490 lines
18 KiB
Python
490 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], 2020.07 #
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##############################################################################
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# This file is used to train (all) architecture candidate in the size search #
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# space in NATS-Bench (sss) 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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# (NOTE): the topology for all candidates in sss is fixed as: ######################
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# |nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|nor_conv_3x3~1|nor_conv_3x3~2| #
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###################################################################################################
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# Please use the script of scripts/NATS-Bench/train-shapes.sh to run. #
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##############################################################################
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import os, sys, time, torch, 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 utils import split_str2indexes
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def evaluate_all_datasets(
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channels: 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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workers: int,
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logger,
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):
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machine_info = get_machine_info()
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all_infos = {"info": machine_info}
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all_dataset_keys = []
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# look all the dataset
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for dataset, xpath, split in zip(datasets, xpaths, splits):
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# the train and 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 the splitted 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-index= 9930, arch=|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|nor_conv_3x3~1|nor_conv_3x3~2|
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# this genotype is the architecture with the highest accuracy on CIFAR-100 validation set
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genotype = "|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|nor_conv_3x3~1|nor_conv_3x3~2|"
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arch_config = dict2config(
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dict(
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name="infer.shape.tiny",
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channels=channels,
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genotype=genotype,
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num_classes=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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):
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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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channelstr = 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, channelstr, "-" * 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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channelstr, datasets, xpaths, splits, opt_config, seed, workers, 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 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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def filter_indexes(xlist, mode, save_dir, seeds):
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all_indexes = []
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for index in xlist:
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if mode == "cover":
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all_indexes.append(index)
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else:
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for seed in seeds:
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temp_path = save_dir / "arch-{:06d}-seed-{:04d}.pth".format(index, seed)
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if not temp_path.exists():
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all_indexes.append(index)
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break
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print(
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"{:} [FILTER-INDEXES] : there are {:}/{:} architectures in total".format(
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time_string(), len(all_indexes), len(xlist)
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)
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)
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SLURM_PROCID, SLURM_NTASKS = "SLURM_PROCID", "SLURM_NTASKS"
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if SLURM_PROCID in os.environ and SLURM_NTASKS in os.environ: # run on the slurm
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proc_id, ntasks = int(os.environ[SLURM_PROCID]), int(os.environ[SLURM_NTASKS])
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assert 0 <= proc_id < ntasks, "invalid proc_id {:} vs ntasks {:}".format(
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proc_id, ntasks
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)
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scales = [int(float(i) / ntasks * len(all_indexes)) for i in range(ntasks)] + [
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len(all_indexes)
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]
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per_job = []
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for i in range(ntasks):
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xs, xe = min(max(scales[i], 0), len(all_indexes) - 1), min(
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max(scales[i + 1] - 1, 0), len(all_indexes) - 1
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)
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per_job.append((xs, xe))
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for i, srange in enumerate(per_job):
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print(" -->> {:2d}/{:02d} : {:}".format(i, ntasks, srange))
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current_range = per_job[proc_id]
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all_indexes = [
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all_indexes[i] for i in range(current_range[0], current_range[1] + 1)
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]
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# set the device id
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device = proc_id % torch.cuda.device_count()
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torch.cuda.set_device(device)
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print(" set the device id = {:}".format(device))
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print(
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"{:} [FILTER-INDEXES] : after filtering there are {:} architectures in total".format(
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time_string(), len(all_indexes)
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)
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)
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return all_indexes
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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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"--mode",
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type=str,
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required=True,
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choices=["new", "cover"],
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help="The script mode.",
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)
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parser.add_argument(
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"--save_dir",
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type=str,
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default="output/NATS-Bench-size",
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help="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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# use for train the model
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parser.add_argument(
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"--workers",
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type=int,
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default=8,
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help="The number of data loading workers (default: 2)",
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)
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parser.add_argument(
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"--srange", type=str, required=True, help="The range of models to be evaluated"
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)
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parser.add_argument("--datasets", type=str, nargs="+", help="The applied datasets.")
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parser.add_argument(
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"--xpaths", type=str, nargs="+", help="The root path for this dataset."
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)
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parser.add_argument(
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"--splits", type=int, nargs="+", help="The root path for this dataset."
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)
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parser.add_argument(
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"--hyper",
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type=str,
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default="12",
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choices=["01", "12", "90"],
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help="The tag for hyper-parameters.",
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)
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parser.add_argument(
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"--seeds", type=int, nargs="+", help="The range of models to be evaluated"
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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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opt_config = "./configs/nas-benchmark/hyper-opts/{:}E.config".format(args.hyper)
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if not os.path.isfile(opt_config):
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raise ValueError("{:} is not a file.".format(opt_config))
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save_dir = Path(args.save_dir) / "raw-data-{:}".format(args.hyper)
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save_dir.mkdir(parents=True, exist_ok=True)
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to_evaluate_indexes = split_str2indexes(args.srange, args.check_N, 5)
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if not len(args.seeds):
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raise ValueError("invalid length of seeds args: {:}".format(args.seeds))
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if not (len(args.datasets) == len(args.xpaths) == len(args.splits)):
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raise ValueError(
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"invalid infos : {:} vs {:} vs {:}".format(
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len(args.datasets), len(args.xpaths), len(args.splits)
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)
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)
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if args.workers <= 0:
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raise ValueError("invalid number of workers : {:}".format(args.workers))
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target_indexes = filter_indexes(
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to_evaluate_indexes, args.mode, save_dir, args.seeds
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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
|
|
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",
|
|
)
|