317 lines
20 KiB
Python
317 lines
20 KiB
Python
###############################################################
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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, random, argparse
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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 load_config
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from procedures import save_checkpoint, copy_checkpoint
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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 functions import evaluate_for_seed
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def evaluate_all_datasets(arch, datasets, xpaths, splits, use_less, seed, arch_config, workers, logger):
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machine_info, arch_config = get_machine_info(), deepcopy(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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if use_less: config_path = 'configs/nas-benchmark/LESS.config'
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else : config_path = 'configs/nas-benchmark/CIFAR.config'
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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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if use_less: config_path = 'configs/nas-benchmark/LESS.config'
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else : config_path = 'configs/nas-benchmark/ImageNet-16.config'
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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, \
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{'class_num': class_num,
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'xshape' : xshape}, \
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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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results = evaluate_for_seed(arch_config, config, arch, 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, workers, datasets, xpaths, splits, use_less, srange, arch_index, seeds, cover_mode, meta_info, arch_config):
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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.benchmark = True
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torch.backends.cudnn.deterministic = True
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torch.set_num_threads( workers )
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assert len(srange) == 2 and 0 <= srange[0] <= srange[1], 'invalid srange : {:}'.format(srange)
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if use_less:
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sub_dir = Path(save_dir) / '{:06d}-{:06d}-C{:}-N{:}-LESS'.format(srange[0], srange[1], arch_config['channel'], arch_config['num_cells'])
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else:
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sub_dir = Path(save_dir) / '{:06d}-{:06d}-C{:}-N{:}'.format(srange[0], srange[1], arch_config['channel'], arch_config['num_cells'])
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logger = Logger(str(sub_dir), 0, False)
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all_archs = meta_info['archs']
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assert srange[1] < meta_info['total'], 'invalid range : {:}-{:} vs. {:}'.format(srange[0], srange[1], meta_info['total'])
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assert arch_index == -1 or srange[0] <= arch_index <= srange[1], 'invalid range : {:} vs. {:} vs. {:}'.format(srange[0], arch_index, srange[1])
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if arch_index == -1:
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to_evaluate_indexes = list(range(srange[0], srange[1]+1))
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else:
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to_evaluate_indexes = [arch_index]
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logger.log('xargs : seeds = {:}'.format(seeds))
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logger.log('xargs : arch_index = {:}'.format(arch_index))
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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('Start evaluating range =: {:06d} vs. {:06d} vs. {:06d} / {:06d} with cover-mode={:}'.format(srange[0], arch_index, srange[1], meta_info['total'], cover_mode))
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for i, (dataset, xpath, split) in enumerate(zip(datasets, xpaths, splits)):
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logger.log('--->>> Evaluate {:}/{:} : dataset={:9s}, path={:}, split={:}'.format(i, len(datasets), dataset, xpath, split))
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logger.log('--->>> architecture config : {:}'.format(arch_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 = all_archs[index]
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logger.log('\n{:} evaluate {:06d}/{:06d} ({:06d}/{:06d})-th architecture [seeds={:}] {:}'.format('-'*15, i, len(to_evaluate_indexes), index, meta_info['total'], seeds, '-'*15))
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#logger.log('{:} {:} {:}'.format('-'*15, arch.tostr(), '-'*15))
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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 = sub_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(CellStructure.str2structure(arch), \
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datasets, xpaths, splits, use_less, seed, \
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arch_config, workers, logger)
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torch.save(results, to_save_name)
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logger.log('{:} --evaluate-- {:06d}/{:06d} ({:06d}/{:06d})-th seed={:} done, save into {:}'.format('-'*15, i, len(to_evaluate_indexes), index, meta_info['total'], seed, 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(to_evaluate_indexes), index, meta_info['total'], need_time), '*'*10))
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logger.log('{:}'.format('*'*100))
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logger.close()
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def train_single_model(save_dir, workers, datasets, xpaths, splits, use_less, seeds, model_str, arch_config):
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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 = Path(save_dir) / 'specifics' / '{:}-{:}-{:}-{:}'.format('LESS' if use_less else 'FULL', model_str, arch_config['channel'], arch_config['num_cells'])
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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('The model string is found in pre-defined architecture dict : {:}'.format(model_str))
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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('Invalid model string : {:}. It can not be found or parsed.'.format(model_str))
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assert arch.check_valid_op(get_search_spaces('cell', 'full')), '{:} 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('\nThe {:02d}/{:02d}-th seed is {:} ----------------------<.>----------------------'.format(_is, len(seeds), seed))
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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('Find the existing file {:}, directly load!'.format(to_save_name))
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checkpoint = torch.load(to_save_name)
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else:
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logger.log('Does not find the existing file {:}, train and evaluate!'.format(to_save_name))
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checkpoint = evaluate_all_datasets(arch, datasets, xpaths, splits, use_less, seed, arch_config, workers, logger)
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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('\n{:} dataset : {:} {:}'.format('-'*15, dataset_key, '-'*15))
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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('Flops = {:} MB, Params = {:} MB'.format(dataset_info['flop'], dataset_info['param']))
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logger.log('config : {:}'.format(dataset_info['config']))
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logger.log('Training State (finish) = {:}'.format(dataset_info['finish-train']))
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last_epoch = dataset_info['total_epoch'] - 1
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train_acc1es, train_acc5es = dataset_info['train_acc1es'], dataset_info['train_acc5es']
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valid_acc1es, valid_acc5es = dataset_info['valid_acc1es'], dataset_info['valid_acc5es']
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logger.log('Last Info : Train = Acc@1 {:.2f}% Acc@5 {:.2f}% Error@1 {:.2f}%, Test = Acc@1 {:.2f}% Acc@5 {:.2f}% Error@1 {:.2f}%'.format(train_acc1es[last_epoch], train_acc5es[last_epoch], 100-train_acc1es[last_epoch], valid_acc1es[last_epoch], valid_acc5es[last_epoch], 100-valid_acc1es[last_epoch]))
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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( convert_secs2time(seed_time.avg * (len(seeds)-_is-1), True) )
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logger.log('\n<<<***>>> The {:02d}/{:02d}-th seed is {:} <finish> other procedures need {:}'.format(_is, len(seeds), seed, need_time))
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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 ('There are {:} archs vs {:}.'.format(len(archs), len(aa_nas_bench_ss) ** ((max_node-1)*max_node/2)))
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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 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])
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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])
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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])
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total_arch = len(archs)
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num = 50000
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indexes_5W = list(range(num))
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random.seed( 1021 )
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random.shuffle( indexes_5W )
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train_split = sorted( list(set(indexes_5W[:num//2])) )
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valid_split = sorted( list(set(indexes_5W[num//2:])) )
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assert len(train_split) + len(valid_split) == num
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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])
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splits = {num: {'train': train_split, 'valid': valid_split} }
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info = {'archs' : [x.tostr() for x in archs],
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'total' : total_arch,
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'max_node' : max_node,
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'splits': splits}
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save_dir = Path(save_dir)
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save_dir.mkdir(parents=True, exist_ok=True)
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save_name = save_dir / 'meta-node-{:}.pth'.format(max_node)
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assert not save_name.exists(), '{:} already exist'.format(save_name)
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torch.save(info, save_name)
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print ('save the meta file into {:}'.format(save_name))
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script_name_full = save_dir / 'BENCH-201-N{:}.opt-full.script'.format(max_node)
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script_name_less = save_dir / 'BENCH-201-N{:}.opt-less.script'.format(max_node)
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full_file = open(str(script_name_full), 'w')
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less_file = open(str(script_name_less), 'w')
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gaps = total_arch // divide
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for start in range(0, total_arch, gaps):
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xend = min(start+gaps, total_arch)
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full_file.write('bash ./scripts-search/NAS-Bench-201/train-models.sh 0 {:5d} {:5d} -1 \'777 888 999\'\n'.format(start, xend-1))
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less_file.write('bash ./scripts-search/NAS-Bench-201/train-models.sh 1 {:5d} {:5d} -1 \'777 888 999\'\n'.format(start, xend-1))
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print ('save the training script into {:} and {:}'.format(script_name_full, script_name_less))
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full_file.close()
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less_file.close()
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script_name = save_dir / 'meta-node-{:}.cal-script.txt'.format(max_node)
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macro = 'OMP_NUM_THREADS=6 CUDA_VISIBLE_DEVICES=0'
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with open(str(script_name), 'w') as cfile:
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for start in range(0, total_arch, gaps):
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xend = min(start+gaps, total_arch)
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cfile.write('{:} python exps/NAS-Bench-201/statistics.py --mode cal --target_dir {:06d}-{:06d}-C16-N5\n'.format(macro, start, xend-1))
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print ('save the post-processing script into {:}'.format(script_name))
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if __name__ == '__main__':
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#mode_choices = ['meta', 'new', 'cover'] + ['specific-{:}'.format(_) for _ in CellArchitectures.keys()]
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#parser = argparse.ArgumentParser(description='Algorithm-Agnostic NAS Benchmark', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser = argparse.ArgumentParser(description='NAS-Bench-201', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument('--mode' , type=str, required=True, help='The script mode.')
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parser.add_argument('--save_dir', type=str, help='Folder to save checkpoints and log.')
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parser.add_argument('--max_node', type=int, help='The maximum node in a cell.')
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# use for train the model
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parser.add_argument('--workers', type=int, default=8, help='number of data loading workers (default: 2)')
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parser.add_argument('--srange' , type=int, nargs='+', help='The range of models to be evaluated')
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parser.add_argument('--arch_index', type=int, default=-1, help='The architecture index to be evaluated (cover mode).')
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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('--use_less', type=int, default=0, choices=[0,1], help='Using the less-training-epoch config.')
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parser.add_argument('--seeds' , type=int, nargs='+', help='The range of models to be evaluated')
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parser.add_argument('--channel', type=int, help='The number of channels.')
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parser.add_argument('--num_cells', type=int, help='The number of cells in one stage.')
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args = parser.parse_args()
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assert args.mode in ['meta', 'new', 'cover'] or args.mode.startswith('specific-'), 'invalid mode : {:}'.format(args.mode)
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if args.mode == 'meta':
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generate_meta_info(args.save_dir, args.max_node)
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|
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:
|
|
meta_path = Path(args.save_dir) / 'meta-node-{:}.pth'.format(args.max_node)
|
|
assert meta_path.exists(), '{:} does not exist.'.format(meta_path)
|
|
meta_info = torch.load( meta_path )
|
|
# check whether args is ok
|
|
assert len(args.srange) == 2 and args.srange[0] <= args.srange[1], 'invalid length of srange args: {:}'.format(args.srange)
|
|
assert len(args.seeds) > 0, 'invalid length of seeds args: {:}'.format(args.seeds)
|
|
assert len(args.datasets) == len(args.xpaths) == len(args.splits), 'invalid infos : {:} vs {:} vs {:}'.format(len(args.datasets), len(args.xpaths), len(args.splits))
|
|
assert args.workers > 0, 'invalid number of workers : {:}'.format(args.workers)
|
|
|
|
main(args.save_dir, args.workers, args.datasets, args.xpaths, args.splits, args.use_less>0, \
|
|
tuple(args.srange), args.arch_index, tuple(args.seeds), \
|
|
args.mode == 'cover', meta_info, \
|
|
{'channel': args.channel, 'num_cells': args.num_cells})
|