diff --git a/README.md b/README.md
index b66dfd1..ab72f57 100644
--- a/README.md
+++ b/README.md
@@ -110,6 +110,7 @@ If you find that this project helps your research, please consider citing some o
title = {Network Pruning via Transformable Architecture Search},
author = {Dong, Xuanyi and Yang, Yi},
booktitle = {Neural Information Processing Systems (NeurIPS)},
+ pages = {760--771},
year = {2019}
}
@inproceedings{dong2019one,
diff --git a/README_CN.md b/README_CN.md
index f517d0e..d09f57d 100644
--- a/README_CN.md
+++ b/README_CN.md
@@ -64,8 +64,7 @@
| NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size |
NATS-Bench.md |
-
-
+
| ... |
ENAS / REA / REINFORCE / BOHB |
Please check the original papers. |
@@ -111,6 +110,7 @@ Some methods use knowledge distillation (KD), which require pre-trained models.
author = {Dong, Xuanyi and Yang, Yi},
booktitle = {Neural Information Processing Systems (NeurIPS)},
year = {2019}
+ pages = {760--771},
}
@inproceedings{dong2019one,
title = {One-Shot Neural Architecture Search via Self-Evaluated Template Network},
diff --git a/docs/NATS-Bench.md b/docs/NATS-Bench.md
index 0df2b1a..d3ade19 100644
--- a/docs/NATS-Bench.md
+++ b/docs/NATS-Bench.md
@@ -16,11 +16,17 @@ This facilitates a much larger community of researchers to focus on developing b
## The Procedure of Creating NATS-Bench
-1, train all architecture candidate in the size search space with 90 epochs and use the random seed of `777`.
+### The Size Search Space
+
+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
```
bash ./scripts/NATS-Bench/train-shapes.sh 00000-32767 90 777
```
-The checkpoint of all candidates are located at `output/NATS-Bench-size` by default
+The checkpoint of all candidates are located at `output/NATS-Bench-size` by default.
+
+
+### The Topology Search Space
+
diff --git a/exps/NATS-Bench/main-sss.py b/exps/NATS-Bench/main-sss.py
index 78c32d1..70fdbb9 100644
--- a/exps/NATS-Bench/main-sss.py
+++ b/exps/NATS-Bench/main-sss.py
@@ -3,6 +3,16 @@
##############################################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.07 #
##############################################################################
+# This file is used to train (all) architecture candidate in the size search #
+# space in NATS-Bench (sss) with different hyper-parameters. #
+# When use mode=new, it will automatically detect whether the checkpoint of #
+# a trial exists, if so, it will skip this trial. When use mode=cover, it #
+# will ignore the (possible) existing checkpoint, run each trial, and save. #
+# (NOTE): the topology for all candidates in sss is fixed as: ######################
+# |nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|nor_conv_3x3~1|nor_conv_3x3~2| #
+###################################################################################################
+# Please use the script of scripts/NATS-Bench/train-shapes.sh to run. #
+##############################################################################
import os, sys, time, torch, argparse
from typing import List, Text, Dict, Any
from PIL import ImageFile
diff --git a/exps/NATS-Bench/main-tss.py b/exps/NATS-Bench/main-tss.py
new file mode 100644
index 0000000..24dfac7
--- /dev/null
+++ b/exps/NATS-Bench/main-tss.py
@@ -0,0 +1,323 @@
+##############################################################################
+# 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 train (all) architecture candidate in the topology #
+# search space in NATS-Bench (tss) with different hyper-parameters. #
+# When use mode=meta,
+###
+##############################################################################
+# 1, generate meta data: #
+# python ./exps/NATS-Bench/main-tss.py --mode meta #
+##############################################################################
+import os, sys, time, torch, random, argparse
+from PIL import ImageFile
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+from copy import deepcopy
+from pathlib import Path
+
+lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
+if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
+from config_utils import load_config
+from procedures import bench_evaluate_for_seed
+from procedures import get_machine_info
+from datasets import get_datasets
+from log_utils import Logger, AverageMeter, time_string, convert_secs2time
+from models import CellStructure, CellArchitectures, get_search_spaces
+
+
+def evaluate_all_datasets(arch, datasets, xpaths, splits, use_less, seed, arch_config, workers, logger):
+ machine_info, arch_config = get_machine_info(), deepcopy(arch_config)
+ all_infos = {'info': machine_info}
+ all_dataset_keys = []
+ # look all the datasets
+ for dataset, xpath, split in zip(datasets, xpaths, splits):
+ # train valid data
+ train_data, valid_data, xshape, class_num = get_datasets(dataset, xpath, -1)
+ # load the configuration
+ if dataset == 'cifar10' or dataset == 'cifar100':
+ if use_less: config_path = 'configs/nas-benchmark/LESS.config'
+ else : config_path = 'configs/nas-benchmark/CIFAR.config'
+ split_info = load_config('configs/nas-benchmark/cifar-split.txt', None, None)
+ elif dataset.startswith('ImageNet16'):
+ if use_less: config_path = 'configs/nas-benchmark/LESS.config'
+ else : config_path = 'configs/nas-benchmark/ImageNet-16.config'
+ split_info = load_config('configs/nas-benchmark/{:}-split.txt'.format(dataset), None, None)
+ else:
+ raise ValueError('invalid dataset : {:}'.format(dataset))
+ config = load_config(config_path, \
+ {'class_num': class_num,
+ 'xshape' : xshape}, \
+ logger)
+ # check whether use splited validation set
+ if bool(split):
+ assert dataset == 'cifar10'
+ ValLoaders = {'ori-test': torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, shuffle=False, num_workers=workers, pin_memory=True)}
+ 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))
+ train_data_v2 = deepcopy(train_data)
+ train_data_v2.transform = valid_data.transform
+ valid_data = train_data_v2
+ # data loader
+ 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)
+ 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)
+ ValLoaders['x-valid'] = valid_loader
+ else:
+ # data loader
+ train_loader = torch.utils.data.DataLoader(train_data, batch_size=config.batch_size, shuffle=True , num_workers=workers, pin_memory=True)
+ valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, shuffle=False, num_workers=workers, pin_memory=True)
+ if dataset == 'cifar10':
+ ValLoaders = {'ori-test': valid_loader}
+ elif dataset == 'cifar100':
+ cifar100_splits = load_config('configs/nas-benchmark/cifar100-test-split.txt', None, None)
+ ValLoaders = {'ori-test': valid_loader,
+ '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),
+ '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)
+ }
+ elif dataset == 'ImageNet16-120':
+ imagenet16_splits = load_config('configs/nas-benchmark/imagenet-16-120-test-split.txt', None, None)
+ ValLoaders = {'ori-test': valid_loader,
+ '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),
+ '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)
+ }
+ else:
+ raise ValueError('invalid dataset : {:}'.format(dataset))
+
+ dataset_key = '{:}'.format(dataset)
+ if bool(split): dataset_key = dataset_key + '-valid'
+ 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))
+ logger.log('Evaluate ||||||| {:10s} ||||||| Config={:}'.format(dataset_key, config))
+ for key, value in ValLoaders.items():
+ logger.log('Evaluate ---->>>> {:10s} with {:} batchs'.format(key, len(value)))
+ results = bench_evaluate_for_seed(arch_config, config, train_loader, ValLoaders, seed, logger)
+ all_infos[dataset_key] = results
+ all_dataset_keys.append( dataset_key )
+ all_infos['all_dataset_keys'] = all_dataset_keys
+ return all_infos
+
+
+def main(save_dir, workers, datasets, xpaths, splits, use_less, srange, arch_index, seeds, cover_mode, meta_info, arch_config):
+ assert torch.cuda.is_available(), 'CUDA is not available.'
+ torch.backends.cudnn.enabled = True
+ #torch.backends.cudnn.benchmark = True
+ torch.backends.cudnn.deterministic = True
+ torch.set_num_threads( workers )
+
+ assert len(srange) == 2 and 0 <= srange[0] <= srange[1], 'invalid srange : {:}'.format(srange)
+
+ if use_less:
+ sub_dir = Path(save_dir) / '{:06d}-{:06d}-C{:}-N{:}-LESS'.format(srange[0], srange[1], arch_config['channel'], arch_config['num_cells'])
+ else:
+ sub_dir = Path(save_dir) / '{:06d}-{:06d}-C{:}-N{:}'.format(srange[0], srange[1], arch_config['channel'], arch_config['num_cells'])
+ logger = Logger(str(sub_dir), 0, False)
+
+ all_archs = meta_info['archs']
+ assert srange[1] < meta_info['total'], 'invalid range : {:}-{:} vs. {:}'.format(srange[0], srange[1], meta_info['total'])
+ assert arch_index == -1 or srange[0] <= arch_index <= srange[1], 'invalid range : {:} vs. {:} vs. {:}'.format(srange[0], arch_index, srange[1])
+ if arch_index == -1:
+ to_evaluate_indexes = list(range(srange[0], srange[1]+1))
+ else:
+ to_evaluate_indexes = [arch_index]
+ logger.log('xargs : seeds = {:}'.format(seeds))
+ logger.log('xargs : arch_index = {:}'.format(arch_index))
+ logger.log('xargs : cover_mode = {:}'.format(cover_mode))
+ logger.log('-'*100)
+
+ 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))
+ for i, (dataset, xpath, split) in enumerate(zip(datasets, xpaths, splits)):
+ 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 {:} 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='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})
diff --git a/exps/NAS-Bench-201/xshape-collect.py b/exps/NATS-Bench/sss-collect.py
similarity index 80%
rename from exps/NAS-Bench-201/xshape-collect.py
rename to exps/NATS-Bench/sss-collect.py
index 7cd4a41..37afe15 100644
--- a/exps/NAS-Bench-201/xshape-collect.py
+++ b/exps/NATS-Bench/sss-collect.py
@@ -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)
diff --git a/lib/nats_bench/api_utils.py b/lib/nats_bench/api_utils.py
index a8312bf..2e89f9d 100644
--- a/lib/nats_bench/api_utils.py
+++ b/lib/nats_bench/api_utils.py
@@ -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
diff --git a/lib/utils/__init__.py b/lib/utils/__init__.py
index 1cc1647..86b4d37 100644
--- a/lib/utils/__init__.py
+++ b/lib/utils/__init__.py
@@ -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
diff --git a/lib/utils/hash_utils.py b/lib/utils/hash_utils.py
new file mode 100644
index 0000000..b372cac
--- /dev/null
+++ b/lib/utils/hash_utils.py
@@ -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:]