Upgrade API of NAS-Bench-201
This commit is contained in:
parent
c8f2a93ecf
commit
d783193392
@ -30,18 +30,14 @@ CUDA_VISIBLE_DEVICES=0 bash ./scripts/nas-infer-train.sh cifar100 SETN 96 -1
|
||||
CUDA_VISIBLE_DEVICES=0,1,2,3 bash ./scripts/nas-infer-train.sh imagenet-1k SETN 256 -1
|
||||
```
|
||||
|
||||
### Searching on the NASNet search space
|
||||
Please use the following scripts to use SETN to search as in the original paper:
|
||||
```
|
||||
CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/NASNet-space-search-by-SETN.sh cifar10 1 -1
|
||||
```
|
||||
|
||||
### Searching on the NAS-Bench-201 search space
|
||||
The searching codes of SETN on a small search space (NAS-Bench-201).
|
||||
```
|
||||
CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/SETN.sh cifar10 1 -1
|
||||
```
|
||||
|
||||
**Searching on the NASNet search space** is not ready yet.
|
||||
|
||||
|
||||
# Citation
|
||||
|
||||
|
@ -21,9 +21,12 @@ You can simply type `pip install nas-bench-201` to install our api. Please see s
|
||||
The benchmark file of NAS-Bench-201 can be downloaded from [Google Drive](https://drive.google.com/open?id=1SKW0Cu0u8-gb18zDpaAGi0f74UdXeGKs) or [Baidu-Wangpan (code:6u5d)](https://pan.baidu.com/s/1CiaNH6C12zuZf7q-Ilm09w).
|
||||
You can move it to anywhere you want and send its path to our API for initialization.
|
||||
- [2020.02.25] v1.0: `NAS-Bench-201-v1_0-e61699.pth`, where `e61699` is the last six digits for this file. It contains all information except for the trained weights of each trial.
|
||||
- [2020.02.25] v1.0: The full data of each architecture can be download from [Google Drive](https://drive.google.com/open?id=1X2i-JXaElsnVLuGgM4tP-yNwtsspXgdQ) (about 226GB). This compressed folder has 15625 files containing the the trained weights.
|
||||
- [2020.02.25] v1.0: The full data of each architecture can be download from [
|
||||
NAS-BENCH-201-4-v1.0-archive.tar](https://drive.google.com/open?id=1X2i-JXaElsnVLuGgM4tP-yNwtsspXgdQ) (about 226GB). This compressed folder has 15625 files containing the the trained weights.
|
||||
- [2020.02.25] v1.0: Checkpoints for 3 runs of each baseline NAS algorithm are provided in [Google Drive](https://drive.google.com/open?id=1eAgLZQAViP3r6dA0_ZOOGG9zPLXhGwXi).
|
||||
- [2020.03.08] v2.0: coming soon (results of two set of hyper-parameters avaliable on all three datasets)
|
||||
- [2020.03.09] v1.2: More robust API with more functions and descriptions
|
||||
- [2020.04.01] v2.0: coming soon (results of two set of hyper-parameters avaliable on all three datasets)
|
||||
|
||||
|
||||
The training and evaluation data used in NAS-Bench-201 can be downloaded from [Google Drive](https://drive.google.com/open?id=1L0Lzq8rWpZLPfiQGd6QR8q5xLV88emU7) or [Baidu-Wangpan (code:4fg7)](https://pan.baidu.com/s/1XAzavPKq3zcat1yBA1L2tQ).
|
||||
It is recommended to put these data into `$TORCH_HOME` (`~/.torch/` by default). If you want to generate NAS-Bench-201 or similar NAS datasets or training models by yourself, you need these data.
|
||||
|
@ -1,7 +1,7 @@
|
||||
#####################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
|
||||
#####################################################
|
||||
# python exps/NAS-Bench-201/check.py --base_save_dir
|
||||
# python exps/NAS-Bench-201/check.py --base_str C16-N5-LESS
|
||||
#####################################################
|
||||
import sys, time, argparse, collections
|
||||
import torch
|
||||
@ -13,10 +13,9 @@ from log_utils import AverageMeter, time_string, convert_secs2time
|
||||
|
||||
|
||||
def check_files(save_dir, meta_file, basestr):
|
||||
meta_infos = torch.load(meta_file, map_location='cpu')
|
||||
meta_archs = meta_infos['archs']
|
||||
meta_infos = torch.load(meta_file, map_location='cpu')
|
||||
meta_archs = meta_infos['archs']
|
||||
meta_num_archs = meta_infos['total']
|
||||
meta_max_node = meta_infos['max_node']
|
||||
assert meta_num_archs == len(meta_archs), 'invalid number of archs : {:} vs {:}'.format(meta_num_archs, len(meta_archs))
|
||||
|
||||
sub_model_dirs = sorted(list(save_dir.glob('*-*-{:}'.format(basestr))))
|
||||
@ -43,7 +42,12 @@ def check_files(save_dir, meta_file, basestr):
|
||||
dir2ckps, dir2ckp_exists = dict(), dict()
|
||||
start_time, epoch_time = time.time(), AverageMeter()
|
||||
for IDX, (sub_dir, arch_indexes) in enumerate(subdir2archs.items()):
|
||||
seeds = [777, 888, 999]
|
||||
if basestr == 'C16-N5':
|
||||
seeds = [777, 888, 999]
|
||||
elif basestr == 'C16-N5-LESS':
|
||||
seeds = [111, 777]
|
||||
else:
|
||||
raise ValueError('Invalid base str : {:}'.format(basestr))
|
||||
numrs = defaultdict(lambda: 0)
|
||||
all_checkpoints, all_ckp_exists = [], []
|
||||
for arch_index in arch_indexes:
|
||||
@ -66,17 +70,15 @@ def check_files(save_dir, meta_file, basestr):
|
||||
if __name__ == '__main__':
|
||||
|
||||
parser = argparse.ArgumentParser(description='NAS Benchmark 201', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||
parser.add_argument('--base_save_dir', type=str, default='./output/NAS-BENCH-201-4', help='The base-name of folder to save checkpoints and log.')
|
||||
parser.add_argument('--max_node', type=int, default=4, help='The maximum node in a cell.')
|
||||
parser.add_argument('--channel', type=int, default=16, help='The number of channels.')
|
||||
parser.add_argument('--num_cells', type=int, default=5, help='The number of cells in one stage.')
|
||||
parser.add_argument('--base_save_dir', type=str, default='./output/NAS-BENCH-201-4', help='The base-name of folder to save checkpoints and log.')
|
||||
parser.add_argument('--meta_path', type=str, default='./output/NAS-BENCH-201-4/meta-node-4.pth', help='The meta file path.')
|
||||
parser.add_argument('--base_str', type=str, default='C16-N5', help='The basic string.')
|
||||
args = parser.parse_args()
|
||||
|
||||
save_dir = Path( args.base_save_dir )
|
||||
meta_path = save_dir / 'meta-node-{:}.pth'.format(args.max_node)
|
||||
|
||||
save_dir = Path(args.base_save_dir)
|
||||
meta_path = Path(args.meta_path)
|
||||
assert save_dir.exists(), 'invalid save dir path : {:}'.format(save_dir)
|
||||
assert meta_path.exists(), 'invalid saved meta path : {:}'.format(meta_path)
|
||||
print ('check NAS-Bench-201 in {:}'.format(save_dir))
|
||||
|
||||
basestr = 'C{:}-N{:}'.format(args.channel, args.num_cells)
|
||||
check_files(save_dir, meta_path, basestr)
|
||||
check_files(save_dir, meta_path, args.base_str)
|
||||
|
@ -1,6 +1,7 @@
|
||||
#####################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
|
||||
#####################################################
|
||||
# [2020.03.09] Upgrade to v1.2
|
||||
import os
|
||||
from setuptools import setup
|
||||
|
||||
@ -12,7 +13,7 @@ def read(fname='README.md'):
|
||||
|
||||
setup(
|
||||
name = "nas_bench_201",
|
||||
version = "1.1",
|
||||
version = "1.2",
|
||||
author = "Xuanyi Dong",
|
||||
author_email = "dongxuanyi888@gmail.com",
|
||||
description = "API for NAS-Bench-201 (a benchmark for neural architecture search).",
|
||||
|
283
exps/NAS-Bench-201/statistics-v2.py
Normal file
283
exps/NAS-Bench-201/statistics-v2.py
Normal file
@ -0,0 +1,283 @@
|
||||
#####################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
|
||||
#####################################################
|
||||
import os, sys, time, argparse, collections
|
||||
import numpy as np
|
||||
import torch
|
||||
from pathlib import Path
|
||||
from collections import defaultdict, OrderedDict
|
||||
from typing import Dict, Any, Text, List
|
||||
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 NASBench201API, ArchResults, ResultsCount
|
||||
from procedures import bench_pure_evaluate as pure_evaluate, get_nas_bench_loaders
|
||||
|
||||
api = NASBench201API('{:}/.torch/NAS-Bench-201-v1_0-e61699.pth'.firmat(os.environ['HOME']))
|
||||
|
||||
def create_result_count(used_seed: int, dataset: Text, arch_config: Dict[Text, Any],
|
||||
results: Dict[Text, Any], dataloader_dict: Dict[Text, Any]) -> ResultsCount:
|
||||
xresult = ResultsCount(dataset, results['net_state_dict'], results['train_acc1es'], results['train_losses'],
|
||||
results['param'], results['flop'], arch_config, used_seed, results['total_epoch'], None)
|
||||
net_config = dict2config({'name': 'infer.tiny', 'C': arch_config['channel'], 'N': arch_config['num_cells'], 'genotype': CellStructure.str2structure(arch_config['arch_str']), 'num_classes':arch_config['class_num']}, None)
|
||||
network = get_cell_based_tiny_net(net_config)
|
||||
network.load_state_dict(xresult.get_net_param())
|
||||
if 'train_times' in results: # new version
|
||||
xresult.update_train_info(results['train_acc1es'], results['train_acc5es'], results['train_losses'], results['train_times'])
|
||||
xresult.update_eval(results['valid_acc1es'], results['valid_losses'], results['valid_times'])
|
||||
else:
|
||||
if dataset == 'cifar10-valid':
|
||||
xresult.update_OLD_eval('x-valid' , results['valid_acc1es'], results['valid_losses'])
|
||||
loss, top1, top5, latencies = pure_evaluate(dataloader_dict['{:}@{:}'.format('cifar10', 'test')], network.cuda())
|
||||
xresult.update_OLD_eval('ori-test', {results['total_epoch']-1: top1}, {results['total_epoch']-1: loss})
|
||||
xresult.update_latency(latencies)
|
||||
elif dataset == 'cifar10':
|
||||
xresult.update_OLD_eval('ori-test', results['valid_acc1es'], results['valid_losses'])
|
||||
loss, top1, top5, latencies = pure_evaluate(dataloader_dict['{:}@{:}'.format(dataset, 'test')], network.cuda())
|
||||
xresult.update_latency(latencies)
|
||||
elif dataset == 'cifar100' or dataset == 'ImageNet16-120':
|
||||
xresult.update_OLD_eval('ori-test', results['valid_acc1es'], results['valid_losses'])
|
||||
loss, top1, top5, latencies = pure_evaluate(dataloader_dict['{:}@{:}'.format(dataset, 'valid')], network.cuda())
|
||||
xresult.update_OLD_eval('x-valid', {results['total_epoch']-1: top1}, {results['total_epoch']-1: loss})
|
||||
loss, top1, top5, latencies = pure_evaluate(dataloader_dict['{:}@{:}'.format(dataset, 'test')], network.cuda())
|
||||
xresult.update_OLD_eval('x-test' , {results['total_epoch']-1: top1}, {results['total_epoch']-1: loss})
|
||||
xresult.update_latency(latencies)
|
||||
else:
|
||||
raise ValueError('invalid dataset name : {:}'.format(dataset))
|
||||
return xresult
|
||||
|
||||
|
||||
|
||||
def account_one_arch(arch_index: int, arch_str: Text, checkpoints: List[Text],
|
||||
datasets: List[Text], dataloader_dict: Dict[Text, Any]) -> ArchResults:
|
||||
information = ArchResults(arch_index, arch_str)
|
||||
|
||||
for checkpoint_path in checkpoints:
|
||||
checkpoint = torch.load(checkpoint_path, map_location='cpu')
|
||||
used_seed = checkpoint_path.name.split('-')[-1].split('.')[0]
|
||||
ok_dataset = 0
|
||||
for dataset in datasets:
|
||||
if dataset not in checkpoint:
|
||||
print('Can not find {:} in arch-{:} from {:}'.format(dataset, arch_index, checkpoint_path))
|
||||
continue
|
||||
else:
|
||||
ok_dataset += 1
|
||||
results = checkpoint[dataset]
|
||||
assert results['finish-train'], 'This {:} arch seed={:} does not finish train on {:} ::: {:}'.format(arch_index, used_seed, dataset, checkpoint_path)
|
||||
arch_config = {'channel': results['channel'], 'num_cells': results['num_cells'], 'arch_str': arch_str, 'class_num': results['config']['class_num']}
|
||||
|
||||
xresult = create_result_count(used_seed, dataset, arch_config, results, dataloader_dict)
|
||||
information.update(dataset, int(used_seed), xresult)
|
||||
if ok_dataset == 0: raise ValueError('{:} does not find any data'.format(checkpoint_path))
|
||||
return information
|
||||
|
||||
|
||||
def correct_time_related_info(arch_index: int, arch_info_full: ArchResults, arch_info_less: ArchResults):
|
||||
# calibrate the latency based on NAS-Bench-201-v1_0-e61699.pth
|
||||
cifar010_latency = (api.get_latency(arch_index, 'cifar10-valid', False) + api.get_latency(arch_index, 'cifar10', False)) / 2
|
||||
arch_info_full.reset_latency('cifar10-valid', None, cifar010_latency)
|
||||
arch_info_full.reset_latency('cifar10', None, cifar010_latency)
|
||||
arch_info_less.reset_latency('cifar10-valid', None, cifar010_latency)
|
||||
arch_info_less.reset_latency('cifar10', None, cifar010_latency)
|
||||
|
||||
cifar100_latency = api.get_latency(arch_index, 'cifar100', False)
|
||||
arch_info_full.reset_latency('cifar100', None, cifar100_latency)
|
||||
arch_info_less.reset_latency('cifar100', None, cifar100_latency)
|
||||
|
||||
image_latency = api.get_latency(arch_index, 'ImageNet16-120', False)
|
||||
arch_info_full.reset_latency('ImageNet16-120', None, image_latency)
|
||||
arch_info_less.reset_latency('ImageNet16-120', None, image_latency)
|
||||
|
||||
train_per_epoch_time = list(arch_info_less.query('cifar10-valid', 777).train_times.values())
|
||||
train_per_epoch_time = sum(train_per_epoch_time) / len(train_per_epoch_time)
|
||||
eval_ori_test_time, eval_x_valid_time = [], []
|
||||
for key, value in arch_info_less.query('cifar10-valid', 777).eval_times.items():
|
||||
if key.startswith('ori-test@'):
|
||||
eval_ori_test_time.append(value)
|
||||
elif key.startswith('x-valid@'):
|
||||
eval_x_valid_time.append(value)
|
||||
else: raise ValueError('-- {:} --'.format(key))
|
||||
eval_ori_test_time, eval_x_valid_time = float(np.mean(eval_ori_test_time)), float(np.mean(eval_x_valid_time))
|
||||
nums = {'ImageNet16-120-train': 151700, 'ImageNet16-120-valid': 3000, 'ImageNet16-120-test': 6000,
|
||||
'cifar10-valid-train': 25000, 'cifar10-valid-valid': 25000,
|
||||
'cifar10-train': 50000, 'cifar10-test': 10000,
|
||||
'cifar100-train': 50000, 'cifar100-test': 10000, 'cifar100-valid': 5000}
|
||||
eval_per_sample = (eval_ori_test_time + eval_x_valid_time) / (nums['cifar10-valid-valid'] + nums['cifar10-test'])
|
||||
for arch_info in [arch_info_less, arch_info_full]:
|
||||
arch_info.reset_pseudo_train_times('cifar10-valid', None,
|
||||
train_per_epoch_time / nums['cifar10-valid-train'] * nums['cifar10-valid-train'])
|
||||
arch_info.reset_pseudo_train_times('cifar10', None,
|
||||
train_per_epoch_time / nums['cifar10-valid-train'] * nums['cifar10-train'])
|
||||
arch_info.reset_pseudo_train_times('cifar100', None,
|
||||
train_per_epoch_time / nums['cifar10-valid-train'] * nums['cifar100-train'])
|
||||
arch_info.reset_pseudo_train_times('ImageNet16-120', None,
|
||||
train_per_epoch_time / nums['cifar10-valid-train'] * nums['ImageNet16-120-train'])
|
||||
arch_info.reset_pseudo_eval_times('cifar10-valid', None, 'x-valid', eval_per_sample*nums['cifar10-valid-valid'])
|
||||
arch_info.reset_pseudo_eval_times('cifar10-valid', None, 'ori-test', eval_per_sample * nums['cifar10-test'])
|
||||
arch_info.reset_pseudo_eval_times('cifar10', None, 'ori-test', eval_per_sample * nums['cifar10-test'])
|
||||
arch_info.reset_pseudo_eval_times('cifar100', None, 'x-valid', eval_per_sample * nums['cifar100-valid'])
|
||||
arch_info.reset_pseudo_eval_times('cifar100', None, 'x-test', eval_per_sample * nums['cifar100-valid'])
|
||||
arch_info.reset_pseudo_eval_times('cifar100', None, 'ori-test', eval_per_sample * nums['cifar100-test'])
|
||||
arch_info.reset_pseudo_eval_times('ImageNet16-120', None, 'x-valid', eval_per_sample * nums['ImageNet16-120-valid'])
|
||||
arch_info.reset_pseudo_eval_times('ImageNet16-120', None, 'x-test', eval_per_sample * nums['ImageNet16-120-valid'])
|
||||
arch_info.reset_pseudo_eval_times('ImageNet16-120', None, 'ori-test', eval_per_sample * nums['ImageNet16-120-test'])
|
||||
# arch_info_full.debug_test()
|
||||
# arch_info_less.debug_test()
|
||||
# import pdb; pdb.set_trace()
|
||||
return arch_info_full, arch_info_less
|
||||
|
||||
|
||||
def simplify(save_dir, meta_file, basestr, target_dir):
|
||||
meta_infos = torch.load(meta_file, map_location='cpu')
|
||||
meta_archs = meta_infos['archs'] # a list of architecture strings
|
||||
meta_num_archs = meta_infos['total']
|
||||
assert meta_num_archs == len(meta_archs), 'invalid number of archs : {:} vs {:}'.format(meta_num_archs, len(meta_archs))
|
||||
|
||||
sub_model_dirs = sorted(list(save_dir.glob('*-*-{:}'.format(basestr))))
|
||||
print ('{:} find {:} directories used to save checkpoints'.format(time_string(), len(sub_model_dirs)))
|
||||
|
||||
subdir2archs, num_evaluated_arch = collections.OrderedDict(), 0
|
||||
num_seeds = defaultdict(lambda: 0)
|
||||
for index, sub_dir in enumerate(sub_model_dirs):
|
||||
xcheckpoints = list(sub_dir.glob('arch-*-seed-*.pth'))
|
||||
arch_indexes = set()
|
||||
for checkpoint in xcheckpoints:
|
||||
temp_names = checkpoint.name.split('-')
|
||||
assert len(temp_names) == 4 and temp_names[0] == 'arch' and temp_names[2] == 'seed', 'invalid checkpoint name : {:}'.format(checkpoint.name)
|
||||
arch_indexes.add( temp_names[1] )
|
||||
subdir2archs[sub_dir] = sorted(list(arch_indexes))
|
||||
num_evaluated_arch += len(arch_indexes)
|
||||
# count number of seeds for each architecture
|
||||
for arch_index in arch_indexes:
|
||||
num_seeds[ len(list(sub_dir.glob('arch-{:}-seed-*.pth'.format(arch_index)))) ] += 1
|
||||
print('{:} There are {:5d} architectures that have been evaluated ({:} in total).'.format(time_string(), num_evaluated_arch, meta_num_archs))
|
||||
for key in sorted( list( num_seeds.keys() ) ): print ('{:} There are {:5d} architectures that are evaluated {:} times.'.format(time_string(), num_seeds[key], key))
|
||||
|
||||
dataloader_dict = get_nas_bench_loaders( 6 )
|
||||
to_save_simply = save_dir / 'simplifies'
|
||||
to_save_allarc = save_dir / 'simplifies' / 'architectures'
|
||||
if not to_save_simply.exists(): to_save_simply.mkdir(parents=True, exist_ok=True)
|
||||
if not to_save_allarc.exists(): to_save_allarc.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
assert (save_dir / target_dir) in subdir2archs, 'can not find {:}'.format(target_dir)
|
||||
arch2infos, datasets = {}, ('cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120')
|
||||
evaluated_indexes = set()
|
||||
target_full_dir = save_dir / target_dir
|
||||
target_less_dir = save_dir / '{:}-LESS'.format(target_dir)
|
||||
arch_indexes = subdir2archs[ target_full_dir ]
|
||||
num_seeds = defaultdict(lambda: 0)
|
||||
end_time = time.time()
|
||||
arch_time = AverageMeter()
|
||||
for idx, arch_index in enumerate(arch_indexes):
|
||||
checkpoints = list(target_full_dir.glob('arch-{:}-seed-*.pth'.format(arch_index)))
|
||||
ckps_less = list(target_less_dir.glob('arch-{:}-seed-*.pth'.format(arch_index)))
|
||||
# create the arch info for each architecture
|
||||
try:
|
||||
arch_info_full = account_one_arch(arch_index, meta_archs[int(arch_index)], checkpoints, datasets, dataloader_dict)
|
||||
arch_info_less = account_one_arch(arch_index, meta_archs[int(arch_index)], ckps_less, datasets, dataloader_dict)
|
||||
num_seeds[ len(checkpoints) ] += 1
|
||||
except:
|
||||
print('Loading {:} failed, : {:}'.format(arch_index, checkpoints))
|
||||
continue
|
||||
assert int(arch_index) not in evaluated_indexes, 'conflict arch-index : {:}'.format(arch_index)
|
||||
assert 0 <= int(arch_index) < len(meta_archs), 'invalid arch-index {:} (not found in meta_archs)'.format(arch_index)
|
||||
arch_info = {'full': arch_info_full, 'less': arch_info_less}
|
||||
evaluated_indexes.add(int(arch_index))
|
||||
arch2infos[int(arch_index)] = arch_info
|
||||
# to correct the latency and training_time info.
|
||||
arch_info_full, arch_info_less = correct_time_related_info(int(arch_index), arch_info_full, arch_info_less)
|
||||
to_save_data = OrderedDict(full=arch_info_full.state_dict(), less=arch_info_less.state_dict())
|
||||
torch.save(to_save_data, to_save_allarc / '{:}-FULL.pth'.format(arch_index))
|
||||
arch_info['full'].clear_params()
|
||||
arch_info['less'].clear_params()
|
||||
torch.save(to_save_data, to_save_allarc / '{:}-SIMPLE.pth'.format(arch_index))
|
||||
# measure elapsed time
|
||||
arch_time.update(time.time() - end_time)
|
||||
end_time = time.time()
|
||||
need_time = '{:}'.format( convert_secs2time(arch_time.avg * (len(arch_indexes)-idx-1), True) )
|
||||
print('{:} {:} [{:03d}/{:03d}] : {:} still need {:}'.format(time_string(), target_dir, idx, len(arch_indexes), arch_index, need_time))
|
||||
# measure time
|
||||
xstrs = ['{:}:{:03d}'.format(key, num_seeds[key]) for key in sorted( list( num_seeds.keys() ) ) ]
|
||||
print('{:} {:} done : {:}'.format(time_string(), target_dir, xstrs))
|
||||
final_infos = {'meta_archs' : meta_archs,
|
||||
'total_archs': meta_num_archs,
|
||||
'basestr' : basestr,
|
||||
'arch2infos' : arch2infos,
|
||||
'evaluated_indexes': evaluated_indexes}
|
||||
save_file_name = to_save_simply / '{:}.pth'.format(target_dir)
|
||||
torch.save(final_infos, save_file_name)
|
||||
print ('Save {:} / {:} architecture results into {:}.'.format(len(evaluated_indexes), meta_num_archs, save_file_name))
|
||||
|
||||
|
||||
def merge_all(save_dir, meta_file, basestr):
|
||||
meta_infos = torch.load(meta_file, map_location='cpu')
|
||||
meta_archs = meta_infos['archs']
|
||||
meta_num_archs = meta_infos['total']
|
||||
assert meta_num_archs == len(meta_archs), 'invalid number of archs : {:} vs {:}'.format(meta_num_archs, len(meta_archs))
|
||||
|
||||
sub_model_dirs = sorted(list(save_dir.glob('*-*-{:}'.format(basestr))))
|
||||
print ('{:} find {:} directories used to save checkpoints'.format(time_string(), len(sub_model_dirs)))
|
||||
for index, sub_dir in enumerate(sub_model_dirs):
|
||||
arch_info_files = sorted( list(sub_dir.glob('arch-*-seed-*.pth') ) )
|
||||
print ('The {:02d}/{:02d}-th directory : {:} : {:} runs.'.format(index, len(sub_model_dirs), sub_dir, len(arch_info_files)))
|
||||
|
||||
arch2infos, evaluated_indexes = dict(), set()
|
||||
for IDX, sub_dir in enumerate(sub_model_dirs):
|
||||
ckp_path = sub_dir.parent / 'simplifies' / '{:}.pth'.format(sub_dir.name)
|
||||
if ckp_path.exists():
|
||||
sub_ckps = torch.load(ckp_path, map_location='cpu')
|
||||
assert sub_ckps['total_archs'] == meta_num_archs and sub_ckps['basestr'] == basestr
|
||||
xarch2infos = sub_ckps['arch2infos']
|
||||
xevalindexs = sub_ckps['evaluated_indexes']
|
||||
for eval_index in xevalindexs:
|
||||
assert eval_index not in evaluated_indexes and eval_index not in arch2infos
|
||||
#arch2infos[eval_index] = xarch2infos[eval_index].state_dict()
|
||||
arch2infos[eval_index] = {'full': xarch2infos[eval_index]['full'].state_dict(),
|
||||
'less': xarch2infos[eval_index]['less'].state_dict()}
|
||||
evaluated_indexes.add( eval_index )
|
||||
print ('{:} [{:03d}/{:03d}] merge data from {:} with {:} models.'.format(time_string(), IDX, len(sub_model_dirs), ckp_path, len(xevalindexs)))
|
||||
else:
|
||||
raise ValueError('Can not find {:}'.format(ckp_path))
|
||||
#print ('{:} [{:03d}/{:03d}] can not find {:}, skip.'.format(time_string(), IDX, len(subdir2archs), ckp_path))
|
||||
|
||||
evaluated_indexes = sorted( list( evaluated_indexes ) )
|
||||
print ('Finally, there are {:} architectures that have been trained and evaluated.'.format(len(evaluated_indexes)))
|
||||
|
||||
to_save_simply = save_dir / 'simplifies'
|
||||
if not to_save_simply.exists(): to_save_simply.mkdir(parents=True, exist_ok=True)
|
||||
final_infos = {'meta_archs' : meta_archs,
|
||||
'total_archs': meta_num_archs,
|
||||
'arch2infos' : arch2infos,
|
||||
'evaluated_indexes': evaluated_indexes}
|
||||
save_file_name = to_save_simply / '{:}-final-infos.pth'.format(basestr)
|
||||
torch.save(final_infos, save_file_name)
|
||||
print ('Save {:} / {:} architecture results into {:}.'.format(len(evaluated_indexes), meta_num_archs, save_file_name))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
parser = argparse.ArgumentParser(description='NAS-BENCH-201', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||
parser.add_argument('--mode' , type=str, choices=['cal', 'merge'], help='The running mode for this script.')
|
||||
parser.add_argument('--base_save_dir', type=str, default='./output/NAS-BENCH-201-4', help='The base-name of folder to save checkpoints and log.')
|
||||
parser.add_argument('--target_dir' , type=str, help='The target directory.')
|
||||
parser.add_argument('--max_node' , type=int, default=4, help='The maximum node in a cell.')
|
||||
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()
|
||||
|
||||
save_dir = Path(args.base_save_dir)
|
||||
meta_path = save_dir / 'meta-node-{:}.pth'.format(args.max_node)
|
||||
assert save_dir.exists(), 'invalid save dir path : {:}'.format(save_dir)
|
||||
assert meta_path.exists(), 'invalid saved meta path : {:}'.format(meta_path)
|
||||
print ('start the statistics of our nas-benchmark from {:} using {:}.'.format(save_dir, args.target_dir))
|
||||
basestr = 'C{:}-N{:}'.format(args.channel, args.num_cells)
|
||||
|
||||
if args.mode == 'cal':
|
||||
simplify(save_dir, meta_path, basestr, args.target_dir)
|
||||
elif args.mode == 'merge':
|
||||
merge_all(save_dir, meta_path, basestr)
|
||||
else:
|
||||
raise ValueError('invalid mode : {:}'.format(args.mode))
|
@ -4,7 +4,6 @@
|
||||
import os, sys, time, argparse, collections
|
||||
from copy import deepcopy
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from pathlib import Path
|
||||
from collections import defaultdict
|
||||
lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
|
||||
@ -15,8 +14,7 @@ from datasets import get_datasets
|
||||
# NAS-Bench-201 related module or function
|
||||
from models import CellStructure, get_cell_based_tiny_net
|
||||
from nas_201_api import ArchResults, ResultsCount
|
||||
from functions import pure_evaluate
|
||||
|
||||
from procedures import bench_pure_evaluate as pure_evaluate
|
||||
|
||||
|
||||
def create_result_count(used_seed, dataset, arch_config, results, dataloader_dict):
|
||||
@ -69,7 +67,6 @@ def account_one_arch(arch_index, arch_str, checkpoints, datasets, dataloader_dic
|
||||
return information
|
||||
|
||||
|
||||
|
||||
def GET_DataLoaders(workers):
|
||||
|
||||
torch.set_num_threads(workers)
|
||||
@ -137,7 +134,6 @@ def GET_DataLoaders(workers):
|
||||
return loaders
|
||||
|
||||
|
||||
|
||||
def simplify(save_dir, meta_file, basestr, target_dir):
|
||||
meta_infos = torch.load(meta_file, map_location='cpu')
|
||||
meta_archs = meta_infos['archs'] # a list of architecture strings
|
||||
@ -221,7 +217,6 @@ def simplify(save_dir, meta_file, basestr, target_dir):
|
||||
print ('Save {:} / {:} architecture results into {:}.'.format(len(evaluated_indexes), meta_num_archs, save_file_name))
|
||||
|
||||
|
||||
|
||||
def merge_all(save_dir, meta_file, basestr):
|
||||
meta_infos = torch.load(meta_file, map_location='cpu')
|
||||
meta_archs = meta_infos['archs']
|
||||
@ -268,7 +263,6 @@ def merge_all(save_dir, meta_file, basestr):
|
||||
print ('Save {:} / {:} architecture results into {:}.'.format(len(evaluated_indexes), meta_num_archs, save_file_name))
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
parser = argparse.ArgumentParser(description='NAS-BENCH-201', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||
@ -280,7 +274,7 @@ if __name__ == '__main__':
|
||||
parser.add_argument('--num_cells' , type=int, default=5, help='The number of cells in one stage.')
|
||||
args = parser.parse_args()
|
||||
|
||||
save_dir = Path( args.base_save_dir )
|
||||
save_dir = Path(args.base_save_dir)
|
||||
meta_path = save_dir / 'meta-node-{:}.pth'.format(args.max_node)
|
||||
assert save_dir.exists(), 'invalid save dir path : {:}'.format(save_dir)
|
||||
assert meta_path.exists(), 'invalid saved meta path : {:}'.format(meta_path)
|
||||
@ -292,4 +286,4 @@ if __name__ == '__main__':
|
||||
elif args.mode == 'merge':
|
||||
merge_all(save_dir, meta_path, basestr)
|
||||
else:
|
||||
raise ValueError('invalid mode : {:}'.format(args.mode))
|
||||
raise ValueError('invalid mode : {:}'.format(args.mode))
|
@ -4,4 +4,5 @@
|
||||
from .api import NASBench201API
|
||||
from .api import ArchResults, ResultsCount
|
||||
|
||||
NAS_BENCH_201_API_VERSION="v1.1" # [2020.02.25]
|
||||
# NAS_BENCH_201_API_VERSION="v1.1" # [2020.02.25]
|
||||
NAS_BENCH_201_API_VERSION="v1.2" # [2020.03.09]
|
||||
|
@ -8,7 +8,7 @@
|
||||
#
|
||||
#
|
||||
import os, copy, random, torch, numpy as np
|
||||
from typing import List, Text, Union, Dict, Any
|
||||
from typing import List, Text, Union, Dict
|
||||
from collections import OrderedDict, defaultdict
|
||||
|
||||
|
||||
@ -19,8 +19,7 @@ def print_information(information, extra_info=None, show=False):
|
||||
return 'loss = {:.3f}, top1 = {:.2f}%'.format(loss, acc)
|
||||
|
||||
for ida, dataset in enumerate(dataset_names):
|
||||
#flop, param, latency = information.get_comput_costs(dataset)
|
||||
metric = information.get_comput_costs(dataset)
|
||||
metric = information.get_compute_costs(dataset)
|
||||
flop, param, latency = metric['flops'], metric['params'], metric['latency']
|
||||
str1 = '{:14s} FLOP={:6.2f} M, Params={:.3f} MB, latency={:} ms.'.format(dataset, flop, param, '{:.2f}'.format(latency*1000) if latency is not None and latency > 0 else None)
|
||||
train_info = information.get_metrics(dataset, 'train')
|
||||
@ -80,6 +79,7 @@ class NASBench201API(object):
|
||||
return ('{name}({num}/{total} architectures)'.format(name=self.__class__.__name__, num=len(self.evaluated_indexes), total=len(self.meta_archs)))
|
||||
|
||||
def random(self):
|
||||
"""Return a random index of all architectures."""
|
||||
return random.randint(0, len(self.meta_archs)-1)
|
||||
|
||||
# This function is used to query the index of an architecture in the search space.
|
||||
@ -166,7 +166,7 @@ class NASBench201API(object):
|
||||
else : basestr, arch2infos = '200epochs', self.arch2infos_full
|
||||
best_index, highest_accuracy = -1, None
|
||||
for i, idx in enumerate(self.evaluated_indexes):
|
||||
info = arch2infos[idx].get_comput_costs(dataset)
|
||||
info = arch2infos[idx].get_compute_costs(dataset)
|
||||
flop, param, latency = info['flops'], info['params'], info['latency']
|
||||
if FLOP_max is not None and flop > FLOP_max : continue
|
||||
if Param_max is not None and param > Param_max: continue
|
||||
@ -178,38 +178,40 @@ class NASBench201API(object):
|
||||
best_index, highest_accuracy = idx, accuracy
|
||||
return best_index, highest_accuracy
|
||||
|
||||
# return the topology structure of the `index`-th architecture
|
||||
|
||||
def arch(self, index: int):
|
||||
"""Return the topology structure of the `index`-th architecture."""
|
||||
assert 0 <= index < len(self.meta_archs), 'invalid index : {:} vs. {:}.'.format(index, len(self.meta_archs))
|
||||
return copy.deepcopy(self.meta_archs[index])
|
||||
|
||||
"""
|
||||
This function is used to obtain the trained weights of the `index`-th architecture on `dataset` with the seed of `seed`
|
||||
Args [seed]:
|
||||
-- None : return a dict containing the trained weights of all trials, where each key is a seed and its corresponding value is the weights.
|
||||
-- a interger : return the weights of a specific trial, whose seed is this interger.
|
||||
Args [use_12epochs_result]:
|
||||
-- True : train the model by 12 epochs
|
||||
-- False : train the model by 200 epochs
|
||||
"""
|
||||
def get_net_param(self, index, dataset, seed, use_12epochs_result=False):
|
||||
if use_12epochs_result: basestr, arch2infos = '12epochs' , self.arch2infos_less
|
||||
else : basestr, arch2infos = '200epochs', self.arch2infos_full
|
||||
archresult = arch2infos[index]
|
||||
return archresult.get_net_param(dataset, seed)
|
||||
"""
|
||||
This function is used to obtain the trained weights of the `index`-th architecture on `dataset` with the seed of `seed`
|
||||
Args [seed]:
|
||||
-- None : return a dict containing the trained weights of all trials, where each key is a seed and its corresponding value is the weights.
|
||||
-- a interger : return the weights of a specific trial, whose seed is this interger.
|
||||
Args [use_12epochs_result]:
|
||||
-- True : train the model by 12 epochs
|
||||
-- False : train the model by 200 epochs
|
||||
"""
|
||||
if use_12epochs_result: arch2infos = self.arch2infos_less
|
||||
else: arch2infos = self.arch2infos_full
|
||||
arch_result = arch2infos[index]
|
||||
return arch_result.get_net_param(dataset, seed)
|
||||
|
||||
"""
|
||||
This function is used to obtain the configuration for the `index`-th architecture on `dataset`.
|
||||
Args [dataset] (4 possible options):
|
||||
-- cifar10-valid : training the model on the CIFAR-10 training set.
|
||||
-- cifar10 : training the model on the CIFAR-10 training + validation set.
|
||||
-- cifar100 : training the model on the CIFAR-100 training set.
|
||||
-- ImageNet16-120 : training the model on the ImageNet16-120 training set.
|
||||
This function will return a dict.
|
||||
========= Some examlpes for using this function:
|
||||
config = api.get_net_config(128, 'cifar10')
|
||||
"""
|
||||
def get_net_config(self, index, dataset):
|
||||
|
||||
def get_net_config(self, index: int, dataset: Text):
|
||||
"""
|
||||
This function is used to obtain the configuration for the `index`-th architecture on `dataset`.
|
||||
Args [dataset] (4 possible options):
|
||||
-- cifar10-valid : training the model on the CIFAR-10 training set.
|
||||
-- cifar10 : training the model on the CIFAR-10 training + validation set.
|
||||
-- cifar100 : training the model on the CIFAR-100 training set.
|
||||
-- ImageNet16-120 : training the model on the ImageNet16-120 training set.
|
||||
This function will return a dict.
|
||||
========= Some examlpes for using this function:
|
||||
config = api.get_net_config(128, 'cifar10')
|
||||
"""
|
||||
archresult = self.arch2infos_full[index]
|
||||
all_results = archresult.query(dataset, None)
|
||||
if len(all_results) == 0: raise ValueError('can not find one valid trial for the {:}-th architecture on {:}'.format(index, dataset))
|
||||
@ -218,12 +220,25 @@ class NASBench201API(object):
|
||||
#print ('SEED [{:}] : {:}'.format(seed, result))
|
||||
raise ValueError('Impossible to reach here!')
|
||||
|
||||
# obtain the cost metric for the `index`-th architecture on a dataset
|
||||
def get_cost_info(self, index, dataset, use_12epochs_result=False):
|
||||
if use_12epochs_result: basestr, arch2infos = '12epochs' , self.arch2infos_less
|
||||
else : basestr, arch2infos = '200epochs', self.arch2infos_full
|
||||
archresult = arch2infos[index]
|
||||
return archresult.get_comput_costs(dataset)
|
||||
|
||||
def get_cost_info(self, index: int, dataset: Text, use_12epochs_result: bool = False) -> Dict[Text, float]:
|
||||
"""To obtain the cost metric for the `index`-th architecture on a dataset."""
|
||||
if use_12epochs_result: arch2infos = self.arch2infos_less
|
||||
else: arch2infos = self.arch2infos_full
|
||||
arch_result = arch2infos[index]
|
||||
return arch_result.get_compute_costs(dataset)
|
||||
|
||||
|
||||
def get_latency(self, index: int, dataset: Text, use_12epochs_result: bool = False) -> float:
|
||||
"""
|
||||
To obtain the latency of the network (by default it will return the latency with the batch size of 256).
|
||||
:param index: the index of the target architecture
|
||||
:param dataset: the dataset name (cifar10-valid, cifar10, cifar100, ImageNet16-120)
|
||||
:return: return a float value in seconds
|
||||
"""
|
||||
cost_dict = self.get_cost_info(index, dataset, use_12epochs_result)
|
||||
return cost_dict['latency']
|
||||
|
||||
|
||||
# obtain the metric for the `index`-th architecture
|
||||
# `dataset` indicates the dataset:
|
||||
@ -298,12 +313,15 @@ class NASBench201API(object):
|
||||
xifo['est-valid-accuracy'] = est_valid_info['accuracy']
|
||||
return xifo
|
||||
|
||||
"""
|
||||
This function will print the information of a specific (or all) architecture(s).
|
||||
If the index < 0: it will loop for all architectures and print their information one by one.
|
||||
else: it will print the information of the 'index'-th archiitecture.
|
||||
"""
|
||||
|
||||
def show(self, index: int = -1) -> None:
|
||||
"""
|
||||
This function will print the information of a specific (or all) architecture(s).
|
||||
|
||||
:param index: If the index < 0: it will loop for all architectures and print their information one by one.
|
||||
else: it will print the information of the 'index'-th archiitecture.
|
||||
:return: nothing
|
||||
"""
|
||||
if index < 0: # show all architectures
|
||||
print(self)
|
||||
for i, idx in enumerate(self.evaluated_indexes):
|
||||
@ -330,19 +348,27 @@ class NASBench201API(object):
|
||||
else:
|
||||
print('This index ({:}) is out of range (0~{:}).'.format(index, len(self.meta_archs)))
|
||||
|
||||
# This func shows how to read the string-based architecture encoding
|
||||
# the same as the `str2structure` func in `AutoDL-Projects/lib/models/cell_searchs/genotypes.py`
|
||||
# Usage:
|
||||
# arch = api.str2lists( '|nor_conv_1x1~0|+|none~0|none~1|+|none~0|none~1|skip_connect~2|' )
|
||||
# print ('there are {:} nodes in this arch'.format(len(arch)+1)) # arch is a list
|
||||
# for i, node in enumerate(arch):
|
||||
# print('the {:}-th node is the sum of these {:} nodes with op: {:}'.format(i+1, len(node), node))
|
||||
|
||||
@staticmethod
|
||||
def str2lists(xstr: Text) -> List[Any]:
|
||||
# assert isinstance(xstr, str), 'must take string (not {:}) as input'.format(type(xstr))
|
||||
nodestrs = xstr.split('+')
|
||||
def str2lists(arch_str: Text) -> List[tuple]:
|
||||
"""
|
||||
This function shows how to read the string-based architecture encoding.
|
||||
It is the same as the `str2structure` func in `AutoDL-Projects/lib/models/cell_searchs/genotypes.py`
|
||||
|
||||
:param
|
||||
arch_str: the input is a string indicates the architecture topology, such as
|
||||
|nor_conv_1x1~0|+|none~0|none~1|+|none~0|none~1|skip_connect~2|
|
||||
:return: a list of tuple, contains multiple (op, input_node_index) pairs.
|
||||
|
||||
:usage
|
||||
arch = api.str2lists( '|nor_conv_1x1~0|+|none~0|none~1|+|none~0|none~1|skip_connect~2|' )
|
||||
print ('there are {:} nodes in this arch'.format(len(arch)+1)) # arch is a list
|
||||
for i, node in enumerate(arch):
|
||||
print('the {:}-th node is the sum of these {:} nodes with op: {:}'.format(i+1, len(node), node))
|
||||
"""
|
||||
node_strs = arch_str.split('+')
|
||||
genotypes = []
|
||||
for i, node_str in enumerate(nodestrs):
|
||||
for i, node_str in enumerate(node_strs):
|
||||
inputs = list(filter(lambda x: x != '', node_str.split('|')))
|
||||
for xinput in inputs: assert len(xinput.split('~')) == 2, 'invalid input length : {:}'.format(xinput)
|
||||
inputs = ( xi.split('~') for xi in inputs )
|
||||
@ -350,40 +376,47 @@ class NASBench201API(object):
|
||||
genotypes.append( input_infos )
|
||||
return genotypes
|
||||
|
||||
# This func shows how to convert the string-based architecture encoding to the encoding strategy in NAS-Bench-101
|
||||
# Usage:
|
||||
# # this will return a numpy matrix (2-D np.array)
|
||||
# matrix = api.str2matrix( '|nor_conv_1x1~0|+|none~0|none~1|+|none~0|none~1|skip_connect~2|' )
|
||||
# # This matrix is 4-by-4 matrix representing a cell with 4 nodes (only the lower left triangle is useful).
|
||||
# [ [0, 0, 0, 0], # the first line represents the input (0-th) node
|
||||
# [2, 0, 0, 0], # the second line represents the 1-st node, is calculated by 2-th-op( 0-th-node )
|
||||
# [0, 0, 0, 0], # the third line represents the 2-nd node, is calculated by 0-th-op( 0-th-node ) + 0-th-op( 1-th-node )
|
||||
# [0, 0, 1, 0] ] # the fourth line represents the 3-rd node, is calculated by 0-th-op( 0-th-node ) + 0-th-op( 1-th-node ) + 1-th-op( 2-th-node )
|
||||
# In NAS-Bench-201 search space, 0-th-op is 'none', 1-th-op is 'skip_connect'
|
||||
# 2-th-op is 'nor_conv_1x1', 3-th-op is 'nor_conv_3x3', 4-th-op is 'avg_pool_3x3'.
|
||||
|
||||
@staticmethod
|
||||
def str2matrix(xstr):
|
||||
assert isinstance(xstr, str), 'must take string (not {:}) as input'.format(type(xstr))
|
||||
# this only support NAS-Bench-201 search space
|
||||
# this defination will be consistant with this line https://github.com/D-X-Y/AutoDL-Projects/blob/master/lib/models/cell_operations.py#L24
|
||||
# If a node has two input-edges from the same node, this function does not work. One edge will be overleaped.
|
||||
NAS_BENCH_201 = ['none', 'skip_connect', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3']
|
||||
nodestrs = xstr.split('+')
|
||||
num_nodes = len(nodestrs) + 1
|
||||
matrix = np.zeros((num_nodes,num_nodes))
|
||||
for i, node_str in enumerate(nodestrs):
|
||||
def str2matrix(arch_str: Text,
|
||||
search_space: List[Text] = ['none', 'skip_connect', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3']) -> np.ndarray:
|
||||
"""
|
||||
This func shows how to convert the string-based architecture encoding to the encoding strategy in NAS-Bench-101.
|
||||
|
||||
:param
|
||||
arch_str: the input is a string indicates the architecture topology, such as
|
||||
|nor_conv_1x1~0|+|none~0|none~1|+|none~0|none~1|skip_connect~2|
|
||||
search_space: a list of operation string, the default list is the search space for NAS-Bench-201
|
||||
the default value should be be consistent with this line https://github.com/D-X-Y/AutoDL-Projects/blob/master/lib/models/cell_operations.py#L24
|
||||
:return
|
||||
the numpy matrix (2-D np.ndarray) representing the DAG of this architecture topology
|
||||
:usage
|
||||
matrix = api.str2matrix( '|nor_conv_1x1~0|+|none~0|none~1|+|none~0|none~1|skip_connect~2|' )
|
||||
This matrix is 4-by-4 matrix representing a cell with 4 nodes (only the lower left triangle is useful).
|
||||
[ [0, 0, 0, 0], # the first line represents the input (0-th) node
|
||||
[2, 0, 0, 0], # the second line represents the 1-st node, is calculated by 2-th-op( 0-th-node )
|
||||
[0, 0, 0, 0], # the third line represents the 2-nd node, is calculated by 0-th-op( 0-th-node ) + 0-th-op( 1-th-node )
|
||||
[0, 0, 1, 0] ] # the fourth line represents the 3-rd node, is calculated by 0-th-op( 0-th-node ) + 0-th-op( 1-th-node ) + 1-th-op( 2-th-node )
|
||||
In NAS-Bench-201 search space, 0-th-op is 'none', 1-th-op is 'skip_connect',
|
||||
2-th-op is 'nor_conv_1x1', 3-th-op is 'nor_conv_3x3', 4-th-op is 'avg_pool_3x3'.
|
||||
:(NOTE)
|
||||
If a node has two input-edges from the same node, this function does not work. One edge will be overlapped.
|
||||
"""
|
||||
node_strs = arch_str.split('+')
|
||||
num_nodes = len(node_strs) + 1
|
||||
matrix = np.zeros((num_nodes, num_nodes))
|
||||
for i, node_str in enumerate(node_strs):
|
||||
inputs = list(filter(lambda x: x != '', node_str.split('|')))
|
||||
for xinput in inputs: assert len(xinput.split('~')) == 2, 'invalid input length : {:}'.format(xinput)
|
||||
for xi in inputs:
|
||||
op, idx = xi.split('~')
|
||||
if op not in NAS_BENCH_201: raise ValueError('this op ({:}) is not in {:}'.format(op, NAS_BENCH_201))
|
||||
op_idx, node_idx = NAS_BENCH_201.index(op), int(idx)
|
||||
if op not in search_space: raise ValueError('this op ({:}) is not in {:}'.format(op, search_space))
|
||||
op_idx, node_idx = search_space.index(op), int(idx)
|
||||
matrix[i+1, node_idx] = op_idx
|
||||
return matrix
|
||||
|
||||
|
||||
|
||||
|
||||
class ArchResults(object):
|
||||
|
||||
def __init__(self, arch_index, arch_str):
|
||||
@ -393,15 +426,15 @@ class ArchResults(object):
|
||||
self.dataset_seed = dict()
|
||||
self.clear_net_done = False
|
||||
|
||||
def get_comput_costs(self, dataset):
|
||||
def get_compute_costs(self, dataset):
|
||||
x_seeds = self.dataset_seed[dataset]
|
||||
results = [self.all_results[ (dataset, seed) ] for seed in x_seeds]
|
||||
|
||||
flops = [result.flop for result in results]
|
||||
params = [result.params for result in results]
|
||||
lantencies = [result.get_latency() for result in results]
|
||||
lantencies = [x for x in lantencies if x > 0]
|
||||
mean_latency = np.mean(lantencies) if len(lantencies) > 0 else None
|
||||
flops = [result.flop for result in results]
|
||||
params = [result.params for result in results]
|
||||
latencies = [result.get_latency() for result in results]
|
||||
latencies = [x for x in latencies if x > 0]
|
||||
mean_latency = np.mean(latencies) if len(latencies) > 0 else None
|
||||
time_infos = defaultdict(list)
|
||||
for result in results:
|
||||
time_info = result.get_times()
|
||||
@ -416,38 +449,38 @@ class ArchResults(object):
|
||||
else: info[key] = None
|
||||
return info
|
||||
|
||||
"""
|
||||
This `get_metrics` function is used to obtain obtain the loss, accuracy, etc information on a specific dataset.
|
||||
If not specify, each set refer to the proposed split in NAS-Bench-201 paper.
|
||||
If some args return None or raise error, then it is not avaliable.
|
||||
========================================
|
||||
Args [dataset] (4 possible options):
|
||||
-- cifar10-valid : training the model on the CIFAR-10 training set.
|
||||
-- cifar10 : training the model on the CIFAR-10 training + validation set.
|
||||
-- cifar100 : training the model on the CIFAR-100 training set.
|
||||
-- ImageNet16-120 : training the model on the ImageNet16-120 training set.
|
||||
Args [setname] (each dataset has different setnames):
|
||||
-- When dataset = cifar10-valid, you can use 'train', 'x-valid', 'ori-test'
|
||||
------ 'train' : the metric on the training set.
|
||||
------ 'x-valid' : the metric on the validation set.
|
||||
------ 'ori-test' : the metric on the test set.
|
||||
-- When dataset = cifar10, you can use 'train', 'ori-test'.
|
||||
------ 'train' : the metric on the training + validation set.
|
||||
------ 'ori-test' : the metric on the test set.
|
||||
-- When dataset = cifar100 or ImageNet16-120, you can use 'train', 'ori-test', 'x-valid', 'x-test'
|
||||
------ 'train' : the metric on the training set.
|
||||
------ 'x-valid' : the metric on the validation set.
|
||||
------ 'x-test' : the metric on the test set.
|
||||
------ 'ori-test' : the metric on the validation + test set.
|
||||
Args [iepoch] (None or an integer in [0, the-number-of-total-training-epochs)
|
||||
------ None : return the metric after the last training epoch.
|
||||
------ an integer i : return the metric after the i-th training epoch.
|
||||
Args [is_random]:
|
||||
------ True : return the metric of a randomly selected trial.
|
||||
------ False : return the averaged metric of all avaliable trials.
|
||||
------ an integer indicating the 'seed' value : return the metric of a specific trial (whose random seed is 'is_random').
|
||||
"""
|
||||
def get_metrics(self, dataset, setname, iepoch=None, is_random=False):
|
||||
"""
|
||||
This `get_metrics` function is used to obtain obtain the loss, accuracy, etc information on a specific dataset.
|
||||
If not specify, each set refer to the proposed split in NAS-Bench-201 paper.
|
||||
If some args return None or raise error, then it is not avaliable.
|
||||
========================================
|
||||
Args [dataset] (4 possible options):
|
||||
-- cifar10-valid : training the model on the CIFAR-10 training set.
|
||||
-- cifar10 : training the model on the CIFAR-10 training + validation set.
|
||||
-- cifar100 : training the model on the CIFAR-100 training set.
|
||||
-- ImageNet16-120 : training the model on the ImageNet16-120 training set.
|
||||
Args [setname] (each dataset has different setnames):
|
||||
-- When dataset = cifar10-valid, you can use 'train', 'x-valid', 'ori-test'
|
||||
------ 'train' : the metric on the training set.
|
||||
------ 'x-valid' : the metric on the validation set.
|
||||
------ 'ori-test' : the metric on the test set.
|
||||
-- When dataset = cifar10, you can use 'train', 'ori-test'.
|
||||
------ 'train' : the metric on the training + validation set.
|
||||
------ 'ori-test' : the metric on the test set.
|
||||
-- When dataset = cifar100 or ImageNet16-120, you can use 'train', 'ori-test', 'x-valid', 'x-test'
|
||||
------ 'train' : the metric on the training set.
|
||||
------ 'x-valid' : the metric on the validation set.
|
||||
------ 'x-test' : the metric on the test set.
|
||||
------ 'ori-test' : the metric on the validation + test set.
|
||||
Args [iepoch] (None or an integer in [0, the-number-of-total-training-epochs)
|
||||
------ None : return the metric after the last training epoch.
|
||||
------ an integer i : return the metric after the i-th training epoch.
|
||||
Args [is_random]:
|
||||
------ True : return the metric of a randomly selected trial.
|
||||
------ False : return the averaged metric of all avaliable trials.
|
||||
------ an integer indicating the 'seed' value : return the metric of a specific trial (whose random seed is 'is_random').
|
||||
"""
|
||||
x_seeds = self.dataset_seed[dataset]
|
||||
results = [self.all_results[ (dataset, seed) ] for seed in x_seeds]
|
||||
infos = defaultdict(list)
|
||||
@ -483,20 +516,55 @@ class ArchResults(object):
|
||||
def get_dataset_seeds(self, dataset):
|
||||
return copy.deepcopy( self.dataset_seed[dataset] )
|
||||
|
||||
"""
|
||||
This function will return the trained network's weights on the 'dataset'.
|
||||
When the 'seed' is None, it will return the weights for every run trial in the form of a dict.
|
||||
When the
|
||||
"""
|
||||
def get_net_param(self, dataset, seed=None):
|
||||
def get_net_param(self, dataset: Text, seed: Union[None, int] =None):
|
||||
"""
|
||||
This function will return the trained network's weights on the 'dataset'.
|
||||
:arg
|
||||
dataset: one of 'cifar10-valid', 'cifar10', 'cifar100', and 'ImageNet16-120'.
|
||||
seed: an integer indicates the seed value or None that indicates returing all trials.
|
||||
"""
|
||||
if seed is None:
|
||||
x_seeds = self.dataset_seed[dataset]
|
||||
return {seed: self.all_results[(dataset, seed)].get_net_param() for seed in x_seeds}
|
||||
else:
|
||||
return self.all_results[(dataset, seed)].get_net_param()
|
||||
|
||||
# get the total number of training epochs
|
||||
def reset_latency(self, dataset: Text, seed: Union[None, Text], latency: float) -> None:
|
||||
"""This function is used to reset the latency in all corresponding ResultsCount(s)."""
|
||||
if seed is None:
|
||||
for seed in self.dataset_seed[dataset]:
|
||||
self.all_results[(dataset, seed)].update_latency([latency])
|
||||
else:
|
||||
self.all_results[(dataset, seed)].update_latency([latency])
|
||||
|
||||
def reset_pseudo_train_times(self, dataset: Text, seed: Union[None, Text], estimated_per_epoch_time: float) -> None:
|
||||
"""This function is used to reset the train-times in all corresponding ResultsCount(s)."""
|
||||
if seed is None:
|
||||
for seed in self.dataset_seed[dataset]:
|
||||
self.all_results[(dataset, seed)].reset_pseudo_train_times(estimated_per_epoch_time)
|
||||
else:
|
||||
self.all_results[(dataset, seed)].reset_pseudo_train_times(estimated_per_epoch_time)
|
||||
|
||||
def reset_pseudo_eval_times(self, dataset: Text, seed: Union[None, Text], eval_name: Text, estimated_per_epoch_time: float) -> None:
|
||||
"""This function is used to reset the eval-times in all corresponding ResultsCount(s)."""
|
||||
if seed is None:
|
||||
for seed in self.dataset_seed[dataset]:
|
||||
self.all_results[(dataset, seed)].reset_pseudo_eval_times(eval_name, estimated_per_epoch_time)
|
||||
else:
|
||||
self.all_results[(dataset, seed)].reset_pseudo_eval_times(eval_name, estimated_per_epoch_time)
|
||||
|
||||
def get_latency(self, dataset: Text) -> float:
|
||||
"""Get the latency of a model on the target dataset. [Timestamp: 2020.03.09]"""
|
||||
latencies = []
|
||||
for seed in self.dataset_seed[dataset]:
|
||||
latency = self.all_results[(dataset, seed)].get_latency()
|
||||
if not isinstance(latency, float) or latency <= 0:
|
||||
raise ValueError('invalid latency of {:} for {:} with {:}'.format(dataset))
|
||||
latencies.append(latency)
|
||||
return sum(latencies) / len(latencies)
|
||||
|
||||
def get_total_epoch(self, dataset=None):
|
||||
"""Return the total number of training epochs."""
|
||||
if dataset is None:
|
||||
epochss = []
|
||||
for xdata, x_seeds in self.dataset_seed.items():
|
||||
@ -509,13 +577,13 @@ class ArchResults(object):
|
||||
if len(set(epochss)) > 1: raise ValueError('Each trial mush have the same number of training epochs : {:}'.format(epochss))
|
||||
return epochss[-1]
|
||||
|
||||
# return the ResultsCount object (containing all information of a single trial) for 'dataset' and 'seed'
|
||||
def query(self, dataset, seed=None):
|
||||
"""Return the ResultsCount object (containing all information of a single trial) for 'dataset' and 'seed'"""
|
||||
if seed is None:
|
||||
x_seeds = self.dataset_seed[dataset]
|
||||
return {seed: self.all_results[ (dataset, seed) ] for seed in x_seeds}
|
||||
return {seed: self.all_results[(dataset, seed)] for seed in x_seeds}
|
||||
else:
|
||||
return self.all_results[ (dataset, seed) ]
|
||||
return self.all_results[(dataset, seed)]
|
||||
|
||||
def arch_idx_str(self):
|
||||
return '{:06d}'.format(self.arch_index)
|
||||
@ -573,7 +641,18 @@ class ArchResults(object):
|
||||
def clear_params(self):
|
||||
for key, result in self.all_results.items():
|
||||
result.net_state_dict = None
|
||||
self.clear_net_done = True
|
||||
self.clear_net_done = True
|
||||
|
||||
def debug_test(self):
|
||||
"""This function is used for me to debug and test, which will call most methods."""
|
||||
all_dataset = ['cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120']
|
||||
for dataset in all_dataset:
|
||||
print('---->>>> {:}'.format(dataset))
|
||||
print('The latency on {:} is {:} s'.format(dataset, self.get_latency(dataset)))
|
||||
for seed in self.dataset_seed[dataset]:
|
||||
result = self.all_results[(dataset, seed)]
|
||||
print(' ==>> result = {:}'.format(result))
|
||||
print(' ==>> cost = {:}'.format(result.get_times()))
|
||||
|
||||
def __repr__(self):
|
||||
return ('{name}(arch-index={index}, arch={arch}, {num} runs, clear={clear})'.format(name=self.__class__.__name__, index=self.arch_index, arch=self.arch_str, num=len(self.all_results), clear=self.clear_net_done))
|
||||
@ -603,12 +682,25 @@ class ResultsCount(object):
|
||||
# evaluation results
|
||||
self.reset_eval()
|
||||
|
||||
def update_train_info(self, train_acc1es, train_acc5es, train_losses, train_times):
|
||||
def update_train_info(self, train_acc1es, train_acc5es, train_losses, train_times) -> None:
|
||||
self.train_acc1es = train_acc1es
|
||||
self.train_acc5es = train_acc5es
|
||||
self.train_losses = train_losses
|
||||
self.train_times = train_times
|
||||
|
||||
def reset_pseudo_train_times(self, estimated_per_epoch_time: float) -> None:
|
||||
"""Assign the training times."""
|
||||
train_times = OrderedDict()
|
||||
for i in range(self.epochs):
|
||||
train_times[i] = estimated_per_epoch_time
|
||||
self.train_times = train_times
|
||||
|
||||
def reset_pseudo_eval_times(self, eval_name: Text, estimated_per_epoch_time: float) -> None:
|
||||
"""Assign the evaluation times."""
|
||||
if eval_name not in self.eval_names: raise ValueError('invalid eval name : {:}'.format(eval_name))
|
||||
for i in range(self.epochs):
|
||||
self.eval_times['{:}@{:}'.format(eval_name,i)] = estimated_per_epoch_time
|
||||
|
||||
def reset_eval(self):
|
||||
self.eval_names = []
|
||||
self.eval_acc1es = {}
|
||||
@ -618,6 +710,11 @@ class ResultsCount(object):
|
||||
def update_latency(self, latency):
|
||||
self.latency = copy.deepcopy( latency )
|
||||
|
||||
def get_latency(self) -> float:
|
||||
"""Return the latency value in seconds. -1 represents not avaliable ; otherwise it should be a float value"""
|
||||
if self.latency is None: return -1.0
|
||||
else: return sum(self.latency) / len(self.latency)
|
||||
|
||||
def update_eval(self, accs, losses, times): # new version
|
||||
data_names = set([x.split('@')[0] for x in accs.keys()])
|
||||
for data_name in data_names:
|
||||
@ -642,28 +739,22 @@ class ResultsCount(object):
|
||||
set_name = '[' + ', '.join(self.eval_names) + ']'
|
||||
return ('{name}({xname}, arch={arch}, FLOP={flop:.2f}M, Param={param:.3f}MB, seed={seed}, {num_eval} eval-sets: {set_name})'.format(name=self.__class__.__name__, xname=self.name, arch=self.arch_config['arch_str'], flop=self.flop, param=self.params, seed=self.seed, num_eval=num_eval, set_name=set_name))
|
||||
|
||||
# get the total number of training epochs
|
||||
def get_total_epoch(self):
|
||||
return copy.deepcopy(self.epochs)
|
||||
|
||||
# get the latency
|
||||
# -1 represents not avaliable ; otherwise it should be a float value
|
||||
def get_latency(self):
|
||||
if self.latency is None: return -1
|
||||
else: return sum(self.latency) / len(self.latency)
|
||||
|
||||
# get the information regarding time
|
||||
def get_times(self):
|
||||
"""Obtain the information regarding both training and evaluation time."""
|
||||
if self.train_times is not None and isinstance(self.train_times, dict):
|
||||
train_times = list( self.train_times.values() )
|
||||
time_info = {'T-train@epoch': np.mean(train_times), 'T-train@total': np.sum(train_times)}
|
||||
for name in self.eval_names:
|
||||
else:
|
||||
time_info = {'T-train@epoch': None, 'T-train@total': None }
|
||||
for name in self.eval_names:
|
||||
try:
|
||||
xtimes = [self.eval_times['{:}@{:}'.format(name,i)] for i in range(self.epochs)]
|
||||
time_info['T-{:}@epoch'.format(name)] = np.mean(xtimes)
|
||||
time_info['T-{:}@total'.format(name)] = np.sum(xtimes)
|
||||
else:
|
||||
time_info = {'T-train@epoch': None, 'T-train@total': None }
|
||||
for name in self.eval_names:
|
||||
except:
|
||||
time_info['T-{:}@epoch'.format(name)] = None
|
||||
time_info['T-{:}@total'.format(name)] = None
|
||||
return time_info
|
||||
@ -699,18 +790,19 @@ class ResultsCount(object):
|
||||
'cur_time': xtime,
|
||||
'all_time': atime}
|
||||
|
||||
def get_net_param(self):
|
||||
return self.net_state_dict
|
||||
def get_net_param(self, clone=False):
|
||||
if clone: return copy.deepcopy(self.net_state_dict)
|
||||
else: return self.net_state_dict
|
||||
|
||||
# This function is used to obtain the config dict for this architecture.
|
||||
def get_config(self, str2structure):
|
||||
if str2structure is None:
|
||||
return {'name': 'infer.tiny', 'C': self.arch_config['channel'], \
|
||||
'N' : self.arch_config['num_cells'], \
|
||||
return {'name': 'infer.tiny', 'C': self.arch_config['channel'],
|
||||
'N' : self.arch_config['num_cells'],
|
||||
'arch_str': self.arch_config['arch_str'], 'num_classes': self.arch_config['class_num']}
|
||||
else:
|
||||
return {'name': 'infer.tiny', 'C': self.arch_config['channel'], \
|
||||
'N' : self.arch_config['num_cells'], \
|
||||
return {'name': 'infer.tiny', 'C': self.arch_config['channel'],
|
||||
'N' : self.arch_config['num_cells'],
|
||||
'genotype': str2structure(self.arch_config['arch_str']), 'num_classes': self.arch_config['class_num']}
|
||||
|
||||
def state_dict(self):
|
||||
|
@ -5,6 +5,7 @@ from .starts import prepare_seed, prepare_logger, get_machine_info, save_che
|
||||
from .optimizers import get_optim_scheduler
|
||||
from .funcs_nasbench import evaluate_for_seed as bench_evaluate_for_seed
|
||||
from .funcs_nasbench import pure_evaluate as bench_pure_evaluate
|
||||
from .funcs_nasbench import get_nas_bench_loaders
|
||||
|
||||
def get_procedures(procedure):
|
||||
from .basic_main import basic_train, basic_valid
|
||||
|
@ -1,14 +1,17 @@
|
||||
#####################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
|
||||
#####################################################
|
||||
import time, torch
|
||||
import os, time, copy, torch, pathlib
|
||||
|
||||
import datasets
|
||||
from config_utils import load_config
|
||||
from procedures import prepare_seed, get_optim_scheduler
|
||||
from utils import get_model_infos, obtain_accuracy
|
||||
from log_utils import AverageMeter, time_string, convert_secs2time
|
||||
from models import get_cell_based_tiny_net
|
||||
|
||||
|
||||
__all__ = ['evaluate_for_seed', 'pure_evaluate']
|
||||
__all__ = ['evaluate_for_seed', 'pure_evaluate', 'get_nas_bench_loaders']
|
||||
|
||||
|
||||
def pure_evaluate(xloader, network, criterion=torch.nn.CrossEntropyLoss()):
|
||||
@ -127,3 +130,72 @@ def evaluate_for_seed(arch_config, opt_config, train_loader, valid_loaders, seed
|
||||
'finish-train': True
|
||||
}
|
||||
return info_seed
|
||||
|
||||
|
||||
def get_nas_bench_loaders(workers):
|
||||
|
||||
torch.set_num_threads(workers)
|
||||
|
||||
root_dir = (pathlib.Path(__file__).parent / '..' / '..').resolve()
|
||||
torch_dir = pathlib.Path(os.environ['TORCH_HOME'])
|
||||
# cifar
|
||||
cifar_config_path = root_dir / 'configs' / 'nas-benchmark' / 'CIFAR.config'
|
||||
cifar_config = load_config(cifar_config_path, None, None)
|
||||
get_datasets = datasets.get_datasets # a function to return the dataset
|
||||
break_line = '-' * 150
|
||||
print ('{:} Create data-loader for all datasets'.format(time_string()))
|
||||
print (break_line)
|
||||
TRAIN_CIFAR10, VALID_CIFAR10, xshape, class_num = get_datasets('cifar10', str(torch_dir/'cifar.python'), -1)
|
||||
print ('original CIFAR-10 : {:} training images and {:} test images : {:} input shape : {:} number of classes'.format(len(TRAIN_CIFAR10), len(VALID_CIFAR10), xshape, class_num))
|
||||
cifar10_splits = load_config(root_dir / 'configs' / 'nas-benchmark' / 'cifar-split.txt', None, None)
|
||||
assert cifar10_splits.train[:10] == [0, 5, 7, 11, 13, 15, 16, 17, 20, 24] and cifar10_splits.valid[:10] == [1, 2, 3, 4, 6, 8, 9, 10, 12, 14]
|
||||
temp_dataset = copy.deepcopy(TRAIN_CIFAR10)
|
||||
temp_dataset.transform = VALID_CIFAR10.transform
|
||||
# data loader
|
||||
trainval_cifar10_loader = torch.utils.data.DataLoader(TRAIN_CIFAR10, batch_size=cifar_config.batch_size, shuffle=True , num_workers=workers, pin_memory=True)
|
||||
train_cifar10_loader = torch.utils.data.DataLoader(TRAIN_CIFAR10, batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar10_splits.train), num_workers=workers, pin_memory=True)
|
||||
valid_cifar10_loader = torch.utils.data.DataLoader(temp_dataset , batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar10_splits.valid), num_workers=workers, pin_memory=True)
|
||||
test__cifar10_loader = torch.utils.data.DataLoader(VALID_CIFAR10, batch_size=cifar_config.batch_size, shuffle=False, num_workers=workers, pin_memory=True)
|
||||
print ('CIFAR-10 : trval-loader has {:3d} batch with {:} per batch'.format(len(trainval_cifar10_loader), cifar_config.batch_size))
|
||||
print ('CIFAR-10 : train-loader has {:3d} batch with {:} per batch'.format(len(train_cifar10_loader), cifar_config.batch_size))
|
||||
print ('CIFAR-10 : valid-loader has {:3d} batch with {:} per batch'.format(len(valid_cifar10_loader), cifar_config.batch_size))
|
||||
print ('CIFAR-10 : test--loader has {:3d} batch with {:} per batch'.format(len(test__cifar10_loader), cifar_config.batch_size))
|
||||
print (break_line)
|
||||
# CIFAR-100
|
||||
TRAIN_CIFAR100, VALID_CIFAR100, xshape, class_num = get_datasets('cifar100', str(torch_dir/'cifar.python'), -1)
|
||||
print ('original CIFAR-100: {:} training images and {:} test images : {:} input shape : {:} number of classes'.format(len(TRAIN_CIFAR100), len(VALID_CIFAR100), xshape, class_num))
|
||||
cifar100_splits = load_config(root_dir / 'configs' / 'nas-benchmark' / 'cifar100-test-split.txt', None, None)
|
||||
assert cifar100_splits.xvalid[:10] == [1, 3, 4, 5, 8, 10, 13, 14, 15, 16] and cifar100_splits.xtest[:10] == [0, 2, 6, 7, 9, 11, 12, 17, 20, 24]
|
||||
train_cifar100_loader = torch.utils.data.DataLoader(TRAIN_CIFAR100, batch_size=cifar_config.batch_size, shuffle=True, num_workers=workers, pin_memory=True)
|
||||
valid_cifar100_loader = torch.utils.data.DataLoader(VALID_CIFAR100, batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xvalid), num_workers=workers, pin_memory=True)
|
||||
test__cifar100_loader = torch.utils.data.DataLoader(VALID_CIFAR100, batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xtest) , num_workers=workers, pin_memory=True)
|
||||
print ('CIFAR-100 : train-loader has {:3d} batch'.format(len(train_cifar100_loader)))
|
||||
print ('CIFAR-100 : valid-loader has {:3d} batch'.format(len(valid_cifar100_loader)))
|
||||
print ('CIFAR-100 : test--loader has {:3d} batch'.format(len(test__cifar100_loader)))
|
||||
print (break_line)
|
||||
|
||||
imagenet16_config_path = 'configs/nas-benchmark/ImageNet-16.config'
|
||||
imagenet16_config = load_config(imagenet16_config_path, None, None)
|
||||
TRAIN_ImageNet16_120, VALID_ImageNet16_120, xshape, class_num = get_datasets('ImageNet16-120', str(torch_dir/'cifar.python'/'ImageNet16'), -1)
|
||||
print ('original TRAIN_ImageNet16_120: {:} training images and {:} test images : {:} input shape : {:} number of classes'.format(len(TRAIN_ImageNet16_120), len(VALID_ImageNet16_120), xshape, class_num))
|
||||
imagenet_splits = load_config(root_dir / 'configs' / 'nas-benchmark' / 'imagenet-16-120-test-split.txt', None, None)
|
||||
assert imagenet_splits.xvalid[:10] == [1, 2, 3, 6, 7, 8, 9, 12, 16, 18] and imagenet_splits.xtest[:10] == [0, 4, 5, 10, 11, 13, 14, 15, 17, 20]
|
||||
train_imagenet_loader = torch.utils.data.DataLoader(TRAIN_ImageNet16_120, batch_size=imagenet16_config.batch_size, shuffle=True, num_workers=workers, pin_memory=True)
|
||||
valid_imagenet_loader = torch.utils.data.DataLoader(VALID_ImageNet16_120, batch_size=imagenet16_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet_splits.xvalid), num_workers=workers, pin_memory=True)
|
||||
test__imagenet_loader = torch.utils.data.DataLoader(VALID_ImageNet16_120, batch_size=imagenet16_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet_splits.xtest) , num_workers=workers, pin_memory=True)
|
||||
print ('ImageNet-16-120 : train-loader has {:3d} batch with {:} per batch'.format(len(train_imagenet_loader), imagenet16_config.batch_size))
|
||||
print ('ImageNet-16-120 : valid-loader has {:3d} batch with {:} per batch'.format(len(valid_imagenet_loader), imagenet16_config.batch_size))
|
||||
print ('ImageNet-16-120 : test--loader has {:3d} batch with {:} per batch'.format(len(test__imagenet_loader), imagenet16_config.batch_size))
|
||||
|
||||
# 'cifar10', 'cifar100', 'ImageNet16-120'
|
||||
loaders = {'cifar10@trainval': trainval_cifar10_loader,
|
||||
'cifar10@train' : train_cifar10_loader,
|
||||
'cifar10@valid' : valid_cifar10_loader,
|
||||
'cifar10@test' : test__cifar10_loader,
|
||||
'cifar100@train' : train_cifar100_loader,
|
||||
'cifar100@valid' : valid_cifar100_loader,
|
||||
'cifar100@test' : test__cifar100_loader,
|
||||
'ImageNet16-120@train': train_imagenet_loader,
|
||||
'ImageNet16-120@valid': valid_imagenet_loader,
|
||||
'ImageNet16-120@test' : test__imagenet_loader}
|
||||
return loaders
|
Loading…
Reference in New Issue
Block a user