loader
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else:
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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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shuffle=True,
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num_workers=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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shuffle=False,
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num_workers=workers,
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pin_memory=True,
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)
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if dataset == "cifar10":
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ValLoaders = {"ori-test": valid_loader}
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elif dataset == "cifar100":
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cifar100_splits = load_config(
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"configs/nas-benchmark/cifar100-test-split.txt", None, None
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)
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ValLoaders = {
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"ori-test": valid_loader,
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"x-valid": 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(
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cifar100_splits.xvalid
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),
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num_workers=workers,
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pin_memory=True,
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),
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"x-test": 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(
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cifar100_splits.xtest
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),
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num_workers=workers,
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pin_memory=True,
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),
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}
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elif dataset == "ImageNet16-120":
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imagenet16_splits = load_config(
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"configs/nas-benchmark/imagenet-16-120-test-split.txt", None, None
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)
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ValLoaders = {
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"ori-test": valid_loader,
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"x-valid": 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(
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imagenet16_splits.xvalid
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),
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num_workers=workers,
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pin_memory=True,
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),
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"x-test": 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(
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imagenet16_splits.xtest
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),
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num_workers=workers,
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pin_memory=True,
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),
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}
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else:
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raise ValueError("invalid dataset : {:}".format(dataset))
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dataset_key = "{:}".format(dataset)
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if bool(split):
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dataset_key = dataset_key + "-valid"
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logger.log(
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"Evaluate ||||||| {:10s} ||||||| Train-Num={:}, Valid-Num={:}, Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}".format(
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dataset_key,
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len(train_data),
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len(valid_data),
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len(train_loader),
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len(valid_loader),
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config.batch_size,
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)
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)
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logger.log(
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"Evaluate ||||||| {:10s} ||||||| Config={:}".format(dataset_key, config)
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)
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for key, value in ValLoaders.items():
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logger.log(
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"Evaluate ---->>>> {:10s} with {:} batchs".format(key, len(value))
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)
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arch_config = dict2config(
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dict(
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name="infer.tiny",
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C=raw_arch_config["channel"],
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N=raw_arch_config["num_cells"],
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genotype=arch,
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num_classes=config.class_num,
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),
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None,
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)
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results = bench_evaluate_for_seed(
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arch_config, config, train_loader, ValLoaders, seed, logger
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)
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all_infos[dataset_key] = results
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all_dataset_keys.append(dataset_key)
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all_infos["all_dataset_keys"] = all_dataset_keys
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return all_infos
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def main(
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save_dir: Path,
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workers: int,
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datasets: List[Text],
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xpaths: List[Text],
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splits: List[int],
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seeds: List[int],
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nets: List[str],
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opt_config: Dict[Text, Any],
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to_evaluate_indexes: tuple,
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cover_mode: bool,
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arch_config: Dict[Text, Any],
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):
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log_dir = save_dir / "logs"
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log_dir.mkdir(parents=True, exist_ok=True)
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logger = Logger(str(log_dir), os.getpid(), False)
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logger.log("xargs : seeds = {:}".format(seeds))
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logger.log("xargs : cover_mode = {:}".format(cover_mode))
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logger.log("-" * 100)
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logger.log(
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"Start evaluating range =: {:06d} - {:06d}".format(
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min(to_evaluate_indexes), max(to_evaluate_indexes)
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)
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+ "({:} in total) / {:06d} with cover-mode={:}".format(
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len(to_evaluate_indexes), len(nets), cover_mode
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)
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)
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for i, (dataset, xpath, split) in enumerate(zip(datasets, xpaths, splits)):
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logger.log(
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"--->>> Evaluate {:}/{:} : dataset={:9s}, path={:}, split={:}".format(
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i, len(datasets), dataset, xpath, split
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)
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)
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logger.log("--->>> optimization config : {:}".format(opt_config))
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start_time, epoch_time = time.time(), AverageMeter()
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for i, index in enumerate(to_evaluate_indexes):
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arch = nets[index]
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logger.log(
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"\n{:} evaluate {:06d}/{:06d} ({:06d}/{:06d})-th arch [seeds={:}] {:}".format(
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time_string(),
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i,
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len(to_evaluate_indexes),
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index,
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len(nets),
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seeds,
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"-" * 15,
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)
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)
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logger.log("{:} {:} {:}".format("-" * 15, arch, "-" * 15))
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# test this arch on different datasets with different seeds
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has_continue = False
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for seed in seeds:
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to_save_name = save_dir / "arch-{:06d}-seed-{:04d}.pth".format(index, seed)
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if to_save_name.exists():
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if cover_mode:
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logger.log(
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"Find existing file : {:}, remove it before evaluation".format(
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to_save_name
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)
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)
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os.remove(str(to_save_name))
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else:
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logger.log(
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"Find existing file : {:}, skip this evaluation".format(
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to_save_name
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)
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)
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has_continue = True
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continue
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results = evaluate_all_datasets(
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CellStructure.str2structure(arch),
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datasets,
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xpaths,
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splits,
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opt_config,
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seed,
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arch_config,
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workers,
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logger,
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)
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torch.save(results, to_save_name)
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logger.log(
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"\n{:} evaluate {:06d}/{:06d} ({:06d}/{:06d})-th arch [seeds={:}] ===>>> {:}".format(
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time_string(),
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i,
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len(to_evaluate_indexes),
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index,
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len(nets),
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seeds,
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to_save_name,
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)
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)
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# measure elapsed time
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if not has_continue:
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epoch_time.update(time.time() - start_time)
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start_time = time.time()
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need_time = "Time Left: {:}".format(
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convert_secs2time(epoch_time.avg * (len(to_evaluate_indexes) - i - 1), True)
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)
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logger.log(
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"This arch costs : {:}".format(convert_secs2time(epoch_time.val, True))
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)
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logger.log("{:}".format("*" * 100))
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logger.log(
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"{:} {:74s} {:}".format(
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"*" * 10,
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"{:06d}/{:06d} ({:06d}/{:06d})-th done, left {:}".format(
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i, len(to_evaluate_indexes), index, len(nets), need_time
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),
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"*" * 10,
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)
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)
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logger.log("{:}".format("*" * 100))
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logger.close()
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def train_single_model(
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save_dir, workers, datasets, xpaths, splits, use_less, seeds, model_str, arch_config
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):
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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.deterministic = True
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# torch.backends.cudnn.benchmark = True
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torch.set_num_threads(workers)
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save_dir = (
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Path(save_dir)
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/ "specifics"
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/ "{:}-{:}-{:}-{:}".format(
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"LESS" if use_less else "FULL",
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model_str,
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arch_config["channel"],
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arch_config["num_cells"],
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)
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)
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logger = Logger(str(save_dir), 0, False)
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if model_str in CellArchitectures:
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arch = CellArchitectures[model_str]
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logger.log(
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"The model string is found in pre-defined architecture dict : {:}".format(
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model_str
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)
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)
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else:
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try:
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arch = CellStructure.str2structure(model_str)
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except:
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raise ValueError(
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"Invalid model string : {:}. It can not be found or parsed.".format(
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model_str
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)
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)
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assert arch.check_valid_op(
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get_search_spaces("cell", "full")
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), "{:} has the invalid op.".format(arch)
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logger.log("Start train-evaluate {:}".format(arch.tostr()))
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logger.log("arch_config : {:}".format(arch_config))
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start_time, seed_time = time.time(), AverageMeter()
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for _is, seed in enumerate(seeds):
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logger.log(
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"\nThe {:02d}/{:02d}-th seed is {:} ----------------------<.>----------------------".format(
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_is, len(seeds), seed
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)
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)
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to_save_name = save_dir / "seed-{:04d}.pth".format(seed)
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if to_save_name.exists():
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logger.log(
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"Find the existing file {:}, directly load!".format(to_save_name)
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)
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checkpoint = torch.load(to_save_name)
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else:
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logger.log(
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"Does not find the existing file {:}, train and evaluate!".format(
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to_save_name
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)
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)
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checkpoint = evaluate_all_datasets(
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arch,
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datasets,
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xpaths,
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splits,
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use_less,
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seed,
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arch_config,
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workers,
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logger,
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)
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torch.save(checkpoint, to_save_name)
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# log information
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logger.log("{:}".format(checkpoint["info"]))
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all_dataset_keys = checkpoint["all_dataset_keys"]
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for dataset_key in all_dataset_keys:
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logger.log(
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"\n{:} dataset : {:} {:}".format("-" * 15, dataset_key, "-" * 15)
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)
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dataset_info = checkpoint[dataset_key]
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# logger.log('Network ==>\n{:}'.format( dataset_info['net_string'] ))
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logger.log(
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"Flops = {:} MB, Params = {:} MB".format(
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dataset_info["flop"], dataset_info["param"]
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)
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)
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logger.log("config : {:}".format(dataset_info["config"]))
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logger.log(
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"Training State (finish) = {:}".format(dataset_info["finish-train"])
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)
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last_epoch = dataset_info["total_epoch"] - 1
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train_acc1es, train_acc5es = (
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dataset_info["train_acc1es"],
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dataset_info["train_acc5es"],
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)
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valid_acc1es, valid_acc5es = (
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dataset_info["valid_acc1es"],
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dataset_info["valid_acc5es"],
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)
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logger.log(
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"Last Info : Train = Acc@1 {:.2f}% Acc@5 {:.2f}% Error@1 {:.2f}%, Test = Acc@1 {:.2f}% Acc@5 {:.2f}% Error@1 {:.2f}%".format(
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train_acc1es[last_epoch],
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train_acc5es[last_epoch],
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100 - train_acc1es[last_epoch],
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valid_acc1es[last_epoch],
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valid_acc5es[last_epoch],
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100 - valid_acc1es[last_epoch],
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)
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)
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# measure elapsed time
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seed_time.update(time.time() - start_time)
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start_time = time.time()
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need_time = "Time Left: {:}".format(
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convert_secs2time(seed_time.avg * (len(seeds) - _is - 1), True)
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)
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logger.log(
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"\n<<<***>>> The {:02d}/{:02d}-th seed is {:} <finish> other procedures need {:}".format(
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_is, len(seeds), seed, need_time
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)
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)
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logger.close()
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def generate_meta_info(save_dir, max_node, divide=40):
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aa_nas_bench_ss = get_search_spaces("cell", "nas-bench-201")
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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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# to test fixed-random shuffle
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# print ('arch [0] : {:}\n---->>>> {:}'.format( archs[0], archs[0].tostr() ))
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# print ('arch [9] : {:}\n---->>>> {:}'.format( archs[9], archs[9].tostr() ))
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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|"
|
|
), "please check the 9-th architecture : {:}".format(archs[9])
|
|
assert (
|
|
archs[123].tostr()
|
|
== "|avg_pool_3x3~0|+|avg_pool_3x3~0|nor_conv_1x1~1|+|none~0|avg_pool_3x3~1|nor_conv_3x3~2|"
|
|
), "please check the 123-th architecture : {:}".format(archs[123])
|
|
total_arch = len(archs)
|
|
|
|
num = 50000
|
|
indexes_5W = list(range(num))
|
|
random.seed(1021)
|
|
random.shuffle(indexes_5W)
|
|
train_split = sorted(list(set(indexes_5W[: num // 2])))
|
|
valid_split = sorted(list(set(indexes_5W[num // 2 :])))
|
|
assert len(train_split) + len(valid_split) == num
|
|
assert (
|
|
train_split[0] == 0
|
|
and train_split[10] == 26
|
|
and train_split[111] == 203
|
|
and valid_split[0] == 1
|
|
and valid_split[10] == 18
|
|
and valid_split[111] == 242
|
|
), "{:} {:} {:} - {:} {:} {:}".format(
|
|
train_split[0],
|
|
train_split[10],
|
|
train_split[111],
|
|
valid_split[0],
|
|
valid_split[10],
|
|
valid_split[111],
|
|
)
|
|
splits = {num: {"train": train_split, "valid": valid_split}}
|
|
|
|
info = {
|
|
"archs": [x.tostr() for x in archs],
|
|
"total": total_arch,
|
|
"max_node": max_node,
|
|
"splits": splits,
|
|
}
|
|
|
|
save_dir = Path(save_dir)
|
|
save_dir.mkdir(parents=True, exist_ok=True)
|
|
save_name = save_dir / "meta-node-{:}.pth".format(max_node)
|
|
assert not save_name.exists(), "{:} already exist".format(save_name)
|
|
torch.save(info, save_name)
|
|
print("save the meta file into {:}".format(save_name))
|
|
|
|
|
|
def traverse_net(max_node):
|
|
aa_nas_bench_ss = get_search_spaces("cell", "nats-bench")
|
|
archs = CellStructure.gen_all(aa_nas_bench_ss, max_node, False)
|
|
print(
|
|
"There are {:} archs vs {:}.".format(
|
|
len(archs), len(aa_nas_bench_ss) ** ((max_node - 1) * max_node / 2)
|
|
)
|
|
)
|
|
|
|
random.seed(88) # please do not change this line for reproducibility
|
|
random.shuffle(archs)
|
|
assert (
|
|
archs[0].tostr()
|
|
== "|avg_pool_3x3~0|+|nor_conv_1x1~0|skip_connect~1|+|nor_conv_1x1~0|skip_connect~1|skip_connect~2|"
|
|
), "please check the 0-th architecture : {:}".format(archs[0])
|
|
assert (
|
|
archs[9].tostr()
|
|
== "|avg_pool_3x3~0|+|none~0|none~1|+|skip_connect~0|none~1|nor_conv_3x3~2|"
|
|
), "please check the 9-th architecture : {:}".format(archs[9])
|
|
assert (
|
|
archs[123].tostr()
|
|
== "|avg_pool_3x3~0|+|avg_pool_3x3~0|nor_conv_1x1~1|+|none~0|avg_pool_3x3~1|nor_conv_3x3~2|"
|
|
), "please check the 123-th architecture : {:}".format(archs[123])
|
|
return [x.tostr() for x in archs]
|
|
|
|
|
|
def filter_indexes(xlist, mode, save_dir, seeds):
|
|
all_indexes = []
|
|
for index in xlist:
|
|
if mode == "cover":
|
|
all_indexes.append(index)
|
|
else:
|
|
for seed in seeds:
|
|
temp_path = save_dir / "arch-{:06d}-seed-{:04d}.pth".format(index, seed)
|
|
if not temp_path.exists():
|
|
all_indexes.append(index)
|
|
break
|
|
print(
|
|
"{:} [FILTER-INDEXES] : there are {:}/{:} architectures in total".format(
|
|
time_string(), len(all_indexes), len(xlist)
|
|
)
|
|
)
|
|
return all_indexes
|
|
|
|
|
|
if __name__ == "__main__":
|
|
# mode_choices = ['meta', 'new', 'cover'] + ['specific-{:}'.format(_) for _ in CellArchitectures.keys()]
|
|
parser = argparse.ArgumentParser(
|
|
description="NATS-Bench (topology search space)",
|
|
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
|
)
|
|
parser.add_argument("--mode", type=str, required=True, help="The script mode.")
|
|
parser.add_argument(
|
|
"--save_dir",
|
|
type=str,
|
|
default="output/NATS-Bench-topology",
|
|
help="Folder to save checkpoints and log.",
|
|
)
|
|
parser.add_argument(
|
|
"--max_node",
|
|
type=int,
|
|
default=4,
|
|
help="The maximum node in a cell (please do not change it).",
|
|
)
|
|
# use for train the model
|
|
parser.add_argument(
|
|
"--workers",
|
|
type=int,
|
|
default=8,
|
|
help="number of data loading workers (default: 2)",
|
|
)
|
|
parser.add_argument(
|
|
"--srange", type=str, required=True, help="The range of models to be evaluated"
|
|
)
|
|
parser.add_argument("--datasets", type=str, nargs="+", help="The applied datasets.")
|
|
parser.add_argument(
|
|
"--xpaths", type=str, nargs="+", help="The root path for this dataset."
|
|
)
|
|
parser.add_argument(
|
|
"--splits", type=int, nargs="+", help="The root path for this dataset."
|
|
)
|
|
parser.add_argument(
|
|
"--hyper",
|
|
type=str,
|
|
default="12",
|
|
choices=["01", "12", "200"],
|
|
help="The tag for hyper-parameters.",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--seeds", type=int, nargs="+", help="The range of models to be evaluated"
|
|
)
|
|
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.")
|
|
args = parser.parse_args()
|
|
|
|
assert args.mode in ["meta", "new", "cover"] or args.mode.startswith(
|
|
"specific-"
|
|
), "invalid mode : {:}".format(args.mode)
|
|
|
|
if args.mode == "meta":
|
|
generate_meta_info(args.save_dir, args.max_node)
|
|
elif args.mode.startswith("specific"):
|
|
assert len(args.mode.split("-")) == 2, "invalid mode : {:}".format(args.mode)
|
|
model_str = args.mode.split("-")[1]
|
|
train_single_model(
|
|
args.save_dir,
|
|
args.workers,
|
|
args.datasets,
|
|
args.xpaths,
|
|
args.splits,
|
|
args.use_less > 0,
|
|
tuple(args.seeds),
|
|
model_str,
|
|
{"channel": args.channel, "num_cells": args.num_cells},
|
|
)
|
|
else:
|
|
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)
|
|
)
|
|
opt_config = "./configs/nas-benchmark/hyper-opts/{:}E.config".format(args.hyper)
|
|
if not os.path.isfile(opt_config):
|
|
raise ValueError("{:} is not a file.".format(opt_config))
|
|
save_dir = Path(args.save_dir) / "raw-data-{:}".format(args.hyper)
|
|
save_dir.mkdir(parents=True, exist_ok=True)
|
|
to_evaluate_indexes = split_str2indexes(args.srange, args.check_N, 5)
|
|
if not len(args.seeds):
|
|
raise ValueError("invalid length of seeds args: {:}".format(args.seeds))
|
|
if not (len(args.datasets) == len(args.xpaths) == len(args.splits)):
|
|
raise ValueError(
|
|
"invalid infos : {:} vs {:} vs {:}".format(
|
|
len(args.datasets), len(args.xpaths), len(args.splits)
|
|
)
|
|
)
|
|
if args.workers <= 0:
|
|
raise ValueError("invalid number of workers : {:}".format(args.workers))
|
|
|
|
target_indexes = filter_indexes(
|
|
to_evaluate_indexes, args.mode, save_dir, args.seeds
|
|
)
|
|
|
|
assert torch.cuda.is_available(), "CUDA is not available."
|
|
torch.backends.cudnn.enabled = True
|
|
torch.backends.cudnn.deterministic = True
|
|
torch.set_num_threads(args.workers)
|
|
|
|
main(
|
|
save_dir,
|
|
args.workers,
|
|
args.datasets,
|
|
args.xpaths,
|
|
args.splits,
|
|
tuple(args.seeds),
|
|
nets,
|
|
opt_config,
|
|
target_indexes,
|
|
args.mode == "cover",
|
|
{
|
|
"name": "infer.tiny",
|
|
"channel": args.channel,
|
|
"num_cells": args.num_cells,
|
|
},
|
|
)
|