Reformulate via black
This commit is contained in:
D-X-Y
@@ -19,316 +19,505 @@
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##############################################################################
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import os, sys, time, torch, random, argparse
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from typing import List, Text, Dict, Any
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from PIL import ImageFile
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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 models import CellStructure, CellArchitectures, get_search_spaces
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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 models import CellStructure, CellArchitectures, get_search_spaces
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from utils import split_str2indexes
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def evaluate_all_datasets(arch: Text, datasets: List[Text], xpaths: List[Text],
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splits: List[Text], config_path: Text, seed: int, raw_arch_config, workers, logger):
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machine_info, raw_arch_config = get_machine_info(), deepcopy(raw_arch_config)
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all_infos = {'info': machine_info}
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all_dataset_keys = []
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# look all the datasets
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for dataset, xpath, split in zip(datasets, xpaths, splits):
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# train valid data
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train_data, valid_data, xshape, class_num = get_datasets(dataset, xpath, -1)
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# load the configuration
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if dataset == 'cifar10' or dataset == 'cifar100':
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split_info = load_config('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_config = dict2config(dict(name='infer.tiny', C=raw_arch_config['channel'], N=raw_arch_config['num_cells'],
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genotype=arch, num_classes=config.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, arch_config: Dict[Text, Any]):
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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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arch = 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, arch, '-' * 15))
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# test this arch on different datasets with different seeds
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has_continue = False
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for seed in seeds:
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to_save_name = save_dir / 'arch-{:06d}-seed-{:04d}.pth'.format(index, seed)
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if to_save_name.exists():
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if cover_mode:
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logger.log('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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arch: Text,
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datasets: List[Text],
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xpaths: List[Text],
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splits: List[Text],
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config_path: Text,
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seed: int,
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raw_arch_config,
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workers,
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logger,
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):
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machine_info, raw_arch_config = get_machine_info(), deepcopy(raw_arch_config)
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all_infos = {"info": machine_info}
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all_dataset_keys = []
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# look all the datasets
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for dataset, xpath, split in zip(datasets, xpaths, splits):
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# train valid data
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train_data, valid_data, xshape, class_num = get_datasets(dataset, xpath, -1)
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# load the configuration
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if dataset == "cifar10" or dataset == "cifar100":
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split_info = load_config("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(CellStructure.str2structure(arch),
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datasets, xpaths, splits, opt_config, 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('\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 splited validation set
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if bool(split):
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assert dataset == "cifar10"
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ValLoaders = {
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"ori-test": torch.utils.data.DataLoader(
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valid_data, 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_config = dict2config(
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dict(
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name="infer.tiny",
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C=raw_arch_config["channel"],
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N=raw_arch_config["num_cells"],
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genotype=arch,
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num_classes=config.class_num,
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),
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None,
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)
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results = bench_evaluate_for_seed(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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arch_config: Dict[Text, Any],
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):
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log_dir = save_dir / "logs"
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log_dir.mkdir(parents=True, exist_ok=True)
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logger = Logger(str(log_dir), os.getpid(), False)
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logger.log("xargs : seeds = {:}".format(seeds))
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logger.log("xargs : cover_mode = {:}".format(cover_mode))
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logger.log("-" * 100)
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logger.log(
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"Start evaluating range =: {:06d} - {:06d}".format(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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arch = nets[index]
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logger.log(
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"\n{:} evaluate {:06d}/{:06d} ({:06d}/{:06d})-th arch [seeds={:}] {:}".format(
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time_string(), 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, arch, "-" * 15))
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# test this arch on different datasets with different seeds
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has_continue = False
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for seed in seeds:
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to_save_name = save_dir / "arch-{:06d}-seed-{:04d}.pth".format(index, seed)
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if to_save_name.exists():
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if cover_mode:
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logger.log("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(
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CellStructure.str2structure(arch),
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datasets,
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xpaths,
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splits,
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opt_config,
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seed,
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arch_config,
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workers,
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logger,
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)
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torch.save(results, to_save_name)
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logger.log(
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"\n{:} evaluate {:06d}/{:06d} ({:06d}/{:06d})-th arch [seeds={:}] ===>>> {:}".format(
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time_string(), 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 train_single_model(save_dir, workers, datasets, xpaths, splits, use_less, seeds, model_str, arch_config):
|
||||
assert torch.cuda.is_available(), 'CUDA is not available.'
|
||||
torch.backends.cudnn.enabled = True
|
||||
torch.backends.cudnn.deterministic = True
|
||||
#torch.backends.cudnn.benchmark = True
|
||||
torch.set_num_threads( workers )
|
||||
|
||||
save_dir = Path(save_dir) / 'specifics' / '{:}-{:}-{:}-{:}'.format('LESS' if use_less else 'FULL', model_str, arch_config['channel'], arch_config['num_cells'])
|
||||
logger = Logger(str(save_dir), 0, False)
|
||||
if model_str in CellArchitectures:
|
||||
arch = CellArchitectures[model_str]
|
||||
logger.log('The model string is found in pre-defined architecture dict : {:}'.format(model_str))
|
||||
else:
|
||||
try:
|
||||
arch = CellStructure.str2structure(model_str)
|
||||
except:
|
||||
raise ValueError('Invalid model string : {:}. It can not be found or parsed.'.format(model_str))
|
||||
assert arch.check_valid_op(get_search_spaces('cell', 'full')), '{:} has the invalid op.'.format(arch)
|
||||
logger.log('Start train-evaluate {:}'.format(arch.tostr()))
|
||||
logger.log('arch_config : {:}'.format(arch_config))
|
||||
assert torch.cuda.is_available(), "CUDA is not available."
|
||||
torch.backends.cudnn.enabled = True
|
||||
torch.backends.cudnn.deterministic = True
|
||||
# torch.backends.cudnn.benchmark = True
|
||||
torch.set_num_threads(workers)
|
||||
|
||||
start_time, seed_time = time.time(), AverageMeter()
|
||||
for _is, seed in enumerate(seeds):
|
||||
logger.log('\nThe {:02d}/{:02d}-th seed is {:} ----------------------<.>----------------------'.format(_is, len(seeds), seed))
|
||||
to_save_name = save_dir / 'seed-{:04d}.pth'.format(seed)
|
||||
if to_save_name.exists():
|
||||
logger.log('Find the existing file {:}, directly load!'.format(to_save_name))
|
||||
checkpoint = torch.load(to_save_name)
|
||||
save_dir = (
|
||||
Path(save_dir)
|
||||
/ "specifics"
|
||||
/ "{:}-{:}-{:}-{:}".format(
|
||||
"LESS" if use_less else "FULL", model_str, arch_config["channel"], arch_config["num_cells"]
|
||||
)
|
||||
)
|
||||
logger = Logger(str(save_dir), 0, False)
|
||||
if model_str in CellArchitectures:
|
||||
arch = CellArchitectures[model_str]
|
||||
logger.log("The model string is found in pre-defined architecture dict : {:}".format(model_str))
|
||||
else:
|
||||
logger.log('Does not find the existing file {:}, train and evaluate!'.format(to_save_name))
|
||||
checkpoint = evaluate_all_datasets(arch, datasets, xpaths, splits, use_less, seed, arch_config, workers, logger)
|
||||
torch.save(checkpoint, to_save_name)
|
||||
# log information
|
||||
logger.log('{:}'.format(checkpoint['info']))
|
||||
all_dataset_keys = checkpoint['all_dataset_keys']
|
||||
for dataset_key in all_dataset_keys:
|
||||
logger.log('\n{:} dataset : {:} {:}'.format('-'*15, dataset_key, '-'*15))
|
||||
dataset_info = checkpoint[dataset_key]
|
||||
#logger.log('Network ==>\n{:}'.format( dataset_info['net_string'] ))
|
||||
logger.log('Flops = {:} MB, Params = {:} MB'.format(dataset_info['flop'], dataset_info['param']))
|
||||
logger.log('config : {:}'.format(dataset_info['config']))
|
||||
logger.log('Training State (finish) = {:}'.format(dataset_info['finish-train']))
|
||||
last_epoch = dataset_info['total_epoch'] - 1
|
||||
train_acc1es, train_acc5es = dataset_info['train_acc1es'], dataset_info['train_acc5es']
|
||||
valid_acc1es, valid_acc5es = dataset_info['valid_acc1es'], dataset_info['valid_acc5es']
|
||||
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]))
|
||||
# measure elapsed time
|
||||
seed_time.update(time.time() - start_time)
|
||||
start_time = time.time()
|
||||
need_time = 'Time Left: {:}'.format( convert_secs2time(seed_time.avg * (len(seeds)-_is-1), True) )
|
||||
logger.log('\n<<<***>>> The {:02d}/{:02d}-th seed is {:} <finish> other procedures need {:}'.format(_is, len(seeds), seed, need_time))
|
||||
logger.close()
|
||||
try:
|
||||
arch = CellStructure.str2structure(model_str)
|
||||
except:
|
||||
raise ValueError("Invalid model string : {:}. It can not be found or parsed.".format(model_str))
|
||||
assert arch.check_valid_op(get_search_spaces("cell", "full")), "{:} has the invalid op.".format(arch)
|
||||
logger.log("Start train-evaluate {:}".format(arch.tostr()))
|
||||
logger.log("arch_config : {:}".format(arch_config))
|
||||
|
||||
start_time, seed_time = time.time(), AverageMeter()
|
||||
for _is, seed in enumerate(seeds):
|
||||
logger.log(
|
||||
"\nThe {:02d}/{:02d}-th seed is {:} ----------------------<.>----------------------".format(
|
||||
_is, len(seeds), seed
|
||||
)
|
||||
)
|
||||
to_save_name = save_dir / "seed-{:04d}.pth".format(seed)
|
||||
if to_save_name.exists():
|
||||
logger.log("Find the existing file {:}, directly load!".format(to_save_name))
|
||||
checkpoint = torch.load(to_save_name)
|
||||
else:
|
||||
logger.log("Does not find the existing file {:}, train and evaluate!".format(to_save_name))
|
||||
checkpoint = evaluate_all_datasets(
|
||||
arch, datasets, xpaths, splits, use_less, seed, arch_config, workers, logger
|
||||
)
|
||||
torch.save(checkpoint, to_save_name)
|
||||
# log information
|
||||
logger.log("{:}".format(checkpoint["info"]))
|
||||
all_dataset_keys = checkpoint["all_dataset_keys"]
|
||||
for dataset_key in all_dataset_keys:
|
||||
logger.log("\n{:} dataset : {:} {:}".format("-" * 15, dataset_key, "-" * 15))
|
||||
dataset_info = checkpoint[dataset_key]
|
||||
# logger.log('Network ==>\n{:}'.format( dataset_info['net_string'] ))
|
||||
logger.log("Flops = {:} MB, Params = {:} MB".format(dataset_info["flop"], dataset_info["param"]))
|
||||
logger.log("config : {:}".format(dataset_info["config"]))
|
||||
logger.log("Training State (finish) = {:}".format(dataset_info["finish-train"]))
|
||||
last_epoch = dataset_info["total_epoch"] - 1
|
||||
train_acc1es, train_acc5es = dataset_info["train_acc1es"], dataset_info["train_acc5es"]
|
||||
valid_acc1es, valid_acc5es = dataset_info["valid_acc1es"], dataset_info["valid_acc5es"]
|
||||
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],
|
||||
)
|
||||
)
|
||||
# measure elapsed time
|
||||
seed_time.update(time.time() - start_time)
|
||||
start_time = time.time()
|
||||
need_time = "Time Left: {:}".format(convert_secs2time(seed_time.avg * (len(seeds) - _is - 1), True))
|
||||
logger.log(
|
||||
"\n<<<***>>> The {:02d}/{:02d}-th seed is {:} <finish> other procedures need {:}".format(
|
||||
_is, len(seeds), seed, need_time
|
||||
)
|
||||
)
|
||||
logger.close()
|
||||
|
||||
|
||||
def generate_meta_info(save_dir, max_node, divide=40):
|
||||
aa_nas_bench_ss = get_search_spaces('cell', 'nas-bench-201')
|
||||
archs = CellStructure.gen_all(aa_nas_bench_ss, max_node, False)
|
||||
print ('There are {:} archs vs {:}.'.format(len(archs), len(aa_nas_bench_ss) ** ((max_node-1)*max_node/2)))
|
||||
aa_nas_bench_ss = get_search_spaces("cell", "nas-bench-201")
|
||||
archs = CellStructure.gen_all(aa_nas_bench_ss, max_node, False)
|
||||
print("There are {:} archs vs {:}.".format(len(archs), len(aa_nas_bench_ss) ** ((max_node - 1) * max_node / 2)))
|
||||
|
||||
random.seed( 88 ) # please do not change this line for reproducibility
|
||||
random.shuffle( archs )
|
||||
# to test fixed-random shuffle
|
||||
#print ('arch [0] : {:}\n---->>>> {:}'.format( archs[0], archs[0].tostr() ))
|
||||
#print ('arch [9] : {:}\n---->>>> {:}'.format( archs[9], archs[9].tostr() ))
|
||||
assert archs[0 ].tostr() == '|avg_pool_3x3~0|+|nor_conv_1x1~0|skip_connect~1|+|nor_conv_1x1~0|skip_connect~1|skip_connect~2|', 'please check the 0-th architecture : {:}'.format(archs[0])
|
||||
assert archs[9 ].tostr() == '|avg_pool_3x3~0|+|none~0|none~1|+|skip_connect~0|none~1|nor_conv_3x3~2|', 'please check the 9-th architecture : {:}'.format(archs[9])
|
||||
assert archs[123].tostr() == '|avg_pool_3x3~0|+|avg_pool_3x3~0|nor_conv_1x1~1|+|none~0|avg_pool_3x3~1|nor_conv_3x3~2|', 'please check the 123-th architecture : {:}'.format(archs[123])
|
||||
total_arch = len(archs)
|
||||
|
||||
num = 50000
|
||||
indexes_5W = list(range(num))
|
||||
random.seed( 1021 )
|
||||
random.shuffle( indexes_5W )
|
||||
train_split = sorted( list(set(indexes_5W[:num//2])) )
|
||||
valid_split = sorted( list(set(indexes_5W[num//2:])) )
|
||||
assert len(train_split) + len(valid_split) == num
|
||||
assert train_split[0] == 0 and train_split[10] == 26 and train_split[111] == 203 and valid_split[0] == 1 and valid_split[10] == 18 and valid_split[111] == 242, '{:} {:} {:} - {:} {:} {:}'.format(train_split[0], train_split[10], train_split[111], valid_split[0], valid_split[10], valid_split[111])
|
||||
splits = {num: {'train': train_split, 'valid': valid_split} }
|
||||
random.seed(88) # please do not change this line for reproducibility
|
||||
random.shuffle(archs)
|
||||
# to test fixed-random shuffle
|
||||
# print ('arch [0] : {:}\n---->>>> {:}'.format( archs[0], archs[0].tostr() ))
|
||||
# print ('arch [9] : {:}\n---->>>> {:}'.format( archs[9], archs[9].tostr() ))
|
||||
assert (
|
||||
archs[0].tostr()
|
||||
== "|avg_pool_3x3~0|+|nor_conv_1x1~0|skip_connect~1|+|nor_conv_1x1~0|skip_connect~1|skip_connect~2|"
|
||||
), "please check the 0-th architecture : {:}".format(archs[0])
|
||||
assert (
|
||||
archs[9].tostr() == "|avg_pool_3x3~0|+|none~0|none~1|+|skip_connect~0|none~1|nor_conv_3x3~2|"
|
||||
), "please check the 9-th architecture : {:}".format(archs[9])
|
||||
assert (
|
||||
archs[123].tostr() == "|avg_pool_3x3~0|+|avg_pool_3x3~0|nor_conv_1x1~1|+|none~0|avg_pool_3x3~1|nor_conv_3x3~2|"
|
||||
), "please check the 123-th architecture : {:}".format(archs[123])
|
||||
total_arch = len(archs)
|
||||
|
||||
info = {'archs' : [x.tostr() for x in archs],
|
||||
'total' : total_arch,
|
||||
'max_node' : max_node,
|
||||
'splits': splits}
|
||||
num = 50000
|
||||
indexes_5W = list(range(num))
|
||||
random.seed(1021)
|
||||
random.shuffle(indexes_5W)
|
||||
train_split = sorted(list(set(indexes_5W[: num // 2])))
|
||||
valid_split = sorted(list(set(indexes_5W[num // 2 :])))
|
||||
assert len(train_split) + len(valid_split) == num
|
||||
assert (
|
||||
train_split[0] == 0
|
||||
and train_split[10] == 26
|
||||
and train_split[111] == 203
|
||||
and valid_split[0] == 1
|
||||
and valid_split[10] == 18
|
||||
and valid_split[111] == 242
|
||||
), "{:} {:} {:} - {:} {:} {:}".format(
|
||||
train_split[0], train_split[10], train_split[111], valid_split[0], valid_split[10], valid_split[111]
|
||||
)
|
||||
splits = {num: {"train": train_split, "valid": valid_split}}
|
||||
|
||||
save_dir = Path(save_dir)
|
||||
save_dir.mkdir(parents=True, exist_ok=True)
|
||||
save_name = save_dir / 'meta-node-{:}.pth'.format(max_node)
|
||||
assert not save_name.exists(), '{:} already exist'.format(save_name)
|
||||
torch.save(info, save_name)
|
||||
print ('save the meta file into {:}'.format(save_name))
|
||||
info = {"archs": [x.tostr() for x in archs], "total": total_arch, "max_node": max_node, "splits": splits}
|
||||
|
||||
save_dir = Path(save_dir)
|
||||
save_dir.mkdir(parents=True, exist_ok=True)
|
||||
save_name = save_dir / "meta-node-{:}.pth".format(max_node)
|
||||
assert not save_name.exists(), "{:} already exist".format(save_name)
|
||||
torch.save(info, save_name)
|
||||
print("save the meta file into {:}".format(save_name))
|
||||
|
||||
|
||||
def traverse_net(max_node):
|
||||
aa_nas_bench_ss = get_search_spaces('cell', 'nats-bench')
|
||||
archs = CellStructure.gen_all(aa_nas_bench_ss, max_node, False)
|
||||
print ('There are {:} archs vs {:}.'.format(len(archs), len(aa_nas_bench_ss) ** ((max_node-1)*max_node/2)))
|
||||
aa_nas_bench_ss = get_search_spaces("cell", "nats-bench")
|
||||
archs = CellStructure.gen_all(aa_nas_bench_ss, max_node, False)
|
||||
print("There are {:} archs vs {:}.".format(len(archs), len(aa_nas_bench_ss) ** ((max_node - 1) * max_node / 2)))
|
||||
|
||||
random.seed( 88 ) # please do not change this line for reproducibility
|
||||
random.shuffle( archs )
|
||||
assert archs[0 ].tostr() == '|avg_pool_3x3~0|+|nor_conv_1x1~0|skip_connect~1|+|nor_conv_1x1~0|skip_connect~1|skip_connect~2|', 'please check the 0-th architecture : {:}'.format(archs[0])
|
||||
assert archs[9 ].tostr() == '|avg_pool_3x3~0|+|none~0|none~1|+|skip_connect~0|none~1|nor_conv_3x3~2|', 'please check the 9-th architecture : {:}'.format(archs[9])
|
||||
assert archs[123].tostr() == '|avg_pool_3x3~0|+|avg_pool_3x3~0|nor_conv_1x1~1|+|none~0|avg_pool_3x3~1|nor_conv_3x3~2|', 'please check the 123-th architecture : {:}'.format(archs[123])
|
||||
return [x.tostr() for x in archs]
|
||||
random.seed(88) # please do not change this line for reproducibility
|
||||
random.shuffle(archs)
|
||||
assert (
|
||||
archs[0].tostr()
|
||||
== "|avg_pool_3x3~0|+|nor_conv_1x1~0|skip_connect~1|+|nor_conv_1x1~0|skip_connect~1|skip_connect~2|"
|
||||
), "please check the 0-th architecture : {:}".format(archs[0])
|
||||
assert (
|
||||
archs[9].tostr() == "|avg_pool_3x3~0|+|none~0|none~1|+|skip_connect~0|none~1|nor_conv_3x3~2|"
|
||||
), "please check the 9-th architecture : {:}".format(archs[9])
|
||||
assert (
|
||||
archs[123].tostr() == "|avg_pool_3x3~0|+|avg_pool_3x3~0|nor_conv_1x1~1|+|none~0|avg_pool_3x3~1|nor_conv_3x3~2|"
|
||||
), "please check the 123-th architecture : {:}".format(archs[123])
|
||||
return [x.tostr() for x in archs]
|
||||
|
||||
|
||||
def filter_indexes(xlist, mode, save_dir, seeds):
|
||||
all_indexes = []
|
||||
for index in xlist:
|
||||
if mode == 'cover':
|
||||
all_indexes.append(index)
|
||||
all_indexes = []
|
||||
for index in xlist:
|
||||
if mode == "cover":
|
||||
all_indexes.append(index)
|
||||
else:
|
||||
for seed in seeds:
|
||||
temp_path = save_dir / "arch-{:06d}-seed-{:04d}.pth".format(index, seed)
|
||||
if not temp_path.exists():
|
||||
all_indexes.append(index)
|
||||
break
|
||||
print(
|
||||
"{:} [FILTER-INDEXES] : there are {:}/{:} architectures in total".format(
|
||||
time_string(), len(all_indexes), len(xlist)
|
||||
)
|
||||
)
|
||||
return all_indexes
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# mode_choices = ['meta', 'new', 'cover'] + ['specific-{:}'.format(_) for _ in CellArchitectures.keys()]
|
||||
parser = argparse.ArgumentParser(
|
||||
description="NATS-Bench (topology search space)", formatter_class=argparse.ArgumentDefaultsHelpFormatter
|
||||
)
|
||||
parser.add_argument("--mode", type=str, required=True, help="The script mode.")
|
||||
parser.add_argument(
|
||||
"--save_dir", type=str, default="output/NATS-Bench-topology", help="Folder to save checkpoints and log."
|
||||
)
|
||||
parser.add_argument("--max_node", type=int, default=4, help="The maximum node in a cell (please do not change it).")
|
||||
# use for train the model
|
||||
parser.add_argument("--workers", type=int, default=8, help="number of data loading workers (default: 2)")
|
||||
parser.add_argument("--srange", type=str, required=True, help="The range of models to be evaluated")
|
||||
parser.add_argument("--datasets", type=str, nargs="+", help="The applied datasets.")
|
||||
parser.add_argument("--xpaths", type=str, nargs="+", help="The root path for this dataset.")
|
||||
parser.add_argument("--splits", type=int, nargs="+", help="The root path for this dataset.")
|
||||
parser.add_argument(
|
||||
"--hyper", type=str, default="12", choices=["01", "12", "200"], help="The tag for hyper-parameters."
|
||||
)
|
||||
|
||||
parser.add_argument("--seeds", type=int, nargs="+", help="The range of models to be evaluated")
|
||||
parser.add_argument("--channel", type=int, default=16, help="The number of channels.")
|
||||
parser.add_argument("--num_cells", type=int, default=5, help="The number of cells in one stage.")
|
||||
parser.add_argument("--check_N", type=int, default=15625, help="For safety.")
|
||||
args = parser.parse_args()
|
||||
|
||||
assert args.mode in ["meta", "new", "cover"] or args.mode.startswith("specific-"), "invalid mode : {:}".format(
|
||||
args.mode
|
||||
)
|
||||
|
||||
if args.mode == "meta":
|
||||
generate_meta_info(args.save_dir, args.max_node)
|
||||
elif args.mode.startswith("specific"):
|
||||
assert len(args.mode.split("-")) == 2, "invalid mode : {:}".format(args.mode)
|
||||
model_str = args.mode.split("-")[1]
|
||||
train_single_model(
|
||||
args.save_dir,
|
||||
args.workers,
|
||||
args.datasets,
|
||||
args.xpaths,
|
||||
args.splits,
|
||||
args.use_less > 0,
|
||||
tuple(args.seeds),
|
||||
model_str,
|
||||
{"channel": args.channel, "num_cells": args.num_cells},
|
||||
)
|
||||
else:
|
||||
for seed in seeds:
|
||||
temp_path = save_dir / 'arch-{:06d}-seed-{:04d}.pth'.format(index, seed)
|
||||
if not temp_path.exists():
|
||||
all_indexes.append(index)
|
||||
break
|
||||
print('{:} [FILTER-INDEXES] : there are {:}/{:} architectures in total'.format(time_string(), len(all_indexes), len(xlist)))
|
||||
return all_indexes
|
||||
nets = traverse_net(args.max_node)
|
||||
if len(nets) != args.check_N:
|
||||
raise ValueError("Pre-num-check failed : {:} vs {:}".format(len(nets), args.check_N))
|
||||
opt_config = "./configs/nas-benchmark/hyper-opts/{:}E.config".format(args.hyper)
|
||||
if not os.path.isfile(opt_config):
|
||||
raise ValueError("{:} is not a file.".format(opt_config))
|
||||
save_dir = Path(args.save_dir) / "raw-data-{:}".format(args.hyper)
|
||||
save_dir.mkdir(parents=True, exist_ok=True)
|
||||
to_evaluate_indexes = split_str2indexes(args.srange, args.check_N, 5)
|
||||
if not len(args.seeds):
|
||||
raise ValueError("invalid length of seeds args: {:}".format(args.seeds))
|
||||
if not (len(args.datasets) == len(args.xpaths) == len(args.splits)):
|
||||
raise ValueError(
|
||||
"invalid infos : {:} vs {:} vs {:}".format(len(args.datasets), len(args.xpaths), len(args.splits))
|
||||
)
|
||||
if args.workers <= 0:
|
||||
raise ValueError("invalid number of workers : {:}".format(args.workers))
|
||||
|
||||
target_indexes = filter_indexes(to_evaluate_indexes, args.mode, save_dir, args.seeds)
|
||||
|
||||
if __name__ == '__main__':
|
||||
# mode_choices = ['meta', 'new', 'cover'] + ['specific-{:}'.format(_) for _ in CellArchitectures.keys()]
|
||||
parser = argparse.ArgumentParser(description='NATS-Bench (topology search space)', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||
parser.add_argument('--mode' , type=str, required=True, help='The script mode.')
|
||||
parser.add_argument('--save_dir', type=str, default='output/NATS-Bench-topology', help='Folder to save checkpoints and log.')
|
||||
parser.add_argument('--max_node', type=int, default=4, help='The maximum node in a cell (please do not change it).')
|
||||
# use for train the model
|
||||
parser.add_argument('--workers', type=int, default=8, help='number of data loading workers (default: 2)')
|
||||
parser.add_argument('--srange' , type=str, required=True, help='The range of models to be evaluated')
|
||||
parser.add_argument('--datasets', type=str, nargs='+', help='The applied datasets.')
|
||||
parser.add_argument('--xpaths', type=str, nargs='+', help='The root path for this dataset.')
|
||||
parser.add_argument('--splits', type=int, nargs='+', help='The root path for this dataset.')
|
||||
parser.add_argument('--hyper', type=str, default='12', choices=['01', '12', '200'], help='The tag for hyper-parameters.')
|
||||
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)
|
||||
|
||||
parser.add_argument('--seeds' , type=int, nargs='+', help='The range of models to be evaluated')
|
||||
parser.add_argument('--channel', type=int, default=16, help='The number of channels.')
|
||||
parser.add_argument('--num_cells', type=int, default=5, help='The number of cells in one stage.')
|
||||
parser.add_argument('--check_N', type=int, default=15625, help='For safety.')
|
||||
args = parser.parse_args()
|
||||
|
||||
assert args.mode in ['meta', 'new', 'cover'] or args.mode.startswith('specific-'), 'invalid mode : {:}'.format(args.mode)
|
||||
|
||||
if args.mode == 'meta':
|
||||
generate_meta_info(args.save_dir, args.max_node)
|
||||
elif args.mode.startswith('specific'):
|
||||
assert len(args.mode.split('-')) == 2, 'invalid mode : {:}'.format(args.mode)
|
||||
model_str = args.mode.split('-')[1]
|
||||
train_single_model(args.save_dir, args.workers, args.datasets, args.xpaths, args.splits, args.use_less>0, \
|
||||
tuple(args.seeds), model_str, {'channel': args.channel, 'num_cells': args.num_cells})
|
||||
else:
|
||||
nets = traverse_net(args.max_node)
|
||||
if len(nets) != args.check_N:
|
||||
raise ValueError('Pre-num-check failed : {:} vs {:}'.format(len(nets), args.check_N))
|
||||
opt_config = './configs/nas-benchmark/hyper-opts/{:}E.config'.format(args.hyper)
|
||||
if not os.path.isfile(opt_config):
|
||||
raise ValueError('{:} is not a file.'.format(opt_config))
|
||||
save_dir = Path(args.save_dir) / 'raw-data-{:}'.format(args.hyper)
|
||||
save_dir.mkdir(parents=True, exist_ok=True)
|
||||
to_evaluate_indexes = split_str2indexes(args.srange, args.check_N, 5)
|
||||
if not len(args.seeds):
|
||||
raise ValueError('invalid length of seeds args: {:}'.format(args.seeds))
|
||||
if not (len(args.datasets) == len(args.xpaths) == len(args.splits)):
|
||||
raise ValueError('invalid infos : {:} vs {:} vs {:}'.format(len(args.datasets), len(args.xpaths), len(args.splits)))
|
||||
if args.workers <= 0:
|
||||
raise ValueError('invalid number of workers : {:}'.format(args.workers))
|
||||
|
||||
target_indexes = filter_indexes(to_evaluate_indexes, args.mode, save_dir, args.seeds)
|
||||
|
||||
assert torch.cuda.is_available(), 'CUDA is not available.'
|
||||
torch.backends.cudnn.enabled = True
|
||||
torch.backends.cudnn.deterministic = True
|
||||
torch.set_num_threads(args.workers)
|
||||
|
||||
main(save_dir, args.workers, args.datasets, args.xpaths, args.splits, tuple(args.seeds), nets, opt_config, target_indexes, args.mode == 'cover', \
|
||||
{'name': 'infer.tiny', 'channel': args.channel, 'num_cells': args.num_cells})
|
||||
main(
|
||||
save_dir,
|
||||
args.workers,
|
||||
args.datasets,
|
||||
args.xpaths,
|
||||
args.splits,
|
||||
tuple(args.seeds),
|
||||
nets,
|
||||
opt_config,
|
||||
target_indexes,
|
||||
args.mode == "cover",
|
||||
{"name": "infer.tiny", "channel": args.channel, "num_cells": args.num_cells},
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user