Update NATS-Bench (sss version 1.0)
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@ -110,6 +110,7 @@ If you find that this project helps your research, please consider citing some o
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title = {Network Pruning via Transformable Architecture Search},
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author = {Dong, Xuanyi and Yang, Yi},
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booktitle = {Neural Information Processing Systems (NeurIPS)},
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pages = {760--771},
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year = {2019}
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}
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@inproceedings{dong2019one,
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@ -64,8 +64,7 @@
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<td align="center" valign="middle"> <a href="https://xuanyidong.com/assets/projects/NATS-Bench"> NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size</a> </td>
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<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/master/docs/NATS-Bench.md">NATS-Bench.md</a> </td>
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</tr>
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<tr> <!-- (6-th row) -->
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<tr> <!-- (7-th row) -->
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<td align="center" valign="middle"> ... </td>
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<td align="center" valign="middle"> ENAS / REA / REINFORCE / BOHB </td>
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<td align="center" valign="middle"> Please check the original papers. </td>
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@ -111,6 +110,7 @@ Some methods use knowledge distillation (KD), which require pre-trained models.
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author = {Dong, Xuanyi and Yang, Yi},
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booktitle = {Neural Information Processing Systems (NeurIPS)},
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year = {2019}
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pages = {760--771},
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}
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@inproceedings{dong2019one,
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title = {One-Shot Neural Architecture Search via Self-Evaluated Template Network},
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@ -16,11 +16,17 @@ This facilitates a much larger community of researchers to focus on developing b
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## The Procedure of Creating NATS-Bench
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1, train all architecture candidate in the size search space with 90 epochs and use the random seed of `777`.
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### The Size Search Space
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The following command will train all architecture candidate in the size search space with 90 epochs and use the random seed of `777`. If you want to use a different number of training epochs, please replace `90` with it, such as `01` or `12`. If you want to use a different
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```
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bash ./scripts/NATS-Bench/train-shapes.sh 00000-32767 90 777
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```
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The checkpoint of all candidates are located at `output/NATS-Bench-size` by default
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The checkpoint of all candidates are located at `output/NATS-Bench-size` by default.
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### The Topology Search Space
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@ -3,6 +3,16 @@
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##############################################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.07 #
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##############################################################################
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# This file is used to train (all) architecture candidate in the size search #
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# space in NATS-Bench (sss) with different hyper-parameters. #
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# When use mode=new, it will automatically detect whether the checkpoint of #
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# a trial exists, if so, it will skip this trial. When use mode=cover, it #
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# will ignore the (possible) existing checkpoint, run each trial, and save. #
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# (NOTE): the topology for all candidates in sss is fixed as: ######################
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# |nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|nor_conv_3x3~1|nor_conv_3x3~2| #
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###################################################################################################
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# Please use the script of scripts/NATS-Bench/train-shapes.sh to run. #
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##############################################################################
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import os, sys, time, torch, argparse
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from typing import List, Text, Dict, Any
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from PIL import ImageFile
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323
exps/NATS-Bench/main-tss.py
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323
exps/NATS-Bench/main-tss.py
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@ -0,0 +1,323 @@
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##############################################################################
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# NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size #
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##############################################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.07 #
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##############################################################################
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# This file is used to train (all) architecture candidate in the topology #
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# search space in NATS-Bench (tss) with different hyper-parameters. #
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# When use mode=meta,
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###
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##############################################################################
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# 1, generate meta data: #
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# python ./exps/NATS-Bench/main-tss.py --mode meta #
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##############################################################################
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import os, sys, time, torch, random, argparse
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from PIL import ImageFile
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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from copy import deepcopy
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from pathlib import Path
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lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
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if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
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from config_utils import load_config
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from procedures import 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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def evaluate_all_datasets(arch, datasets, xpaths, splits, use_less, seed, arch_config, workers, logger):
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machine_info, arch_config = get_machine_info(), deepcopy(arch_config)
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all_infos = {'info': machine_info}
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all_dataset_keys = []
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# look all the datasets
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for dataset, xpath, split in zip(datasets, xpaths, splits):
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# train valid data
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train_data, valid_data, xshape, class_num = get_datasets(dataset, xpath, -1)
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# load the configuration
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if dataset == 'cifar10' or dataset == 'cifar100':
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if use_less: config_path = 'configs/nas-benchmark/LESS.config'
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else : config_path = 'configs/nas-benchmark/CIFAR.config'
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split_info = load_config('configs/nas-benchmark/cifar-split.txt', None, None)
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elif dataset.startswith('ImageNet16'):
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if use_less: config_path = 'configs/nas-benchmark/LESS.config'
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else : config_path = 'configs/nas-benchmark/ImageNet-16.config'
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split_info = load_config('configs/nas-benchmark/{:}-split.txt'.format(dataset), None, None)
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else:
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raise ValueError('invalid dataset : {:}'.format(dataset))
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config = load_config(config_path, \
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{'class_num': class_num,
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'xshape' : xshape}, \
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logger)
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# check whether use splited validation set
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if bool(split):
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assert dataset == 'cifar10'
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ValLoaders = {'ori-test': torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, shuffle=False, num_workers=workers, pin_memory=True)}
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assert len(train_data) == len(split_info.train) + len(split_info.valid), 'invalid length : {:} vs {:} + {:}'.format(len(train_data), len(split_info.train), len(split_info.valid))
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train_data_v2 = deepcopy(train_data)
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train_data_v2.transform = valid_data.transform
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valid_data = train_data_v2
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# data loader
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train_loader = torch.utils.data.DataLoader(train_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.train), num_workers=workers, pin_memory=True)
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valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.valid), num_workers=workers, pin_memory=True)
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ValLoaders['x-valid'] = valid_loader
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else:
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# data loader
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train_loader = torch.utils.data.DataLoader(train_data, batch_size=config.batch_size, shuffle=True , num_workers=workers, pin_memory=True)
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valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, shuffle=False, num_workers=workers, pin_memory=True)
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if dataset == 'cifar10':
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ValLoaders = {'ori-test': valid_loader}
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elif dataset == 'cifar100':
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cifar100_splits = load_config('configs/nas-benchmark/cifar100-test-split.txt', None, None)
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ValLoaders = {'ori-test': valid_loader,
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'x-valid' : torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xvalid), num_workers=workers, pin_memory=True),
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'x-test' : torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xtest ), num_workers=workers, pin_memory=True)
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}
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elif dataset == 'ImageNet16-120':
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imagenet16_splits = load_config('configs/nas-benchmark/imagenet-16-120-test-split.txt', None, None)
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ValLoaders = {'ori-test': valid_loader,
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'x-valid' : torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet16_splits.xvalid), num_workers=workers, pin_memory=True),
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'x-test' : torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet16_splits.xtest ), num_workers=workers, pin_memory=True)
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}
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else:
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raise ValueError('invalid dataset : {:}'.format(dataset))
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dataset_key = '{:}'.format(dataset)
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if bool(split): dataset_key = dataset_key + '-valid'
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logger.log('Evaluate ||||||| {:10s} ||||||| Train-Num={:}, Valid-Num={:}, Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(dataset_key, len(train_data), len(valid_data), len(train_loader), len(valid_loader), config.batch_size))
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logger.log('Evaluate ||||||| {:10s} ||||||| Config={:}'.format(dataset_key, config))
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for key, value in ValLoaders.items():
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logger.log('Evaluate ---->>>> {:10s} with {:} batchs'.format(key, len(value)))
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results = 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, workers, datasets, xpaths, splits, use_less, srange, arch_index, seeds, cover_mode, meta_info, arch_config):
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assert torch.cuda.is_available(), 'CUDA is not available.'
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torch.backends.cudnn.enabled = True
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#torch.backends.cudnn.benchmark = True
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torch.backends.cudnn.deterministic = True
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torch.set_num_threads( workers )
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assert len(srange) == 2 and 0 <= srange[0] <= srange[1], 'invalid srange : {:}'.format(srange)
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if use_less:
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sub_dir = Path(save_dir) / '{:06d}-{:06d}-C{:}-N{:}-LESS'.format(srange[0], srange[1], arch_config['channel'], arch_config['num_cells'])
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else:
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sub_dir = Path(save_dir) / '{:06d}-{:06d}-C{:}-N{:}'.format(srange[0], srange[1], arch_config['channel'], arch_config['num_cells'])
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logger = Logger(str(sub_dir), 0, False)
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all_archs = meta_info['archs']
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assert srange[1] < meta_info['total'], 'invalid range : {:}-{:} vs. {:}'.format(srange[0], srange[1], meta_info['total'])
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assert arch_index == -1 or srange[0] <= arch_index <= srange[1], 'invalid range : {:} vs. {:} vs. {:}'.format(srange[0], arch_index, srange[1])
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if arch_index == -1:
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to_evaluate_indexes = list(range(srange[0], srange[1]+1))
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else:
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to_evaluate_indexes = [arch_index]
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logger.log('xargs : seeds = {:}'.format(seeds))
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logger.log('xargs : arch_index = {:}'.format(arch_index))
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logger.log('xargs : cover_mode = {:}'.format(cover_mode))
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logger.log('-'*100)
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logger.log('Start evaluating range =: {:06d} vs. {:06d} vs. {:06d} / {:06d} with cover-mode={:}'.format(srange[0], arch_index, srange[1], meta_info['total'], cover_mode))
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for i, (dataset, xpath, split) in enumerate(zip(datasets, xpaths, splits)):
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logger.log('--->>> Evaluate {:}/{:} : dataset={:9s}, path={:}, split={:}'.format(i, len(datasets), dataset, xpath, split))
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logger.log('--->>> architecture config : {:}'.format(arch_config))
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start_time, epoch_time = time.time(), AverageMeter()
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for i, index in enumerate(to_evaluate_indexes):
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arch = all_archs[index]
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logger.log('\n{:} evaluate {:06d}/{:06d} ({:06d}/{:06d})-th architecture [seeds={:}] {:}'.format('-'*15, i, len(to_evaluate_indexes), index, meta_info['total'], seeds, '-'*15))
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#logger.log('{:} {:} {:}'.format('-'*15, arch.tostr(), '-'*15))
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logger.log('{:} {:} {:}'.format('-'*15, arch, '-'*15))
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# test this arch on different datasets with different seeds
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has_continue = False
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for seed in seeds:
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to_save_name = sub_dir / 'arch-{:06d}-seed-{:04d}.pth'.format(index, seed)
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if to_save_name.exists():
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if cover_mode:
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logger.log('Find existing file : {:}, remove it before evaluation'.format(to_save_name))
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os.remove(str(to_save_name))
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else :
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logger.log('Find existing file : {:}, skip this evaluation'.format(to_save_name))
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has_continue = True
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continue
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results = evaluate_all_datasets(CellStructure.str2structure(arch), \
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datasets, xpaths, splits, use_less, seed, \
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arch_config, workers, logger)
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torch.save(results, to_save_name)
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logger.log('{:} --evaluate-- {:06d}/{:06d} ({:06d}/{:06d})-th seed={:} done, save into {:}'.format('-'*15, i, len(to_evaluate_indexes), index, meta_info['total'], seed, to_save_name))
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# measure elapsed time
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if not has_continue: epoch_time.update(time.time() - start_time)
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start_time = time.time()
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need_time = 'Time Left: {:}'.format( convert_secs2time(epoch_time.avg * (len(to_evaluate_indexes)-i-1), True) )
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logger.log('This arch costs : {:}'.format( convert_secs2time(epoch_time.val, True) ))
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logger.log('{:}'.format('*'*100))
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logger.log('{:} {:74s} {:}'.format('*'*10, '{:06d}/{:06d} ({:06d}/{:06d})-th done, left {:}'.format(i, len(to_evaluate_indexes), index, meta_info['total'], need_time), '*'*10))
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logger.log('{:}'.format('*'*100))
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logger.close()
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def train_single_model(save_dir, workers, datasets, xpaths, splits, use_less, seeds, model_str, arch_config):
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assert torch.cuda.is_available(), 'CUDA is not available.'
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torch.backends.cudnn.enabled = True
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torch.backends.cudnn.deterministic = True
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#torch.backends.cudnn.benchmark = True
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torch.set_num_threads( workers )
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save_dir = Path(save_dir) / 'specifics' / '{:}-{:}-{:}-{:}'.format('LESS' if use_less else 'FULL', model_str, arch_config['channel'], arch_config['num_cells'])
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logger = Logger(str(save_dir), 0, False)
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if model_str in CellArchitectures:
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arch = CellArchitectures[model_str]
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logger.log('The model string is found in pre-defined architecture dict : {:}'.format(model_str))
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else:
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try:
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arch = CellStructure.str2structure(model_str)
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except:
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raise ValueError('Invalid model string : {:}. It can not be found or parsed.'.format(model_str))
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assert arch.check_valid_op(get_search_spaces('cell', 'full')), '{:} has the invalid op.'.format(arch)
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logger.log('Start train-evaluate {:}'.format(arch.tostr()))
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logger.log('arch_config : {:}'.format(arch_config))
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start_time, seed_time = time.time(), AverageMeter()
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for _is, seed in enumerate(seeds):
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logger.log('\nThe {:02d}/{:02d}-th seed is {:} ----------------------<.>----------------------'.format(_is, len(seeds), seed))
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to_save_name = save_dir / 'seed-{:04d}.pth'.format(seed)
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if to_save_name.exists():
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logger.log('Find the existing file {:}, directly load!'.format(to_save_name))
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checkpoint = torch.load(to_save_name)
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else:
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logger.log('Does not find the existing file {:}, train and evaluate!'.format(to_save_name))
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checkpoint = evaluate_all_datasets(arch, datasets, xpaths, splits, use_less, seed, arch_config, workers, logger)
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torch.save(checkpoint, to_save_name)
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# log information
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logger.log('{:}'.format(checkpoint['info']))
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all_dataset_keys = checkpoint['all_dataset_keys']
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for dataset_key in all_dataset_keys:
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logger.log('\n{:} dataset : {:} {:}'.format('-'*15, dataset_key, '-'*15))
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dataset_info = checkpoint[dataset_key]
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#logger.log('Network ==>\n{:}'.format( dataset_info['net_string'] ))
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logger.log('Flops = {:} MB, Params = {:} MB'.format(dataset_info['flop'], dataset_info['param']))
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logger.log('config : {:}'.format(dataset_info['config']))
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logger.log('Training State (finish) = {:}'.format(dataset_info['finish-train']))
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last_epoch = dataset_info['total_epoch'] - 1
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train_acc1es, train_acc5es = dataset_info['train_acc1es'], dataset_info['train_acc5es']
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valid_acc1es, valid_acc5es = dataset_info['valid_acc1es'], dataset_info['valid_acc5es']
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logger.log('Last Info : Train = Acc@1 {:.2f}% Acc@5 {:.2f}% Error@1 {:.2f}%, Test = Acc@1 {:.2f}% Acc@5 {:.2f}% Error@1 {:.2f}%'.format(train_acc1es[last_epoch], train_acc5es[last_epoch], 100-train_acc1es[last_epoch], valid_acc1es[last_epoch], valid_acc5es[last_epoch], 100-valid_acc1es[last_epoch]))
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# measure elapsed time
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seed_time.update(time.time() - start_time)
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start_time = time.time()
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need_time = 'Time Left: {:}'.format( convert_secs2time(seed_time.avg * (len(seeds)-_is-1), True) )
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logger.log('\n<<<***>>> The {:02d}/{:02d}-th seed is {:} <finish> other procedures need {:}'.format(_is, len(seeds), seed, need_time))
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logger.close()
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def generate_meta_info(save_dir, max_node, divide=40):
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aa_nas_bench_ss = get_search_spaces('cell', 'nas-bench-201')
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archs = CellStructure.gen_all(aa_nas_bench_ss, max_node, False)
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print ('There are {:} archs vs {:}.'.format(len(archs), len(aa_nas_bench_ss) ** ((max_node-1)*max_node/2)))
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random.seed( 88 ) # please do not change this line for reproducibility
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random.shuffle( archs )
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# to test fixed-random shuffle
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#print ('arch [0] : {:}\n---->>>> {:}'.format( archs[0], archs[0].tostr() ))
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#print ('arch [9] : {:}\n---->>>> {:}'.format( archs[9], archs[9].tostr() ))
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assert archs[0 ].tostr() == '|avg_pool_3x3~0|+|nor_conv_1x1~0|skip_connect~1|+|nor_conv_1x1~0|skip_connect~1|skip_connect~2|', 'please check the 0-th architecture : {:}'.format(archs[0])
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assert archs[9 ].tostr() == '|avg_pool_3x3~0|+|none~0|none~1|+|skip_connect~0|none~1|nor_conv_3x3~2|', 'please check the 9-th architecture : {:}'.format(archs[9])
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assert archs[123].tostr() == '|avg_pool_3x3~0|+|avg_pool_3x3~0|nor_conv_1x1~1|+|none~0|avg_pool_3x3~1|nor_conv_3x3~2|', 'please check the 123-th architecture : {:}'.format(archs[123])
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total_arch = len(archs)
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||||
|
||||
num = 50000
|
||||
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='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', '90'], 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.')
|
||||
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.hyper, \
|
||||
tuple(args.srange), args.arch_index, tuple(args.seeds), \
|
||||
args.mode == 'cover', meta_info, \
|
||||
{'channel': args.channel, 'num_cells': args.num_cells})
|
||||
@ -1,8 +1,15 @@
|
||||
#####################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
|
||||
#####################################################
|
||||
# python exps/NAS-Bench-201/xshape-collect.py
|
||||
#####################################################
|
||||
##############################################################################
|
||||
# NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size #
|
||||
##############################################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.07 #
|
||||
##############################################################################
|
||||
# This file is used to re-orangize all checkpoints (created by main-sss.py) #
|
||||
# into a single benchmark file. Besides, for each trial, we will merge the #
|
||||
# information of all its trials into a single file. #
|
||||
# #
|
||||
# Usage: #
|
||||
# python exps/NATS-Bench/sss-collect.py #
|
||||
##############################################################################
|
||||
import os, re, sys, time, argparse, collections
|
||||
import numpy as np
|
||||
import torch
|
||||
@ -14,10 +21,10 @@ lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
|
||||
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
|
||||
from log_utils import AverageMeter, time_string, convert_secs2time
|
||||
from config_utils import dict2config
|
||||
# NAS-Bench-201 related module or function
|
||||
from models import CellStructure, get_cell_based_tiny_net
|
||||
from nas_201_api import ArchResults, ResultsCount
|
||||
from procedures import bench_pure_evaluate as pure_evaluate, get_nas_bench_loaders
|
||||
from utils import get_md5_file
|
||||
|
||||
|
||||
def account_one_arch(arch_index: int, arch_str: Text, checkpoints: List[Text], datasets: List[Text]) -> ArchResults:
|
||||
@ -163,41 +170,54 @@ def simplify(save_dir, save_name, nets, total):
|
||||
for seed, xlist in seed2names.items():
|
||||
seeds.add(seed)
|
||||
nums.append(len(xlist))
|
||||
print(' seed={:}, num={:}'.format(seed, len(xlist)))
|
||||
# assert len(nets) == total == max(nums), 'there are some missed files : {:} vs {:}'.format(max(nums), total)
|
||||
print(' [seed={:}] there are {:} checkpoints.'.format(seed, len(xlist)))
|
||||
assert len(nets) == total == max(nums), 'there are some missed files : {:} vs {:}'.format(max(nums), total)
|
||||
print('{:} start simplify the checkpoint.'.format(time_string()))
|
||||
|
||||
datasets = ('cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120')
|
||||
|
||||
simplify_save_dir, arch2infos, evaluated_indexes = save_dir / save_name, {}, set()
|
||||
simplify_save_dir.mkdir(parents=True, exist_ok=True)
|
||||
# Create the directory to save the processed data
|
||||
# full_save_dir contains all benchmark files with trained weights.
|
||||
# simplify_save_dir contains all benchmark files without trained weights.
|
||||
full_save_dir = save_dir / (save_name + '-FULL')
|
||||
simple_save_dir = save_dir / (save_name + '-SIMPLIFY')
|
||||
full_save_dir.mkdir(parents=True, exist_ok=True)
|
||||
simple_save_dir.mkdir(parents=True, exist_ok=True)
|
||||
# all data in memory
|
||||
arch2infos, evaluated_indexes = dict(), set()
|
||||
end_time, arch_time = time.time(), AverageMeter()
|
||||
# for index, arch_str in enumerate(nets):
|
||||
|
||||
for index in tqdm(range(total)):
|
||||
arch_str = nets[index]
|
||||
hp2info = OrderedDict()
|
||||
|
||||
full_save_path = full_save_dir / '{:06d}.npy'.format(index)
|
||||
simple_save_path = simple_save_dir / '{:06d}.npy'.format(index)
|
||||
|
||||
for hp in hps:
|
||||
sub_save_dir = save_dir / 'raw-data-{:}'.format(hp)
|
||||
ckps = [sub_save_dir / 'arch-{:06d}-seed-{:}.pth'.format(index, seed) for seed in seeds]
|
||||
ckps = [x for x in ckps if x.exists()]
|
||||
if len(ckps) == 0: raise ValueError('Invalid data : index={:}, hp={:}'.format(index, hp))
|
||||
|
||||
if len(ckps) == 0:
|
||||
raise ValueError('Invalid data : index={:}, hp={:}'.format(index, hp))
|
||||
|
||||
arch_info = account_one_arch(index, arch_str, ckps, datasets)
|
||||
hp2info[hp] = arch_info
|
||||
|
||||
hp2info = correct_time_related_info(hp2info)
|
||||
evaluated_indexes.add(index)
|
||||
|
||||
hp2info['01'].clear_params() # to save some spaces...
|
||||
to_save_data = OrderedDict({'01': hp2info['01'].state_dict(),
|
||||
'12': hp2info['12'].state_dict(),
|
||||
'90': hp2info['90'].state_dict()})
|
||||
torch.save(to_save_data, simplify_save_dir / '{:}-FULL.pth'.format(index))
|
||||
np.save(str(full_save_path), to_save_data)
|
||||
|
||||
for hp in hps: hp2info[hp].clear_params()
|
||||
to_save_data = OrderedDict({'01': hp2info['01'].state_dict(),
|
||||
'12': hp2info['12'].state_dict(),
|
||||
'90': hp2info['90'].state_dict()})
|
||||
torch.save(to_save_data, simplify_save_dir / '{:}-SIMPLE.pth'.format(index))
|
||||
np.save(str(simple_save_path), to_save_data)
|
||||
arch2infos[index] = to_save_data
|
||||
# measure elapsed time
|
||||
arch_time.update(time.time() - end_time)
|
||||
@ -209,8 +229,9 @@ def simplify(save_dir, save_name, nets, total):
|
||||
'total_archs': total,
|
||||
'arch2infos' : arch2infos,
|
||||
'evaluated_indexes': evaluated_indexes}
|
||||
save_file_name = save_dir / '{:}.pth'.format(save_name)
|
||||
torch.save(final_infos, save_file_name)
|
||||
save_file_name = save_dir / '{:}.npy'.format(save_name)
|
||||
np.save(str(save_file_name), final_infos)
|
||||
import pdb; pdb.set_trace()
|
||||
print ('Save {:} / {:} architecture results into {:}.'.format(len(evaluated_indexes), total, save_file_name))
|
||||
|
||||
|
||||
@ -226,17 +247,17 @@ def traverse_net(candidates: List[int], N: int):
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
parser = argparse.ArgumentParser(description='NAS-BENCH-201', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||
parser.add_argument('--base_save_dir', type=str, default='./output/NAS-BENCH-202', help='The base-name of folder to save checkpoints and log.')
|
||||
parser = argparse.ArgumentParser(description='NATS-Bench (size search space)', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||
parser.add_argument('--base_save_dir', type=str, default='./output/NATS-Bench-size', help='The base-name of folder to save checkpoints and log.')
|
||||
parser.add_argument('--candidateC' , type=int, nargs='+', default=[8, 16, 24, 32, 40, 48, 56, 64], help='.')
|
||||
parser.add_argument('--num_layers' , type=int, default=5, help='The number of layers in a network.')
|
||||
parser.add_argument('--check_N' , type=int, default=32768, help='For safety.')
|
||||
parser.add_argument('--save_name' , type=str, default='simplify', help='The save directory.')
|
||||
parser.add_argument('--save_name' , type=str, default='process', help='The save directory.')
|
||||
args = parser.parse_args()
|
||||
|
||||
nets = traverse_net(args.candidateC, args.num_layers)
|
||||
if len(nets) != args.check_N: raise ValueError('Pre-num-check failed : {:} vs {:}'.format(len(nets), args.check_N))
|
||||
if len(nets) != args.check_N:
|
||||
raise ValueError('Pre-num-check failed : {:} vs {:}'.format(len(nets), args.check_N))
|
||||
|
||||
save_dir = Path(args.base_save_dir)
|
||||
simplify(save_dir, args.save_name, nets, args.check_N)
|
||||
@ -10,10 +10,15 @@
|
||||
# History:
|
||||
# [2020.07.31] The first version, where most content reused nas_201_api/api_utils.py
|
||||
#
|
||||
import os, abc, copy, random, torch, numpy as np
|
||||
from pathlib import Path
|
||||
import abc, copy, random, numpy as np
|
||||
import importlib, warnings
|
||||
from typing import List, Text, Union, Dict, Optional
|
||||
from collections import OrderedDict, defaultdict
|
||||
USE_TORCH = importlib.find_loader('torch') is not None
|
||||
if USE_TORCH:
|
||||
import torch
|
||||
else:
|
||||
warnings.warn('Can not find PyTorch, and thus some features maybe invalid.')
|
||||
|
||||
|
||||
def remap_dataset_set_names(dataset, metric_on_set, verbose=False):
|
||||
@ -545,6 +550,8 @@ class ArchResults(object):
|
||||
def create_from_state_dict(state_dict_or_file):
|
||||
x = ArchResults(-1, -1)
|
||||
if isinstance(state_dict_or_file, str): # a file path
|
||||
if not USE_TORCH:
|
||||
raise ValueError('Since torch is not imported, this logic can not be used.')
|
||||
state_dict = torch.load(state_dict_or_file, map_location='cpu')
|
||||
elif isinstance(state_dict_or_file, dict):
|
||||
state_dict = state_dict_or_file
|
||||
|
||||
@ -3,3 +3,4 @@ from .gpu_manager import GPUManager
|
||||
from .flop_benchmark import get_model_infos, count_parameters_in_MB
|
||||
from .affine_utils import normalize_points, denormalize_points
|
||||
from .affine_utils import identity2affine, solve2theta, affine2image
|
||||
from .hash_utils import get_md5_file
|
||||
|
||||
16
lib/utils/hash_utils.py
Normal file
16
lib/utils/hash_utils.py
Normal file
@ -0,0 +1,16 @@
|
||||
import os, hashlib
|
||||
|
||||
|
||||
def get_md5_file(file_path, post_truncated=5):
|
||||
md5_hash = hashlib.md5()
|
||||
if os.path.exists(file_path):
|
||||
xfile = open(file_path, "rb")
|
||||
content = xfile.read()
|
||||
md5_hash.update(content)
|
||||
digest = md5_hash.hexdigest()
|
||||
else:
|
||||
raise ValueError('[get_md5_file] {:} does not exist'.format(file_path))
|
||||
if post_truncated is None:
|
||||
return digest
|
||||
else:
|
||||
return digest[-post_truncated:]
|
||||
Loading…
Reference in New Issue
Block a user