4612cd198b
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825 lines
33 KiB
Python
825 lines
33 KiB
Python
###############################################################
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# NAS-Bench-201, ICLR 2020 (https://arxiv.org/abs/2001.00326) #
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###############################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
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###############################################################
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import os, sys, time, torch, random, argparse
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from PIL import ImageFile
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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from copy import deepcopy
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from pathlib import Path
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from xautodl.config_utils import load_config
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from xautodl.procedures import save_checkpoint, copy_checkpoint
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from xautodl.procedures import get_machine_info
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from xautodl.datasets import get_datasets
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from xautodl.log_utils import Logger, AverageMeter, time_string, convert_secs2time
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from xautodl.models import CellStructure, CellArchitectures, get_search_spaces
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from functions import evaluate_for_seed
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from torchvision import datasets, transforms
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# NASBENCH201_CONFIG_PATH = os.path.join( os.getcwd(), 'main_exp', 'transfer_nag')
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NASBENCH201_CONFIG_PATH = '/lustre/hpe/ws11/ws11.1/ws/xmuhanma-nbdit/autodl-projects/configs/nas-benchmark'
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def evaluate_all_datasets(arch, datasets, xpaths, splits, use_less, seed,
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arch_config, workers, logger):
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machine_info, arch_config = get_machine_info(), deepcopy(arch_config)
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all_infos = {'info': machine_info}
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all_dataset_keys = []
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# look all the datasets
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for dataset, xpath, split in zip(datasets, xpaths, splits):
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# train valid data
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task = None
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train_data, valid_data, xshape, class_num = get_datasets(
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dataset, xpath, -1, task)
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# load the configuration
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if dataset in ['mnist', 'svhn', 'aircraft', 'oxford']:
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if use_less:
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# config_path = os.path.join(
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# NASBENCH201_CONFIG_PATH, 'nas_bench_201/configs/nas-benchmark/LESS.config')
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config_path = os.path.join(
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NASBENCH201_CONFIG_PATH, 'LESS.config')
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else:
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# config_path = os.path.join(
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# NASBENCH201_CONFIG_PATH, 'nas_bench_201/configs/nas-benchmark/{}.config'.format(dataset))
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config_path = os.path.join(
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NASBENCH201_CONFIG_PATH, '{}.config'.format(dataset))
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p = os.path.join(
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NASBENCH201_CONFIG_PATH, '{:}-split.txt'.format(dataset))
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if not os.path.exists(p):
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import json
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label_list = list(range(len(train_data)))
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random.shuffle(label_list)
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strlist = [str(label_list[i]) for i in range(len(label_list))]
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splited = {'train': ["int", strlist[:len(train_data) // 2]],
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'valid': ["int", strlist[len(train_data) // 2:]]}
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with open(p, 'w') as f:
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f.write(json.dumps(splited))
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split_info = load_config(os.path.join(
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NASBENCH201_CONFIG_PATH, '{:}-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(
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config_path, {'class_num': class_num, 'xshape': xshape}, logger)
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# data loader
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train_loader = torch.utils.data.DataLoader(train_data, batch_size=config.batch_size,
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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,
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shuffle=False, num_workers=workers, pin_memory=True)
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splits = load_config(os.path.join(
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NASBENCH201_CONFIG_PATH, '{}-test-split.txt'.format(dataset)), 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,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(
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splits.xvalid),
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num_workers=workers, pin_memory=True),
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'x-test': torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(
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splits.xtest),
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num_workers=workers, pin_memory=True)
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}
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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={:}'.
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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(
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dataset_key, config))
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for key, value in ValLoaders.items():
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logger.log(
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'Evaluate ---->>>> {:10s} with {:} batchs'.format(key, len(value)))
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results = evaluate_for_seed(
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arch_config, config, arch, train_loader, ValLoaders, seed, logger)
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all_infos[dataset_key] = results
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all_dataset_keys.append(dataset_key)
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all_infos['all_dataset_keys'] = all_dataset_keys
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return all_infos
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def evaluate_all_datasets1(
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arch, datasets, xpaths, splits, use_less, seed, arch_config, workers, logger
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):
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machine_info, arch_config = get_machine_info(), deepcopy(arch_config)
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all_infos = {"info": machine_info}
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all_dataset_keys = []
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# look all the datasets
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for dataset, xpath, split in zip(datasets, xpaths, splits):
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# train valid data
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train_data, valid_data, xshape, class_num = get_datasets(dataset, xpath, -1)
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# load the configuration
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if dataset == "cifar10" or dataset == "cifar100":
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if use_less:
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config_path = "configs/nas-benchmark/LESS.config"
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else:
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config_path = "configs/nas-benchmark/CIFAR.config"
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split_info = load_config(
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"configs/nas-benchmark/cifar-split.txt", None, None
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)
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elif dataset.startswith("ImageNet16"):
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if use_less:
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config_path = "configs/nas-benchmark/LESS.config"
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else:
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config_path = "configs/nas-benchmark/ImageNet-16.config"
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split_info = load_config(
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"configs/nas-benchmark/{:}-split.txt".format(dataset), None, None
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)
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elif dataset.startswith("aircraft"):
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if use_less:
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config_path = "configs/nas-benchmark/LESS.config"
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else:
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config_path = "configs/nas-benchmark/aircraft.config"
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split_info = load_config(
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"configs/nas-benchmark/{:}-split.txt".format(dataset), None, None
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)
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elif dataset.startswith("oxford"):
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if use_less:
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config_path = "configs/nas-benchmark/LESS.config"
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else:
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config_path = "configs/nas-benchmark/oxford.config"
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split_info = load_config(
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"configs/nas-benchmark/{:}-split.txt".format(dataset), None, None
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)
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else:
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raise ValueError("invalid dataset : {:}".format(dataset))
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config = load_config(
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config_path, {"class_num": class_num, "xshape": xshape}, logger
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)
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# check whether use splited validation set
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# if dataset == 'aircraft':
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# split = True
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if bool(split):
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if dataset == "cifar10" or dataset == "cifar100":
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assert dataset == "cifar10"
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ValLoaders = {
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"ori-test": torch.utils.data.DataLoader(
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valid_data,
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batch_size=config.batch_size,
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shuffle=False,
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num_workers=workers,
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pin_memory=True,
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)
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}
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assert len(train_data) == len(split_info.train) + len(
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split_info.valid
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), "invalid length : {:} vs {:} + {:}".format(
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len(train_data), len(split_info.train), len(split_info.valid)
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)
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train_data_v2 = deepcopy(train_data)
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train_data_v2.transform = valid_data.transform
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valid_data = train_data_v2
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# data loader
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train_loader = torch.utils.data.DataLoader(
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train_data,
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batch_size=config.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.train),
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num_workers=workers,
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pin_memory=True,
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)
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valid_loader = torch.utils.data.DataLoader(
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valid_data,
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batch_size=config.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.valid),
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num_workers=workers,
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pin_memory=True,
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)
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ValLoaders["x-valid"] = valid_loader
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elif dataset == "aircraft":
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ValLoaders = {
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"ori-test": torch.utils.data.DataLoader(
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valid_data,
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batch_size=config.batch_size,
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shuffle=False,
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num_workers=workers,
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pin_memory=True,
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)
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}
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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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# 使用 DataLoader
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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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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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elif dataset == "oxford":
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ValLoaders = {
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"ori-test": torch.utils.data.DataLoader(
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valid_data,
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batch_size=config.batch_size,
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shuffle=False,
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num_workers=workers,
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pin_memory=True
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)
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}
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# train_data_v2 = deepcopy(train_data)
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# train_data_v2.transform = valid_data.transform
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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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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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else:
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# data loader
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train_loader = torch.utils.data.DataLoader(
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train_data,
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batch_size=config.batch_size,
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shuffle=True,
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num_workers=workers,
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pin_memory=True,
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)
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valid_loader = torch.utils.data.DataLoader(
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valid_data,
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batch_size=config.batch_size,
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shuffle=False,
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num_workers=workers,
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pin_memory=True,
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)
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if dataset == "cifar10" or dataset == "aircraft" or dataset == "oxford":
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ValLoaders = {"ori-test": valid_loader}
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elif dataset == "cifar100":
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cifar100_splits = load_config(
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"configs/nas-benchmark/cifar100-test-split.txt", None, None
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)
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ValLoaders = {
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"ori-test": valid_loader,
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"x-valid": torch.utils.data.DataLoader(
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valid_data,
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batch_size=config.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(
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cifar100_splits.xvalid
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),
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num_workers=workers,
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pin_memory=True,
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),
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"x-test": torch.utils.data.DataLoader(
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valid_data,
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batch_size=config.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(
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cifar100_splits.xtest
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),
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num_workers=workers,
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pin_memory=True,
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),
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}
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elif dataset == "ImageNet16-120":
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imagenet16_splits = load_config(
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"configs/nas-benchmark/imagenet-16-120-test-split.txt", None, None
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)
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ValLoaders = {
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"ori-test": valid_loader,
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"x-valid": torch.utils.data.DataLoader(
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valid_data,
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batch_size=config.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(
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imagenet16_splits.xvalid
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),
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num_workers=workers,
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pin_memory=True,
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),
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"x-test": torch.utils.data.DataLoader(
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valid_data,
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batch_size=config.batch_size,
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sampler=torch.utils.data.sampler.SubsetRandomSampler(
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imagenet16_splits.xtest
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),
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num_workers=workers,
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pin_memory=True,
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),
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}
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else:
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raise ValueError("invalid dataset : {:}".format(dataset))
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dataset_key = "{:}".format(dataset)
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if bool(split):
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dataset_key = dataset_key + "-valid"
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logger.log(
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"Evaluate ||||||| {:10s} ||||||| Train-Num={:}, Valid-Num={:}, Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}".format(
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dataset_key,
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len(train_data),
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len(valid_data),
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len(train_loader),
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len(valid_loader),
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config.batch_size,
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)
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)
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logger.log(
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"Evaluate ||||||| {:10s} ||||||| Config={:}".format(dataset_key, config)
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)
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for key, value in ValLoaders.items():
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logger.log(
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"Evaluate ---->>>> {:10s} with {:} batchs".format(key, len(value))
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)
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results = evaluate_for_seed(
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arch_config, config, arch, train_loader, ValLoaders, seed, logger
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)
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all_infos[dataset_key] = results
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all_dataset_keys.append(dataset_key)
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all_infos["all_dataset_keys"] = all_dataset_keys
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return all_infos
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def main(
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save_dir,
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workers,
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datasets,
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xpaths,
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splits,
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use_less,
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srange,
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arch_index,
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seeds,
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cover_mode,
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meta_info,
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arch_config,
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):
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assert torch.cuda.is_available(), "CUDA is not available."
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torch.backends.cudnn.enabled = True
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# torch.backends.cudnn.benchmark = True
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torch.backends.cudnn.deterministic = True
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torch.set_num_threads(workers)
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assert (
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len(srange) == 2 and 0 <= srange[0] <= srange[1]
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), "invalid srange : {:}".format(srange)
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if use_less:
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sub_dir = Path(save_dir) / "{:06d}-{:06d}-C{:}-N{:}-LESS".format(
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srange[0], srange[1], arch_config["channel"], arch_config["num_cells"]
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)
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else:
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sub_dir = Path(save_dir) / "{:06d}-{:06d}-C{:}-N{:}".format(
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srange[0], srange[1], arch_config["channel"], arch_config["num_cells"]
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)
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logger = Logger(str(sub_dir), 0, False)
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all_archs = meta_info["archs"]
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assert srange[1] < meta_info["total"], "invalid range : {:}-{:} vs. {:}".format(
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srange[0], srange[1], meta_info["total"]
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)
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assert (
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arch_index == -1 or srange[0] <= arch_index <= srange[1]
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), "invalid range : {:} vs. {:} vs. {:}".format(srange[0], arch_index, srange[1])
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if arch_index == -1:
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to_evaluate_indexes = list(range(srange[0], srange[1] + 1))
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else:
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to_evaluate_indexes = [arch_index]
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logger.log("xargs : seeds = {:}".format(seeds))
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logger.log("xargs : arch_index = {:}".format(arch_index))
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logger.log("xargs : cover_mode = {:}".format(cover_mode))
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logger.log("-" * 100)
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logger.log(
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"Start evaluating range =: {:06d} vs. {:06d} vs. {:06d} / {:06d} with cover-mode={:}".format(
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srange[0], arch_index, srange[1], meta_info["total"], cover_mode
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)
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)
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for i, (dataset, xpath, split) in enumerate(zip(datasets, xpaths, splits)):
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logger.log(
|
|
"--->>> Evaluate {:}/{:} : dataset={:9s}, path={:}, split={:}".format(
|
|
i, len(datasets), dataset, xpath, split
|
|
)
|
|
)
|
|
logger.log("--->>> architecture config : {:}".format(arch_config))
|
|
|
|
start_time, epoch_time = time.time(), AverageMeter()
|
|
for i, index in enumerate(to_evaluate_indexes):
|
|
arch = all_archs[index]
|
|
logger.log(
|
|
"\n{:} evaluate {:06d}/{:06d} ({:06d}/{:06d})-th architecture [seeds={:}] {:}".format(
|
|
"-" * 15,
|
|
i,
|
|
len(to_evaluate_indexes),
|
|
index,
|
|
meta_info["total"],
|
|
seeds,
|
|
"-" * 15,
|
|
)
|
|
)
|
|
# logger.log('{:} {:} {:}'.format('-'*15, arch.tostr(), '-'*15))
|
|
logger.log("{:} {:} {:}".format("-" * 15, arch, "-" * 15))
|
|
|
|
# test this arch on different datasets with different seeds
|
|
has_continue = False
|
|
for seed in seeds:
|
|
to_save_name = sub_dir / "arch-{:06d}-seed-{:04d}.pth".format(index, seed)
|
|
if to_save_name.exists():
|
|
if cover_mode:
|
|
logger.log(
|
|
"Find existing file : {:}, remove it before evaluation".format(
|
|
to_save_name
|
|
)
|
|
)
|
|
os.remove(str(to_save_name))
|
|
else:
|
|
logger.log(
|
|
"Find existing file : {:}, skip this evaluation".format(
|
|
to_save_name
|
|
)
|
|
)
|
|
has_continue = True
|
|
continue
|
|
results = evaluate_all_datasets(
|
|
CellStructure.str2structure(arch),
|
|
datasets,
|
|
xpaths,
|
|
splits,
|
|
use_less,
|
|
seed,
|
|
arch_config,
|
|
workers,
|
|
logger,
|
|
)
|
|
torch.save(results, to_save_name)
|
|
logger.log(
|
|
"{:} --evaluate-- {:06d}/{:06d} ({:06d}/{:06d})-th seed={:} done, save into {:}".format(
|
|
"-" * 15,
|
|
i,
|
|
len(to_evaluate_indexes),
|
|
index,
|
|
meta_info["total"],
|
|
seed,
|
|
to_save_name,
|
|
)
|
|
)
|
|
# measure elapsed time
|
|
if not has_continue:
|
|
epoch_time.update(time.time() - start_time)
|
|
start_time = time.time()
|
|
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, meta_info["total"], 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))
|
|
|
|
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)
|
|
)
|
|
)
|
|
|
|
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}}
|
|
|
|
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))
|
|
|
|
script_name_full = save_dir / "BENCH-201-N{:}.opt-full.script".format(max_node)
|
|
script_name_less = save_dir / "BENCH-201-N{:}.opt-less.script".format(max_node)
|
|
full_file = open(str(script_name_full), "w")
|
|
less_file = open(str(script_name_less), "w")
|
|
gaps = total_arch // divide
|
|
for start in range(0, total_arch, gaps):
|
|
xend = min(start + gaps, total_arch)
|
|
full_file.write(
|
|
"bash ./scripts-search/NAS-Bench-201/train-models.sh 0 {:5d} {:5d} -1 '777 888 999'\n".format(
|
|
start, xend - 1
|
|
)
|
|
)
|
|
less_file.write(
|
|
"bash ./scripts-search/NAS-Bench-201/train-models.sh 1 {:5d} {:5d} -1 '777 888 999'\n".format(
|
|
start, xend - 1
|
|
)
|
|
)
|
|
print(
|
|
"save the training script into {:} and {:}".format(
|
|
script_name_full, script_name_less
|
|
)
|
|
)
|
|
full_file.close()
|
|
less_file.close()
|
|
|
|
script_name = save_dir / "meta-node-{:}.cal-script.txt".format(max_node)
|
|
macro = "OMP_NUM_THREADS=6 CUDA_VISIBLE_DEVICES=0"
|
|
with open(str(script_name), "w") as cfile:
|
|
for start in range(0, total_arch, gaps):
|
|
xend = min(start + gaps, total_arch)
|
|
cfile.write(
|
|
"{:} python exps/NAS-Bench-201/statistics.py --mode cal --target_dir {:06d}-{:06d}-C16-N5\n".format(
|
|
macro, start, xend - 1
|
|
)
|
|
)
|
|
print("save the post-processing script into {:}".format(script_name))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
# mode_choices = ['meta', 'new', 'cover'] + ['specific-{:}'.format(_) for _ in CellArchitectures.keys()]
|
|
# parser = argparse.ArgumentParser(description='Algorithm-Agnostic NAS Benchmark', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
|
parser = argparse.ArgumentParser(
|
|
description="NAS-Bench-201",
|
|
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
|
)
|
|
parser.add_argument("--mode", type=str, required=True, help="The script mode.")
|
|
parser.add_argument(
|
|
"--save_dir", type=str, help="Folder to save checkpoints and log."
|
|
)
|
|
parser.add_argument("--max_node", type=int, help="The maximum node in a cell.")
|
|
# 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=int, nargs="+", help="The range of models to be evaluated"
|
|
)
|
|
parser.add_argument(
|
|
"--arch_index",
|
|
type=int,
|
|
default=-1,
|
|
help="The architecture index to be evaluated (cover mode).",
|
|
)
|
|
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(
|
|
"--use_less",
|
|
type=int,
|
|
default=0,
|
|
choices=[0, 1],
|
|
help="Using the less-training-epoch config.",
|
|
)
|
|
parser.add_argument(
|
|
"--seeds", type=int, nargs="+", help="The range of models to be evaluated"
|
|
)
|
|
parser.add_argument("--channel", type=int, help="The number of channels.")
|
|
parser.add_argument(
|
|
"--num_cells", type=int, help="The number of cells in one stage."
|
|
)
|
|
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:
|
|
meta_path = Path(args.save_dir) / "meta-node-{:}.pth".format(args.max_node)
|
|
assert meta_path.exists(), "{:} does not exist.".format(meta_path)
|
|
meta_info = torch.load(meta_path)
|
|
# check whether args is ok
|
|
assert (
|
|
len(args.srange) == 2 and args.srange[0] <= args.srange[1]
|
|
), "invalid length of srange args: {:}".format(args.srange)
|
|
assert len(args.seeds) > 0, "invalid length of seeds args: {:}".format(
|
|
args.seeds
|
|
)
|
|
assert (
|
|
len(args.datasets) == len(args.xpaths) == len(args.splits)
|
|
), "invalid infos : {:} vs {:} vs {:}".format(
|
|
len(args.datasets), len(args.xpaths), len(args.splits)
|
|
)
|
|
assert args.workers > 0, "invalid number of workers : {:}".format(args.workers)
|
|
|
|
main(
|
|
args.save_dir,
|
|
args.workers,
|
|
args.datasets,
|
|
args.xpaths,
|
|
args.splits,
|
|
args.use_less > 0,
|
|
tuple(args.srange),
|
|
args.arch_index,
|
|
tuple(args.seeds),
|
|
args.mode == "cover",
|
|
meta_info,
|
|
{"channel": args.channel, "num_cells": args.num_cells},
|
|
)
|