update NAS-Bench
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D-X-Y
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#####################################################
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# python exps/NAS-Bench-201/check.py --base_save_dir
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#####################################################
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import os, sys, time, argparse, collections
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from shutil import copyfile
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import sys, time, argparse, collections
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import torch
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import torch.nn as nn
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from pathlib import Path
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from collections import defaultdict
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lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
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39
exps/NAS-Bench-201/show-best.py
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39
exps/NAS-Bench-201/show-best.py
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.01 #
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################################################################################################
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# python exps/NAS-Bench-201/show-best.py --api_path $HOME/.torch/NAS-Bench-201-v1_0-e61699.pth #
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################################################################################################
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import os, sys, time, glob, random, argparse
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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: sys.path.insert(0, str(lib_dir))
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from nas_201_api import NASBench201API as API
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if __name__ == '__main__':
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parser = argparse.ArgumentParser("Analysis of NAS-Bench-201")
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parser.add_argument('--api_path', type=str, default=None, help='The path to the NAS-Bench-201 benchmark file.')
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args = parser.parse_args()
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meta_file = Path(args.api_path)
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assert meta_file.exists(), 'invalid path for api : {:}'.format(meta_file)
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api = API(str(meta_file))
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# This will show the results of the best architecture based on the validation set of each dataset.
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arch_index, accuracy = api.find_best('cifar10-valid', 'x-valid', None, None, False)
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print('FOR CIFAR-010, using the hyper-parameters with 200 training epochs :::')
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print('arch-index={:5d}, arch={:}'.format(arch_index, api.arch(arch_index)))
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api.show(arch_index)
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print('')
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arch_index, accuracy = api.find_best('cifar100', 'x-valid', None, None, False)
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print('FOR CIFAR-100, using the hyper-parameters with 200 training epochs :::')
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print('arch-index={:5d}, arch={:}'.format(arch_index, api.arch(arch_index)))
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api.show(arch_index)
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print('')
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arch_index, accuracy = api.find_best('ImageNet16-120', 'x-valid', None, None, False)
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print('FOR ImageNet16-120, using the hyper-parameters with 200 training epochs :::')
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print('arch-index={:5d}, arch={:}'.format(arch_index, api.arch(arch_index)))
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api.show(arch_index)
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print('')
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196
exps/NAS-Bench-201/xshapes.py
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exps/NAS-Bench-201/xshapes.py
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###############################################################
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# NAS-Bench-201, ICLR 2020 (https://arxiv.org/abs/2001.00326) #
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###############################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
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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: 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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def evaluate_all_datasets(channels: Text, datasets: List[Text], xpaths: List[Text],
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splits: List[Text], config_path: Text, seed: int, workers: int, logger):
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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 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 configurature
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if dataset == 'cifar10' or dataset == 'cifar100':
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split_info = load_config('configs/nas-benchmark/cifar-split.txt', None, None)
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elif dataset.startswith('ImageNet16'):
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split_info = load_config('configs/nas-benchmark/{:}-split.txt'.format(dataset), None, None)
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else:
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raise ValueError('invalid dataset : {:}'.format(dataset))
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config = load_config(config_path, dict(class_num=class_num, xshape=xshape), logger)
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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 = {'ori-test': torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, shuffle=False, num_workers=workers, pin_memory=True)}
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assert len(train_data) == len(split_info.train) + len(split_info.valid), 'invalid length : {:} vs {:} + {:}'.format(len(train_data), len(split_info.train), len(split_info.valid))
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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(train_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.train), num_workers=workers, pin_memory=True)
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valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.valid), num_workers=workers, pin_memory=True)
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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(train_data, batch_size=config.batch_size, shuffle=True , num_workers=workers, pin_memory=True)
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valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, shuffle=False, num_workers=workers, pin_memory=True)
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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('configs/nas-benchmark/cifar100-test-split.txt', None, None)
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ValLoaders = {'ori-test': valid_loader,
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'x-valid' : torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xvalid), num_workers=workers, pin_memory=True),
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'x-test' : torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xtest ), num_workers=workers, pin_memory=True)
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}
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elif dataset == 'ImageNet16-120':
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imagenet16_splits = load_config('configs/nas-benchmark/imagenet-16-120-test-split.txt', None, None)
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ValLoaders = {'ori-test': valid_loader,
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'x-valid' : torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet16_splits.xvalid), num_workers=workers, pin_memory=True),
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'x-test' : torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet16_splits.xtest ), num_workers=workers, pin_memory=True)
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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): dataset_key = dataset_key + '-valid'
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logger.log('Evaluate ||||||| {:10s} ||||||| Train-Num={:}, Valid-Num={:}, Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(dataset_key, len(train_data), len(valid_data), len(train_loader), len(valid_loader), config.batch_size))
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logger.log('Evaluate ||||||| {:10s} ||||||| Config={:}'.format(dataset_key, config))
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for key, value in ValLoaders.items():
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logger.log('Evaluate ---->>>> {:10s} with {:} batchs'.format(key, len(value)))
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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(dict(name='infer.shape.tiny', channels=channels, genotype=genotype, num_classes=class_num), None)
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results = bench_evaluate_for_seed(arch_config, config, train_loader, ValLoaders, seed, logger)
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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(save_dir: Path, workers: int, datasets: List[Text], xpaths: List[Text],
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splits: List[int], seeds: List[int], nets: List[str], opt_config: Dict[Text, Any],
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srange: tuple, cover_mode: bool):
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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.set_num_threads(workers)
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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), 0, 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} / {:06d} with cover-mode={:}'.format(srange[0], srange[1], len(nets),
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cover_mode))
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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(i, len(datasets), dataset, xpath, split))
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logger.log('--->>> optimization config : {:}'.format(opt_config))
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to_evaluate_indexes = list(range(srange[0], srange[1] + 1))
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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('\n{:} evaluate {:06d}/{:06d} ({:06d}/{:06d})-th arch [seeds={:}] {:}'.format(time_string(), i,
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len(to_evaluate_indexes), index, len(nets), seeds, '-' * 15))
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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('Find existing file : {:}, remove it before evaluation'.format(to_save_name))
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os.remove(str(to_save_name))
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else:
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logger.log('Find existing file : {:}, skip this evaluation'.format(to_save_name))
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has_continue = True
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continue
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results = evaluate_all_datasets(channelstr, datasets, xpaths, splits, opt_config, seed, workers, logger)
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torch.save(results, to_save_name)
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logger.log('\n{:} evaluate {:06d}/{:06d} ({:06d}/{:06d})-th arch [seeds={:}] ===>>> {:}'.format(time_string(), i,
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len(to_evaluate_indexes), index, len(nets), seeds, to_save_name))
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# measure elapsed time
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if not has_continue: epoch_time.update(time.time() - start_time)
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start_time = time.time()
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need_time = 'Time Left: {:}'.format(convert_secs2time(epoch_time.avg * (len(to_evaluate_indexes) - i - 1), True))
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logger.log('This arch costs : {:}'.format(convert_secs2time(epoch_time.val, True)))
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logger.log('{:}'.format('*' * 100))
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logger.log('{:} {:74s} {:}'.format('*' * 10, '{:06d}/{:06d} ({:06d}/{:06d})-th done, left {:}'.format(i, len(
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to_evaluate_indexes), index, len(nets), need_time), '*' * 10))
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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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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='NAS-Bench-X', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument('--mode', type=str, required=True, choices=['new', 'cover'], help='The script mode.')
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parser.add_argument('--save_dir', type=str, default='output/NAS-BENCH-202', help='Folder to save checkpoints and log.')
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parser.add_argument('--candidateC', type=int, nargs='+', default=[8, 16, 24, 32, 40, 48, 56, 64], help='.')
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parser.add_argument('--num_layers', type=int, default=5, help='The number of layers in a network.')
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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('--workers', type=int, default=8, help='The number of data loading workers (default: 2)')
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parser.add_argument('--srange' , type=str, required=True, help='The range of models to be evaluated')
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parser.add_argument('--datasets', type=str, nargs='+', help='The applied datasets.')
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parser.add_argument('--xpaths', type=str, nargs='+', help='The root path for this dataset.')
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parser.add_argument('--splits', type=int, nargs='+', help='The root path for this dataset.')
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parser.add_argument('--hyper', type=str, default='12', choices=['12', '90'], help='The tag for hyper-parameters.')
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parser.add_argument('--seeds' , type=int, nargs='+', help='The range of models to be evaluated')
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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: raise ValueError('Pre-num-check failed : {:} vs {:}'.format(len(nets), args.check_N))
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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): 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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if not isinstance(args.srange, str) or len(args.srange.split('-')) != 2:
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raise ValueError('Invalid scheme for {:}'.format(args.srange))
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srange = args.srange.split('-')
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srange = (int(srange[0]), int(srange[1]))
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assert 0 <= srange[0] <= srange[1] < args.check_N, '{:} vs {:} vs {:}'.format(srange[0], srange[1], args.check_N)
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assert len(args.seeds) > 0, 'invalid length of seeds args: {:}'.format(args.seeds)
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assert len(args.datasets) == len(args.xpaths) == len(args.splits), 'invalid infos : {:} vs {:} vs {:}'.format(len(args.datasets), len(args.xpaths), len(args.splits))
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assert args.workers > 0, 'invalid number of workers : {:}'.format(args.workers)
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main(save_dir, args.workers, args.datasets, args.xpaths, args.splits, tuple(args.seeds), nets, opt_config,
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srange, args.mode == 'cover')
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