Reformulate via black
This commit is contained in:
D-X-Y
@@ -15,230 +15,349 @@
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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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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 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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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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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(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 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('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 the splitted 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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to_evaluate_indexes: tuple, cover_mode: bool):
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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(min(to_evaluate_indexes), max(to_evaluate_indexes))
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+'({:} in total) / {:06d} with cover-mode={:}'.format(len(to_evaluate_indexes), len(nets), 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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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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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("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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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,
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datasets, xpaths, splits, opt_config, seed,
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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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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 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, batch_size=config.batch_size, shuffle=False, num_workers=workers, 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(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(
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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, batch_size=config.batch_size, shuffle=True, num_workers=workers, pin_memory=True
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)
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valid_loader = torch.utils.data.DataLoader(
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valid_data, batch_size=config.batch_size, shuffle=False, num_workers=workers, 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("configs/nas-benchmark/cifar100-test-split.txt", None, None)
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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(cifar100_splits.xvalid),
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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(cifar100_splits.xtest),
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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("configs/nas-benchmark/imagenet-16-120-test-split.txt", None, None)
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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(imagenet16_splits.xvalid),
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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(imagenet16_splits.xtest),
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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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logger.close()
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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, len(train_data), len(valid_data), len(train_loader), len(valid_loader), config.batch_size
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)
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)
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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(
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dict(name="infer.shape.tiny", channels=channels, genotype=genotype, num_classes=class_num), None
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)
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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(
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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(min(to_evaluate_indexes), max(to_evaluate_indexes))
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+ "({:} in total) / {:06d} with cover-mode={:}".format(len(to_evaluate_indexes), len(nets), cover_mode)
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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(), i, len(to_evaluate_indexes), index, len(nets), seeds, "-" * 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("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(
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"\n{:} evaluate {:06d}/{:06d} ({:06d}/{:06d})-th arch [seeds={:}] ===>>> {:}".format(
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time_string(), i, len(to_evaluate_indexes), index, len(nets), seeds, 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(
|
||||
convert_secs2time(epoch_time.avg * (len(to_evaluate_indexes) - i - 1), True)
|
||||
)
|
||||
logger.log("This arch costs : {:}".format(convert_secs2time(epoch_time.val, True)))
|
||||
logger.log("{:}".format("*" * 100))
|
||||
logger.log(
|
||||
"{:} {:74s} {:}".format(
|
||||
"*" * 10,
|
||||
"{:06d}/{:06d} ({:06d}/{:06d})-th done, left {:}".format(
|
||||
i, len(to_evaluate_indexes), index, len(nets), need_time
|
||||
),
|
||||
"*" * 10,
|
||||
)
|
||||
)
|
||||
logger.log("{:}".format("*" * 100))
|
||||
|
||||
logger.close()
|
||||
|
||||
|
||||
def traverse_net(candidates: List[int], N: int):
|
||||
nets = ['']
|
||||
for i in range(N):
|
||||
new_nets = []
|
||||
for net in nets:
|
||||
for C in candidates:
|
||||
new_nets.append(str(C) if net == '' else "{:}:{:}".format(net,C))
|
||||
nets = new_nets
|
||||
return nets
|
||||
nets = [""]
|
||||
for i in range(N):
|
||||
new_nets = []
|
||||
for net in nets:
|
||||
for C in candidates:
|
||||
new_nets.append(str(C) if net == "" else "{:}:{:}".format(net, C))
|
||||
nets = new_nets
|
||||
return nets
|
||||
|
||||
|
||||
def filter_indexes(xlist, mode, save_dir, seeds):
|
||||
all_indexes = []
|
||||
for index in xlist:
|
||||
if mode == 'cover':
|
||||
all_indexes.append(index)
|
||||
else:
|
||||
for seed in seeds:
|
||||
temp_path = save_dir / 'arch-{:06d}-seed-{:04d}.pth'.format(index, seed)
|
||||
if not temp_path.exists():
|
||||
all_indexes.append(index)
|
||||
break
|
||||
print('{:} [FILTER-INDEXES] : there are {:}/{:} architectures in total'.format(time_string(), len(all_indexes), len(xlist)))
|
||||
all_indexes = []
|
||||
for index in xlist:
|
||||
if mode == "cover":
|
||||
all_indexes.append(index)
|
||||
else:
|
||||
for seed in seeds:
|
||||
temp_path = save_dir / "arch-{:06d}-seed-{:04d}.pth".format(index, seed)
|
||||
if not temp_path.exists():
|
||||
all_indexes.append(index)
|
||||
break
|
||||
print(
|
||||
"{:} [FILTER-INDEXES] : there are {:}/{:} architectures in total".format(
|
||||
time_string(), len(all_indexes), len(xlist)
|
||||
)
|
||||
)
|
||||
|
||||
SLURM_PROCID, SLURM_NTASKS = 'SLURM_PROCID', 'SLURM_NTASKS'
|
||||
if SLURM_PROCID in os.environ and SLURM_NTASKS in os.environ: # run on the slurm
|
||||
proc_id, ntasks = int(os.environ[SLURM_PROCID]), int(os.environ[SLURM_NTASKS])
|
||||
assert 0 <= proc_id < ntasks, 'invalid proc_id {:} vs ntasks {:}'.format(proc_id, ntasks)
|
||||
scales = [int(float(i)/ntasks*len(all_indexes)) for i in range(ntasks)] + [len(all_indexes)]
|
||||
per_job = []
|
||||
for i in range(ntasks):
|
||||
xs, xe = min(max(scales[i],0), len(all_indexes)-1), min(max(scales[i+1]-1,0), len(all_indexes)-1)
|
||||
per_job.append((xs, xe))
|
||||
for i, srange in enumerate(per_job):
|
||||
print(' -->> {:2d}/{:02d} : {:}'.format(i, ntasks, srange))
|
||||
current_range = per_job[proc_id]
|
||||
all_indexes = [all_indexes[i] for i in range(current_range[0], current_range[1]+1)]
|
||||
# set the device id
|
||||
device = proc_id % torch.cuda.device_count()
|
||||
torch.cuda.set_device(device)
|
||||
print(' set the device id = {:}'.format(device))
|
||||
print('{:} [FILTER-INDEXES] : after filtering there are {:} architectures in total'.format(time_string(), len(all_indexes)))
|
||||
return all_indexes
|
||||
SLURM_PROCID, SLURM_NTASKS = "SLURM_PROCID", "SLURM_NTASKS"
|
||||
if SLURM_PROCID in os.environ and SLURM_NTASKS in os.environ: # run on the slurm
|
||||
proc_id, ntasks = int(os.environ[SLURM_PROCID]), int(os.environ[SLURM_NTASKS])
|
||||
assert 0 <= proc_id < ntasks, "invalid proc_id {:} vs ntasks {:}".format(proc_id, ntasks)
|
||||
scales = [int(float(i) / ntasks * len(all_indexes)) for i in range(ntasks)] + [len(all_indexes)]
|
||||
per_job = []
|
||||
for i in range(ntasks):
|
||||
xs, xe = min(max(scales[i], 0), len(all_indexes) - 1), min(max(scales[i + 1] - 1, 0), len(all_indexes) - 1)
|
||||
per_job.append((xs, xe))
|
||||
for i, srange in enumerate(per_job):
|
||||
print(" -->> {:2d}/{:02d} : {:}".format(i, ntasks, srange))
|
||||
current_range = per_job[proc_id]
|
||||
all_indexes = [all_indexes[i] for i in range(current_range[0], current_range[1] + 1)]
|
||||
# set the device id
|
||||
device = proc_id % torch.cuda.device_count()
|
||||
torch.cuda.set_device(device)
|
||||
print(" set the device id = {:}".format(device))
|
||||
print(
|
||||
"{:} [FILTER-INDEXES] : after filtering there are {:} architectures in total".format(
|
||||
time_string(), len(all_indexes)
|
||||
)
|
||||
)
|
||||
return all_indexes
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='NATS-Bench (size search space)', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||
parser.add_argument('--mode', type=str, required=True, choices=['new', 'cover'], help='The script mode.')
|
||||
parser.add_argument('--save_dir', type=str, default='output/NATS-Bench-size', help='Folder to save checkpoints and log.')
|
||||
parser.add_argument('--candidateC', type=int, nargs='+', default=[8, 16, 24, 32, 40, 48, 56, 64], help='.')
|
||||
parser.add_argument('--num_layers', type=int, default=5, help='The number of layers in a network.')
|
||||
parser.add_argument('--check_N', type=int, default=32768, help='For safety.')
|
||||
# use for train the model
|
||||
parser.add_argument('--workers', type=int, default=8, help='The number of data loading workers (default: 2)')
|
||||
parser.add_argument('--srange' , type=str, required=True, help='The range of models to be evaluated')
|
||||
parser.add_argument('--datasets', type=str, nargs='+', help='The applied datasets.')
|
||||
parser.add_argument('--xpaths', type=str, nargs='+', help='The root path for this dataset.')
|
||||
parser.add_argument('--splits', type=int, nargs='+', help='The root path for this dataset.')
|
||||
parser.add_argument('--hyper', type=str, default='12', choices=['01', '12', '90'], help='The tag for hyper-parameters.')
|
||||
parser.add_argument('--seeds' , type=int, nargs='+', help='The range of models to be evaluated')
|
||||
args = parser.parse_args()
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(
|
||||
description="NATS-Bench (size search space)", formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
parser.add_argument("--mode", type=str, required=True, choices=["new", "cover"], help="The script mode.")
|
||||
parser.add_argument(
|
||||
"--save_dir", type=str, default="output/NATS-Bench-size", help="Folder to save checkpoints and log."
|
||||
)
|
||||
parser.add_argument("--candidateC", type=int, nargs="+", default=[8, 16, 24, 32, 40, 48, 56, 64], help=".")
|
||||
parser.add_argument("--num_layers", type=int, default=5, help="The number of layers in a network.")
|
||||
parser.add_argument("--check_N", type=int, default=32768, help="For safety.")
|
||||
# use for train the model
|
||||
parser.add_argument("--workers", type=int, default=8, help="The number of data loading workers (default: 2)")
|
||||
parser.add_argument("--srange", type=str, required=True, help="The range of models to be evaluated")
|
||||
parser.add_argument("--datasets", type=str, nargs="+", help="The applied datasets.")
|
||||
parser.add_argument("--xpaths", type=str, nargs="+", help="The root path for this dataset.")
|
||||
parser.add_argument("--splits", type=int, nargs="+", help="The root path for this dataset.")
|
||||
parser.add_argument(
|
||||
"--hyper", type=str, default="12", choices=["01", "12", "90"], help="The tag for hyper-parameters."
|
||||
)
|
||||
parser.add_argument("--seeds", type=int, nargs="+", help="The range of models to be evaluated")
|
||||
args = parser.parse_args()
|
||||
|
||||
nets = traverse_net(args.candidateC, args.num_layers)
|
||||
if len(nets) != args.check_N:
|
||||
raise ValueError('Pre-num-check failed : {:} vs {:}'.format(len(nets), args.check_N))
|
||||
nets = traverse_net(args.candidateC, args.num_layers)
|
||||
if len(nets) != args.check_N:
|
||||
raise ValueError("Pre-num-check failed : {:} vs {:}".format(len(nets), args.check_N))
|
||||
|
||||
opt_config = './configs/nas-benchmark/hyper-opts/{:}E.config'.format(args.hyper)
|
||||
if not os.path.isfile(opt_config):
|
||||
raise ValueError('{:} is not a file.'.format(opt_config))
|
||||
save_dir = Path(args.save_dir) / 'raw-data-{:}'.format(args.hyper)
|
||||
save_dir.mkdir(parents=True, exist_ok=True)
|
||||
to_evaluate_indexes = split_str2indexes(args.srange, args.check_N, 5)
|
||||
opt_config = "./configs/nas-benchmark/hyper-opts/{:}E.config".format(args.hyper)
|
||||
if not os.path.isfile(opt_config):
|
||||
raise ValueError("{:} is not a file.".format(opt_config))
|
||||
save_dir = Path(args.save_dir) / "raw-data-{:}".format(args.hyper)
|
||||
save_dir.mkdir(parents=True, exist_ok=True)
|
||||
to_evaluate_indexes = split_str2indexes(args.srange, args.check_N, 5)
|
||||
|
||||
if not len(args.seeds):
|
||||
raise ValueError('invalid length of seeds args: {:}'.format(args.seeds))
|
||||
if not (len(args.datasets) == len(args.xpaths) == len(args.splits)):
|
||||
raise ValueError('invalid infos : {:} vs {:} vs {:}'.format(len(args.datasets), len(args.xpaths), len(args.splits)))
|
||||
if args.workers <= 0:
|
||||
raise ValueError('invalid number of workers : {:}'.format(args.workers))
|
||||
if not len(args.seeds):
|
||||
raise ValueError("invalid length of seeds args: {:}".format(args.seeds))
|
||||
if not (len(args.datasets) == len(args.xpaths) == len(args.splits)):
|
||||
raise ValueError(
|
||||
"invalid infos : {:} vs {:} vs {:}".format(len(args.datasets), len(args.xpaths), len(args.splits))
|
||||
)
|
||||
if args.workers <= 0:
|
||||
raise ValueError("invalid number of workers : {:}".format(args.workers))
|
||||
|
||||
target_indexes = filter_indexes(to_evaluate_indexes, args.mode, save_dir, args.seeds)
|
||||
|
||||
assert torch.cuda.is_available(), 'CUDA is not available.'
|
||||
torch.backends.cudnn.enabled = True
|
||||
torch.backends.cudnn.deterministic = True
|
||||
torch.set_num_threads(args.workers)
|
||||
target_indexes = filter_indexes(to_evaluate_indexes, args.mode, save_dir, args.seeds)
|
||||
|
||||
main(save_dir, args.workers, args.datasets, args.xpaths, args.splits, tuple(args.seeds), nets, opt_config, target_indexes, args.mode == 'cover')
|
||||
assert torch.cuda.is_available(), "CUDA is not available."
|
||||
torch.backends.cudnn.enabled = True
|
||||
torch.backends.cudnn.deterministic = True
|
||||
torch.set_num_threads(args.workers)
|
||||
|
||||
main(
|
||||
save_dir,
|
||||
args.workers,
|
||||
args.datasets,
|
||||
args.xpaths,
|
||||
args.splits,
|
||||
tuple(args.seeds),
|
||||
nets,
|
||||
opt_config,
|
||||
target_indexes,
|
||||
args.mode == "cover",
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user