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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
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# NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search, ICLR 2020 #
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# The history of benchmark files:
# [2020.02.25] NAS-Bench-201-v1_0-e61699.pth : 6219 architectures are trained once, 1621 architectures are trained twice, 7785 architectures are trained three times. `LESS` only supports CIFAR10-VALID.
# [2020.03.16] NAS-Bench-201-v1_1-096897.pth : 2225 architectures are trained once, 5439 archiitectures are trained twice, 7961 architectures are trained three times on all training sets. For the hyper-parameters with the total epochs of 12, each model is trained on CIFAR-10, CIFAR-100, ImageNet16-120 once, and is trained on CIFAR-10-VALID twice.
#
# I'm still actively enhancing this benchmark. Please feel free to contact me if you have any question w.r.t. NAS-Bench-201.
#
import os, copy, random, torch, numpy as np
from pathlib import Path
from typing import List, Text, Union, Dict, Optional
from collections import OrderedDict, defaultdict
from .api_utils import ArchResults
from .api_utils import NASBenchMetaAPI
from .api_utils import remap_dataset_set_names
ALL_BENCHMARK_FILES = ['NAS-Bench-201-v1_0-e61699.pth', 'NAS-Bench-201-v1_1-096897.pth']
ALL_ARCHIVE_DIRS = ['NAS-Bench-201-v1_1-archive']
def print_information(information, extra_info=None, show=False):
dataset_names = information.get_dataset_names()
strings = [information.arch_str, 'datasets : {:}, extra-info : {:}'.format(dataset_names, extra_info)]
def metric2str(loss, acc):
return 'loss = {:.3f}, top1 = {:.2f}%'.format(loss, acc)
for ida, dataset in enumerate(dataset_names):
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')
if dataset == 'cifar10-valid':
valid_info = information.get_metrics(dataset, 'x-valid')
str2 = '{:14s} train : [{:}], valid : [{:}]'.format(dataset, metric2str(train_info['loss'], train_info['accuracy']), metric2str(valid_info['loss'], valid_info['accuracy']))
elif dataset == 'cifar10':
test__info = information.get_metrics(dataset, 'ori-test')
str2 = '{:14s} train : [{:}], test : [{:}]'.format(dataset, metric2str(train_info['loss'], train_info['accuracy']), metric2str(test__info['loss'], test__info['accuracy']))
else:
valid_info = information.get_metrics(dataset, 'x-valid')
test__info = information.get_metrics(dataset, 'x-test')
str2 = '{:14s} train : [{:}], valid : [{:}], test : [{:}]'.format(dataset, metric2str(train_info['loss'], train_info['accuracy']), metric2str(valid_info['loss'], valid_info['accuracy']), metric2str(test__info['loss'], test__info['accuracy']))
strings += [str1, str2]
if show: print('\n'.join(strings))
return strings
"""
This is the class for the API of NAS-Bench-201.
"""
class NASBench201API(NASBenchMetaAPI):
""" The initialization function that takes the dataset file path (or a dict loaded from that path) as input. """
def __init__(self, file_path_or_dict: Optional[Union[Text, Dict]]=None,
verbose: bool=True):
self.filename = None
self.reset_time()
if file_path_or_dict is None:
file_path_or_dict = os.path.join(os.environ['TORCH_HOME'], ALL_BENCHMARK_FILES[-1])
print ('Try to use the default NAS-Bench-201 path from {:}.'.format(file_path_or_dict))
if isinstance(file_path_or_dict, str) or isinstance(file_path_or_dict, Path):
file_path_or_dict = str(file_path_or_dict)
if verbose: print('try to create the NAS-Bench-201 api from {:}'.format(file_path_or_dict))
assert os.path.isfile(file_path_or_dict), 'invalid path : {:}'.format(file_path_or_dict)
self.filename = Path(file_path_or_dict).name
file_path_or_dict = torch.load(file_path_or_dict, map_location='cpu')
elif isinstance(file_path_or_dict, dict):
file_path_or_dict = copy.deepcopy(file_path_or_dict)
else: raise ValueError('invalid type : {:} not in [str, dict]'.format(type(file_path_or_dict)))
assert isinstance(file_path_or_dict, dict), 'It should be a dict instead of {:}'.format(type(file_path_or_dict))
self.verbose = verbose # [TODO] a flag indicating whether to print more logs
keys = ('meta_archs', 'arch2infos', 'evaluated_indexes')
for key in keys: assert key in file_path_or_dict, 'Can not find key[{:}] in the dict'.format(key)
self.meta_archs = copy.deepcopy( file_path_or_dict['meta_archs'] )
# This is a dict mapping each architecture to a dict, where the key is #epochs and the value is ArchResults
self.arch2infos_dict = OrderedDict()
self._avaliable_hps = set(['12', '200'])
for xkey in sorted(list(file_path_or_dict['arch2infos'].keys())):
all_info = file_path_or_dict['arch2infos'][xkey]
hp2archres = OrderedDict()
# self.arch2infos_less[xkey] = ArchResults.create_from_state_dict( all_info['less'] )
# self.arch2infos_full[xkey] = ArchResults.create_from_state_dict( all_info['full'] )
hp2archres['12'] = ArchResults.create_from_state_dict(all_info['less'])
hp2archres['200'] = ArchResults.create_from_state_dict(all_info['full'])
self.arch2infos_dict[xkey] = hp2archres
self.evaluated_indexes = sorted(list(file_path_or_dict['evaluated_indexes']))
self.archstr2index = {}
for idx, arch in enumerate(self.meta_archs):
assert arch not in self.archstr2index, 'This [{:}]-th arch {:} already in the dict ({:}).'.format(idx, arch, self.archstr2index[arch])
self.archstr2index[ arch ] = idx
def reload(self, archive_root: Text = None, index: int = None):
"""Overwrite all information of the 'index'-th architecture in the search space.
It will load its data from 'archive_root'.
"""
if archive_root is None:
archive_root = os.path.join(os.environ['TORCH_HOME'], ALL_ARCHIVE_DIRS[-1])
assert os.path.isdir(archive_root), 'invalid directory : {:}'.format(archive_root)
if index is None:
indexes = list(range(len(self)))
else:
indexes = [index]
for idx in indexes:
assert 0 <= idx < len(self.meta_archs), 'invalid index of {:}'.format(idx)
xfile_path = os.path.join(archive_root, '{:06d}-FULL.pth'.format(idx))
assert os.path.isfile(xfile_path), 'invalid data path : {:}'.format(xfile_path)
xdata = torch.load(xfile_path, map_location='cpu')
assert isinstance(xdata, dict) and 'full' in xdata and 'less' in xdata, 'invalid format of data in {:}'.format(xfile_path)
hp2archres = OrderedDict()
hp2archres['12'] = ArchResults.create_from_state_dict(xdata['less'])
hp2archres['200'] = ArchResults.create_from_state_dict(xdata['full'])
self.arch2infos_dict[idx] = hp2archres
def query_info_str_by_arch(self, arch, hp: Text='12'):
""" This function is used to query the information of a specific architecture
'arch' can be an architecture index or an architecture string
When hp=12, the hyper-parameters used to train a model are in 'configs/nas-benchmark/hyper-opts/12E.config'
When hp=200, the hyper-parameters used to train a model are in 'configs/nas-benchmark/hyper-opts/200E.config'
The difference between these three configurations are the number of training epochs.
"""
if self.verbose:
print('Call query_info_str_by_arch with arch={:} and hp={:}'.format(arch, hp))
return self._query_info_str_by_arch(arch, hp, print_information)
# obtain the metric for the `index`-th architecture
# `dataset` indicates the dataset:
# 'cifar10-valid' : using the proposed train set of CIFAR-10 as the training set
# 'cifar10' : using the proposed train+valid set of CIFAR-10 as the training set
# 'cifar100' : using the proposed train set of CIFAR-100 as the training set
# 'ImageNet16-120' : using the proposed train set of ImageNet-16-120 as the training set
# `iepoch` indicates the index of training epochs from 0 to 11/199.
# When iepoch=None, it will return the metric for the last training epoch
# When iepoch=11, it will return the metric for the 11-th training epoch (starting from 0)
# `use_12epochs_result` indicates different hyper-parameters for training
# When use_12epochs_result=True, it trains the network with 12 epochs and the LR decayed from 0.1 to 0 within 12 epochs
# When use_12epochs_result=False, it trains the network with 200 epochs and the LR decayed from 0.1 to 0 within 200 epochs
# `is_random`
# When is_random=True, the performance of a random architecture will be returned
# When is_random=False, the performanceo of all trials will be averaged.
def get_more_info(self, index, dataset, iepoch=None, hp='12', is_random=True):
if self.verbose:
print('Call the get_more_info function with index={:}, dataset={:}, iepoch={:}, hp={:}, and is_random={:}.'.format(index, dataset, iepoch, hp, is_random))
index = self.query_index_by_arch(index) # To avoid the input is a string or an instance of a arch object
if index not in self.arch2infos_dict:
raise ValueError('Did not find {:} from arch2infos_dict.'.format(index))
archresult = self.arch2infos_dict[index][str(hp)]
# if randomly select one trial, select the seed at first
if isinstance(is_random, bool) and is_random:
seeds = archresult.get_dataset_seeds(dataset)
is_random = random.choice(seeds)
# collect the training information
train_info = archresult.get_metrics(dataset, 'train', iepoch=iepoch, is_random=is_random)
total = train_info['iepoch'] + 1
xinfo = {'train-loss' : train_info['loss'],
'train-accuracy': train_info['accuracy'],
'train-per-time': train_info['all_time'] / total if train_info['all_time'] is not None else None,
'train-all-time': train_info['all_time']}
# collect the evaluation information
if dataset == 'cifar10-valid':
valid_info = archresult.get_metrics(dataset, 'x-valid', iepoch=iepoch, is_random=is_random)
try:
test_info = archresult.get_metrics(dataset, 'ori-test', iepoch=iepoch, is_random=is_random)
except:
test_info = None
valtest_info = None
else:
try: # collect results on the proposed test set
if dataset == 'cifar10':
test_info = archresult.get_metrics(dataset, 'ori-test', iepoch=iepoch, is_random=is_random)
else:
test_info = archresult.get_metrics(dataset, 'x-test', iepoch=iepoch, is_random=is_random)
except:
test_info = None
try: # collect results on the proposed validation set
valid_info = archresult.get_metrics(dataset, 'x-valid', iepoch=iepoch, is_random=is_random)
except:
valid_info = None
try:
if dataset != 'cifar10':
valtest_info = archresult.get_metrics(dataset, 'ori-test', iepoch=iepoch, is_random=is_random)
else:
valtest_info = None
except:
valtest_info = None
if valid_info is not None:
xinfo['valid-loss'] = valid_info['loss']
xinfo['valid-accuracy'] = valid_info['accuracy']
xinfo['valid-per-time'] = valid_info['all_time'] / total if valid_info['all_time'] is not None else None
xinfo['valid-all-time'] = valid_info['all_time']
if test_info is not None:
xinfo['test-loss'] = test_info['loss']
xinfo['test-accuracy'] = test_info['accuracy']
xinfo['test-per-time'] = test_info['all_time'] / total if test_info['all_time'] is not None else None
xinfo['test-all-time'] = test_info['all_time']
if valtest_info is not None:
xinfo['valtest-loss'] = valtest_info['loss']
xinfo['valtest-accuracy'] = valtest_info['accuracy']
xinfo['valtest-per-time'] = valtest_info['all_time'] / total if valtest_info['all_time'] is not None else None
xinfo['valtest-all-time'] = valtest_info['all_time']
return xinfo
def show(self, index: int = -1) -> None:
"""This function will print the information of a specific (or all) architecture(s)."""
self._show(index, print_information)
@staticmethod
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(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 )
input_infos = tuple( (op, int(IDX)) for (op, IDX) in inputs)
genotypes.append( input_infos )
return genotypes
@staticmethod
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 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