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Upgrade NAS-API to v2.0:

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we use an abstract class NASBenchMetaAPI to define the spec of an API; it can be inherited to support different kinds of NAS API, while keep the query interface the same.
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D-X-Y
2020-06-30 09:05:38 +00:00
parent 91ee265bd2
commit 6effb6f127
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lib/nas_201_api/__init__.py
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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
#####################################################
from .api import NASBench201API
from .api import ArchResults, ResultsCount
from .api_utils import ArchResults, ResultsCount
from .api_201 import NASBench201API
from .api_301 import NASBench301API
# NAS_BENCH_201_API_VERSION="v1.1" # [2020.02.25]
# NAS_BENCH_201_API_VERSION="v1.2" # [2020.03.09]
NAS_BENCH_201_API_VERSION="v1.3" # [2020.03.16]
# NAS_BENCH_201_API_VERSION="v1.3" # [2020.03.16]
NAS_BENCH_201_API_VERSION="v2.0" # [2020.06.30]

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lib/nas_201_api/api.py
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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
############################################################################################
# NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search, ICLR 2020 #
############################################################################################
# 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
from collections import OrderedDict, defaultdict
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 API of NAS-Bench-201.
"""
class NASBench201API(object):
""" 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: Union[Text, Dict], verbose: bool=True):
self.filename = None
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'] )
self.arch2infos_less = OrderedDict()
self.arch2infos_full = OrderedDict()
for xkey in sorted(list(file_path_or_dict['arch2infos'].keys())):
all_info = file_path_or_dict['arch2infos'][xkey]
self.arch2infos_less[xkey] = ArchResults.create_from_state_dict( all_info['less'] )
self.arch2infos_full[xkey] = ArchResults.create_from_state_dict( all_info['full'] )
self.evaluated_indexes = sorted(list(file_path_or_dict['evaluated_indexes']))
self.archstr2index = {}
for idx, arch in enumerate(self.meta_archs):
#assert arch.tostr() not in self.archstr2index, 'This [{:}]-th arch {:} already in the dict ({:}).'.format(idx, arch, self.archstr2index[arch.tostr()])
assert arch not in self.archstr2index, 'This [{:}]-th arch {:} already in the dict ({:}).'.format(idx, arch, self.archstr2index[arch])
self.archstr2index[ arch ] = idx
def __getitem__(self, index: int):
return copy.deepcopy( self.meta_archs[index] )
def __len__(self):
return len(self.meta_archs)
def __repr__(self):
return ('{name}({num}/{total} architectures, file={filename})'.format(name=self.__class__.__name__, num=len(self.evaluated_indexes), total=len(self.meta_archs), filename=self.filename))
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.
# The input arch can be an architecture string such as '|nor_conv_3x3~0|+|nor_conv_3x3~0|avg_pool_3x3~1|+|skip_connect~0|nor_conv_3x3~1|skip_connect~2|'
# or an instance that has the 'tostr' function that can generate the architecture string.
# This function will return the index.
# If return -1, it means this architecture is not in the search space.
# Otherwise, it will return an int in [0, the-number-of-candidates-in-the-search-space).
def query_index_by_arch(self, arch):
if isinstance(arch, str):
if arch in self.archstr2index: arch_index = self.archstr2index[ arch ]
else : arch_index = -1
elif hasattr(arch, 'tostr'):
if arch.tostr() in self.archstr2index: arch_index = self.archstr2index[ arch.tostr() ]
else : arch_index = -1
else: arch_index = -1
return arch_index
def reload(self, archive_root: Text, index: int):
"""Overwrite all information of the 'index'-th architecture in the search space.
It will load its data from 'archive_root'.
"""
assert os.path.isdir(archive_root), 'invalid directory : {:}'.format(archive_root)
xfile_path = os.path.join(archive_root, '{:06d}-FULL.pth'.format(index))
assert 0 <= index < len(self.meta_archs), 'invalid index of {:}'.format(index)
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)
if index in self.arch2infos_less: del self.arch2infos_less[index]
if index in self.arch2infos_full: del self.arch2infos_full[index]
self.arch2infos_less[index] = ArchResults.create_from_state_dict( xdata['less'] )
self.arch2infos_full[index] = ArchResults.create_from_state_dict( xdata['full'] )
def clear_params(self, index: int, use_12epochs_result: Union[bool, None]):
"""Remove the architecture's weights to save memory.
:arg
index: the index of the target architecture
use_12epochs_result: a flag to controll how to clear the parameters.
-- None: clear all the weights in both `less` and `full`, which indicates the training hyper-parameters.
-- True: clear all the weights in arch2infos_less, which by default is 12-epoch-training result.
-- False: clear all the weights in arch2infos_full, which by default is 200-epoch-training result.
"""
if use_12epochs_result is None:
self.arch2infos_less[index].clear_params()
self.arch2infos_full[index].clear_params()
else:
if use_12epochs_result: arch2infos = self.arch2infos_less
else : arch2infos = self.arch2infos_full
arch2infos[index].clear_params()
# This function is used to query the information of a specific archiitecture
# 'arch' can be an architecture index or an architecture string
# When use_12epochs_result=True, the hyper-parameters used to train a model are in 'configs/nas-benchmark/CIFAR.config'
# When use_12epochs_result=False, the hyper-parameters used to train a model are in 'configs/nas-benchmark/LESS.config'
# The difference between these two configurations are the number of training epochs, which is 200 in CIFAR.config and 12 in LESS.config.
def query_by_arch(self, arch, use_12epochs_result=False):
if isinstance(arch, int):
arch_index = arch
else:
arch_index = self.query_index_by_arch(arch)
if arch_index == -1: return None # the following two lines are used to support few training epochs
if use_12epochs_result: arch2infos = self.arch2infos_less
else : arch2infos = self.arch2infos_full
if arch_index in arch2infos:
strings = print_information(arch2infos[ arch_index ], 'arch-index={:}'.format(arch_index))
return '\n'.join(strings)
else:
print ('Find this arch-index : {:}, but this arch is not evaluated.'.format(arch_index))
return None
# This 'query_by_index' function is used to query information with the training of 12 epochs or 200 epochs.
# ------
# If use_12epochs_result=True, we train the model by 12 epochs (see config in configs/nas-benchmark/LESS.config)
# If use_12epochs_result=False, we train the model by 200 epochs (see config in configs/nas-benchmark/CIFAR.config)
# ------
# If dataname is None, return the ArchResults
# else, return a dict with all trials on that dataset (the key is the seed)
# Options are 'cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120'.
# -- 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.
def query_by_index(self, arch_index: int, dataname: Union[None, Text] = None,
use_12epochs_result: bool = False):
if use_12epochs_result: basestr, arch2infos = '12epochs' , self.arch2infos_less
else : basestr, arch2infos = '200epochs', self.arch2infos_full
assert arch_index in arch2infos, 'arch_index [{:}] does not in arch2info with {:}'.format(arch_index, basestr)
archInfo = copy.deepcopy( arch2infos[ arch_index ] )
if dataname is None: return archInfo
else:
assert dataname in archInfo.get_dataset_names(), 'invalid dataset-name : {:}'.format(dataname)
info = archInfo.query(dataname)
return info
def query_meta_info_by_index(self, arch_index, use_12epochs_result=False):
if use_12epochs_result: basestr, arch2infos = '12epochs' , self.arch2infos_less
else : basestr, arch2infos = '200epochs', self.arch2infos_full
assert arch_index in arch2infos, 'arch_index [{:}] does not in arch2info with {:}'.format(arch_index, basestr)
archInfo = copy.deepcopy( arch2infos[ arch_index ] )
return archInfo
def find_best(self, dataset, metric_on_set, FLOP_max=None, Param_max=None, use_12epochs_result=False):
"""Find the architecture with the highest accuracy based on some constraints."""
if use_12epochs_result: basestr, arch2infos = '12epochs' , self.arch2infos_less
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_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
xinfo = arch2infos[idx].get_metrics(dataset, metric_on_set)
loss, accuracy = xinfo['loss'], xinfo['accuracy']
if best_index == -1:
best_index, highest_accuracy = idx, accuracy
elif highest_accuracy < accuracy:
best_index, highest_accuracy = idx, accuracy
return best_index, highest_accuracy
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])
def get_net_param(self, index, dataset, seed, use_12epochs_result=False):
"""
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)
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))
for seed, result in all_results.items():
return result.get_config(None)
#print ('SEED [{:}] : {:}'.format(seed, result))
raise ValueError('Impossible to reach here!')
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:
# '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: int, dataset, iepoch=None, use_12epochs_result=False, is_random=True):
if use_12epochs_result: basestr, arch2infos = '12epochs' , self.arch2infos_less
else : basestr, arch2infos = '200epochs', self.arch2infos_full
archresult = arch2infos[index]
# 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,
'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
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
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
xinfo['valtest-all-time'] = valtest_info['all_time']
return xinfo
""" # The following logic is deprecated after March 15 2020, where the benchmark file upgrades from NAS-Bench-201-v1_0-e61699.pth to NAS-Bench-201-v1_1-096897.pth.
def get_more_info(self, index: int, dataset, iepoch=None, use_12epochs_result=False, is_random=True):
if use_12epochs_result: basestr, arch2infos = '12epochs' , self.arch2infos_less
else : basestr, arch2infos = '200epochs', self.arch2infos_full
archresult = arch2infos[index]
# 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)
if dataset == 'cifar10-valid':
train_info = archresult.get_metrics(dataset, 'train' , iepoch=iepoch, is_random=is_random)
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
total = train_info['iepoch'] + 1
xifo = {'train-loss' : train_info['loss'],
'train-accuracy': train_info['accuracy'],
'train-per-time': None if train_info['all_time'] is None else train_info['all_time'] / total,
'train-all-time': train_info['all_time'],
'valid-loss' : valid_info['loss'],
'valid-accuracy': valid_info['accuracy'],
'valid-all-time': valid_info['all_time'],
'valid-per-time': None if valid_info['all_time'] is None else valid_info['all_time'] / total}
if test__info is not None:
xifo['test-loss'] = test__info['loss']
xifo['test-accuracy'] = test__info['accuracy']
return xifo
else:
train_info = archresult.get_metrics(dataset, 'train' , iepoch=iepoch, is_random=is_random)
try:
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:
valid_info = archresult.get_metrics(dataset, 'x-valid', iepoch=iepoch, is_random=is_random)
except:
valid_info = None
try:
est_valid_info = archresult.get_metrics(dataset, 'ori-test', iepoch=iepoch, is_random=is_random)
except:
est_valid_info = None
xifo = {'train-loss' : train_info['loss'],
'train-accuracy': train_info['accuracy']}
if test__info is not None:
xifo['test-loss'] = test__info['loss'],
xifo['test-accuracy'] = test__info['accuracy']
if valid_info is not None:
xifo['valid-loss'] = valid_info['loss']
xifo['valid-accuracy'] = valid_info['accuracy']
if est_valid_info is not None:
xifo['est-valid-loss'] = est_valid_info['loss']
xifo['est-valid-accuracy'] = est_valid_info['accuracy']
return xifo
"""
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):
print('\n' + '-' * 10 + ' The ({:5d}/{:5d}) {:06d}-th architecture! '.format(i, len(self.evaluated_indexes), idx) + '-'*10)
print('arch : {:}'.format(self.meta_archs[idx]))
strings = print_information(self.arch2infos_full[idx])
print('>' * 40 + ' {:03d} epochs '.format(self.arch2infos_full[idx].get_total_epoch()) + '>' * 40)
print('\n'.join(strings))
strings = print_information(self.arch2infos_less[idx])
print('>' * 40 + ' {:03d} epochs '.format(self.arch2infos_less[idx].get_total_epoch()) + '>' * 40)
print('\n'.join(strings))
print('<' * 40 + '------------' + '<' * 40)
else:
if 0 <= index < len(self.meta_archs):
if index not in self.evaluated_indexes: print('The {:}-th architecture has not been evaluated or not saved.'.format(index))
else:
strings = print_information(self.arch2infos_full[index])
print('>' * 40 + ' {:03d} epochs '.format(self.arch2infos_full[index].get_total_epoch()) + '>' * 40)
print('\n'.join(strings))
strings = print_information(self.arch2infos_less[index])
print('>' * 40 + ' {:03d} epochs '.format(self.arch2infos_less[index].get_total_epoch()) + '>' * 40)
print('\n'.join(strings))
print('<' * 40 + '------------' + '<' * 40)
else:
print('This index ({:}) is out of range (0~{:}).'.format(index, len(self.meta_archs)))
def statistics(self, dataset: Text, use_12epochs_result: bool) -> Dict[int, int]:
"""
This function will count the number of total trials.
"""
valid_datasets = ['cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120']
if dataset not in valid_datasets:
raise ValueError('{:} not in {:}'.format(dataset, valid_datasets))
if use_12epochs_result: arch2infos = self.arch2infos_less
else : arch2infos = self.arch2infos_full
nums = defaultdict(lambda: 0)
for index in range(len(self)):
archInfo = arch2infos[index]
dataset_seed = archInfo.dataset_seed
if dataset not in dataset_seed:
nums[0] += 1
else:
nums[len(dataset_seed[dataset])] += 1
return dict(nums)
@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
class ArchResults(object):
def __init__(self, arch_index, arch_str):
self.arch_index = int(arch_index)
self.arch_str = copy.deepcopy(arch_str)
self.all_results = dict()
self.dataset_seed = dict()
self.clear_net_done = False
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]
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()
for key, value in time_info.items(): time_infos[key].append( value )
info = {'flops' : np.mean(flops),
'params' : np.mean(params),
'latency': mean_latency}
for key, value in time_infos.items():
if len(value) > 0 and value[0] is not None:
info[key] = np.mean(value)
else: info[key] = None
return info
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)
for result in results:
if setname == 'train':
info = result.get_train(iepoch)
else:
info = result.get_eval(setname, iepoch)
for key, value in info.items(): infos[key].append( value )
return_info = dict()
if isinstance(is_random, bool) and is_random: # randomly select one
index = random.randint(0, len(results)-1)
for key, value in infos.items(): return_info[key] = value[index]
elif isinstance(is_random, bool) and not is_random: # average
for key, value in infos.items():
if len(value) > 0 and value[0] is not None:
return_info[key] = np.mean(value)
else: return_info[key] = None
elif isinstance(is_random, int): # specify the seed
if is_random not in x_seeds: raise ValueError('can not find random seed ({:}) from {:}'.format(is_random, x_seeds))
index = x_seeds.index(is_random)
for key, value in infos.items(): return_info[key] = value[index]
else:
raise ValueError('invalid value for is_random: {:}'.format(is_random))
return return_info
def show(self, is_print=False):
return print_information(self, None, is_print)
def get_dataset_names(self):
return list(self.dataset_seed.keys())
def get_dataset_seeds(self, dataset):
return copy.deepcopy( self.dataset_seed[dataset] )
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()
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():
epochss += [self.all_results[(xdata, seed)].get_total_epoch() for seed in x_seeds]
elif isinstance(dataset, str):
x_seeds = self.dataset_seed[dataset]
epochss = [self.all_results[(dataset, seed)].get_total_epoch() for seed in x_seeds]
else:
raise ValueError('invalid dataset={:}'.format(dataset))
if len(set(epochss)) > 1: raise ValueError('Each trial mush have the same number of training epochs : {:}'.format(epochss))
return epochss[-1]
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}
else:
return self.all_results[(dataset, seed)]
def arch_idx_str(self):
return '{:06d}'.format(self.arch_index)
def update(self, dataset_name, seed, result):
if dataset_name not in self.dataset_seed:
self.dataset_seed[dataset_name] = []
assert seed not in self.dataset_seed[dataset_name], '{:}-th arch alreadly has this seed ({:}) on {:}'.format(self.arch_index, seed, dataset_name)
self.dataset_seed[ dataset_name ].append( seed )
self.dataset_seed[ dataset_name ] = sorted( self.dataset_seed[ dataset_name ] )
assert (dataset_name, seed) not in self.all_results
self.all_results[ (dataset_name, seed) ] = result
self.clear_net_done = False
def state_dict(self):
state_dict = dict()
for key, value in self.__dict__.items():
if key == 'all_results': # contain the class of ResultsCount
xvalue = dict()
assert isinstance(value, dict), 'invalid type of value for {:} : {:}'.format(key, type(value))
for _k, _v in value.items():
assert isinstance(_v, ResultsCount), 'invalid type of value for {:}/{:} : {:}'.format(key, _k, type(_v))
xvalue[_k] = _v.state_dict()
else:
xvalue = value
state_dict[key] = xvalue
return state_dict
def load_state_dict(self, state_dict):
new_state_dict = dict()
for key, value in state_dict.items():
if key == 'all_results': # to convert to the class of ResultsCount
xvalue = dict()
assert isinstance(value, dict), 'invalid type of value for {:} : {:}'.format(key, type(value))
for _k, _v in value.items():
xvalue[_k] = ResultsCount.create_from_state_dict(_v)
else: xvalue = value
new_state_dict[key] = xvalue
self.__dict__.update(new_state_dict)
@staticmethod
def create_from_state_dict(state_dict_or_file):
x = ArchResults(-1, -1)
if isinstance(state_dict_or_file, str): # a file path
state_dict = torch.load(state_dict_or_file, map_location='cpu')
elif isinstance(state_dict_or_file, dict):
state_dict = state_dict_or_file
else:
raise ValueError('invalid type of state_dict_or_file : {:}'.format(type(state_dict_or_file)))
x.load_state_dict(state_dict)
return x
# This function is used to clear the weights saved in each 'result'
# This can help reduce the memory footprint.
def clear_params(self):
for key, result in self.all_results.items():
del result.net_state_dict
result.net_state_dict = None
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))
"""
This class (ResultsCount) is used to save the information of one trial for a single architecture.
I did not write much comment for this class, because it is the lowest-level class in NAS-Bench-201 API, which will be rarely called.
If you have any question regarding this class, please open an issue or email me.
"""
class ResultsCount(object):
def __init__(self, name, state_dict, train_accs, train_losses, params, flop, arch_config, seed, epochs, latency):
self.name = name
self.net_state_dict = state_dict
self.train_acc1es = copy.deepcopy(train_accs)
self.train_acc5es = None
self.train_losses = copy.deepcopy(train_losses)
self.train_times = None
self.arch_config = copy.deepcopy(arch_config)
self.params = params
self.flop = flop
self.seed = seed
self.epochs = epochs
self.latency = latency
# evaluation results
self.reset_eval()
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 = {}
self.eval_times = {}
self.eval_losses = {}
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:
assert data_name not in self.eval_names, '{:} has already been added into eval-names'.format(data_name)
self.eval_names.append( data_name )
for iepoch in range(self.epochs):
xkey = '{:}@{:}'.format(data_name, iepoch)
self.eval_acc1es[ xkey ] = accs[ xkey ]
self.eval_losses[ xkey ] = losses[ xkey ]
self.eval_times [ xkey ] = times[ xkey ]
def update_OLD_eval(self, name, accs, losses): # old version
assert name not in self.eval_names, '{:} has already added'.format(name)
self.eval_names.append( name )
for iepoch in range(self.epochs):
if iepoch in accs:
self.eval_acc1es['{:}@{:}'.format(name,iepoch)] = accs[iepoch]
self.eval_losses['{:}@{:}'.format(name,iepoch)] = losses[iepoch]
def __repr__(self):
num_eval = len(self.eval_names)
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))
def get_total_epoch(self):
return copy.deepcopy(self.epochs)
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)}
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)
except:
time_info['T-{:}@epoch'.format(name)] = None
time_info['T-{:}@total'.format(name)] = None
return time_info
def get_eval_set(self):
return self.eval_names
# get the training information
def get_train(self, iepoch=None):
if iepoch is None: iepoch = self.epochs-1
assert 0 <= iepoch < self.epochs, 'invalid iepoch={:} < {:}'.format(iepoch, self.epochs)
if self.train_times is not None:
xtime = self.train_times[iepoch]
atime = sum([self.train_times[i] for i in range(iepoch+1)])
else: xtime, atime = None, None
return {'iepoch' : iepoch,
'loss' : self.train_losses[iepoch],
'accuracy': self.train_acc1es[iepoch],
'cur_time': xtime,
'all_time': atime}
def get_eval(self, name, iepoch=None):
"""Get the evaluation information ; there could be multiple evaluation sets (identified by the 'name' argument)."""
if iepoch is None: iepoch = self.epochs-1
assert 0 <= iepoch < self.epochs, 'invalid iepoch={:} < {:}'.format(iepoch, self.epochs)
if isinstance(self.eval_times,dict) and len(self.eval_times) > 0:
xtime = self.eval_times['{:}@{:}'.format(name,iepoch)]
atime = sum([self.eval_times['{:}@{:}'.format(name,i)] for i in range(iepoch+1)])
else: xtime, atime = None, None
return {'iepoch' : iepoch,
'loss' : self.eval_losses['{:}@{:}'.format(name,iepoch)],
'accuracy': self.eval_acc1es['{:}@{:}'.format(name,iepoch)],
'cur_time': xtime,
'all_time': atime}
def get_net_param(self, clone=False):
if clone: return copy.deepcopy(self.net_state_dict)
else: return self.net_state_dict
def get_config(self, str2structure):
"""This function is used to obtain the config dict for this architecture."""
if str2structure is None:
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'],
'genotype': str2structure(self.arch_config['arch_str']), 'num_classes': self.arch_config['class_num']}
def state_dict(self):
_state_dict = {key: value for key, value in self.__dict__.items()}
return _state_dict
def load_state_dict(self, state_dict):
self.__dict__.update(state_dict)
@staticmethod
def create_from_state_dict(state_dict):
x = ResultsCount(None, None, None, None, None, None, None, None, None, None)
x.load_state_dict(state_dict)
return x

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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
############################################################################################
# NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search, ICLR 2020 #
############################################################################################
# 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
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()
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))
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: int, 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))
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
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
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
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

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lib/nas_201_api/api_301.py Normal file
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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.06 #
############################################################################################
# NAS-Bench-301, coming soon.
############################################################################################
# The history of benchmark files:
# [2020.06.30] NAS-Bench-301-v1_0
#
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-301-v1_0-363be7.pth']
ALL_ARCHIVE_DIRS = ['NAS-Bench-301-v1_0-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')
test__info = information.get_metrics(dataset, 'ori-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']))
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-301.
"""
class NASBench301API(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
if file_path_or_dict is None:
file_path_or_dict = os.path.join(os.environ['TORCH_HOME'], ALL_BENCHMARK_FILES[-1])
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()
for xkey in sorted(list(file_path_or_dict['arch2infos'].keys())):
all_infos = file_path_or_dict['arch2infos'][xkey]
hp2archres = OrderedDict()
for hp_key, results in all_infos.items():
hp2archres[hp_key] = ArchResults.create_from_state_dict(results)
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
if self.verbose:
print('Create NAS-Bench-301 done with {:}/{:} architectures avaliable.'.format(len(self.evaluated_indexes), len(self.meta_archs)))
def reload(self, archive_root: Text = None, index: int = None):
"""Overwrite all information of the 'index'-th architecture in the search space, where the data will be loaded from 'archive_root'.
If index is None, overwrite all ckps.
"""
if self.verbose:
print('Call clear_params with archive_root={:} and index={:}'.format(archive_root, index))
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))
if not os.path.isfile(xfile_path):
xfile_path = os.path.join(archive_root, '{:d}-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), 'invalid format of data in {:}'.format(xfile_path)
hp2archres = OrderedDict()
for hp_key, results in xdata.items():
hp2archres[hp_key] = ArchResults.create_from_state_dict(results)
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=01, the hyper-parameters used to train a model are in 'configs/nas-benchmark/hyper-opts/01E.config'
When hp=12, the hyper-parameters used to train a model are in 'configs/nas-benchmark/hyper-opts/12E.config'
When hp=90, the hyper-parameters used to train a model are in 'configs/nas-benchmark/hyper-opts/90E.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))
self._query_info_str_by_arch(arch, hp, print_information)
def get_more_info(self, index: int, dataset: Text, iepoch=None, hp='12', is_random=True):
"""This function will return 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)
`hp` indicates different hyper-parameters for training
When hp=01, it trains the network with 01 epochs and the LR decayed from 0.1 to 0 within 01 epochs
When hp=12, it trains the network with 01 epochs and the LR decayed from 0.1 to 0 within 12 epochs
When hp=90, it trains the network with 01 epochs and the LR decayed from 0.1 to 0 within 90 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.
"""
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))
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,
'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
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
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
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).
: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 architecture.
:return: nothing
"""
self._show(index, print_information)

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#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
############################################################################################
# NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search, ICLR 2020 #
############################################################################################
# In this Python file, we define NASBenchMetaAPI, the abstract class for benchmark APIs.
# We also define the class ArchResults, which contains all information of a single architecture trained by one kind of hyper-parameters on three datasets.
# We also define the class ResultsCount, which contains all information of a single trial for a single architecture.
############################################################################################
# History:
# [2020.06.30] The first version.
#
import os, abc, copy, random, torch, numpy as np
from pathlib import Path
from typing import List, Text, Union, Dict, Optional
from collections import OrderedDict, defaultdict
def remap_dataset_set_names(dataset, metric_on_set, verbose=False):
"""re-map the metric_on_set to internal keys"""
if verbose:
print('Call internal function _remap_dataset_set_names with dataset={:} and metric_on_set={:}'.format(dataset, metric_on_set))
if dataset == 'cifar10' and metric_on_set == 'valid':
dataset, metric_on_set = 'cifar10-valid', 'x-valid'
elif dataset == 'cifar10' and metric_on_set == 'test':
dataset, metric_on_set = 'cifar10', 'ori-test'
elif dataset == 'cifar10' and metric_on_set == 'train':
dataset, metric_on_set = 'cifar10', 'train'
elif (dataset == 'cifar100' or dataset == 'ImageNet16-120') and metric_on_set == 'valid':
metric_on_set = 'x-valid'
elif (dataset == 'cifar100' or dataset == 'ImageNet16-120') and metric_on_set == 'test':
metric_on_set = 'x-test'
if verbose:
print(' return dataset={:} and metric_on_set={:}'.format(dataset, metric_on_set))
return dataset, metric_on_set
class NASBenchMetaAPI(metaclass=abc.ABCMeta):
@abc.abstractmethod
def __init__(self, file_path_or_dict: Optional[Union[Text, Dict]]=None, verbose: bool=True):
"""The initialization function that takes the dataset file path (or a dict loaded from that path) as input."""
def __getitem__(self, index: int):
return copy.deepcopy(self.meta_archs[index])
def arch(self, index: int):
"""Return the topology structure of the `index`-th architecture."""
if self.verbose:
print('Call the arch function with index={:}'.format(index))
assert 0 <= index < len(self.meta_archs), 'invalid index : {:} vs. {:}.'.format(index, len(self.meta_archs))
return copy.deepcopy(self.meta_archs[index])
def __len__(self):
return len(self.meta_archs)
def __repr__(self):
return ('{name}({num}/{total} architectures, file={filename})'.format(name=self.__class__.__name__, num=len(self.evaluated_indexes), total=len(self.meta_archs), filename=self.filename))
def random(self):
"""Return a random index of all architectures."""
return random.randint(0, len(self.meta_archs)-1)
def query_index_by_arch(self, arch):
""" This function is used to query the index of an architecture in the search space.
In the topology search space, the input arch can be an architecture string such as '|nor_conv_3x3~0|+|nor_conv_3x3~0|avg_pool_3x3~1|+|skip_connect~0|nor_conv_3x3~1|skip_connect~2|';
or an instance that has the 'tostr' function that can generate the architecture string;
or it is directly an architecture index, in this case, we will check whether it is valid or not.
This function will return the index.
If return -1, it means this architecture is not in the search space.
Otherwise, it will return an int in [0, the-number-of-candidates-in-the-search-space).
"""
if self.verbose:
print('Call query_index_by_arch with arch={:}'.format(arch))
if isinstance(arch, int):
if 0 <= arch < len(self):
return arch
else:
raise ValueError('Invalid architecture index {:} vs [{:}, {:}].'.format(arch, 0, len(self)))
elif isinstance(arch, str):
if arch in self.archstr2index: arch_index = self.archstr2index[ arch ]
else : arch_index = -1
elif hasattr(arch, 'tostr'):
if arch.tostr() in self.archstr2index: arch_index = self.archstr2index[ arch.tostr() ]
else : arch_index = -1
else: arch_index = -1
return arch_index
@abc.abstractmethod
def reload(self, archive_root: Text = None, index: int = None):
"""Overwrite all information of the 'index'-th architecture in the search space, where the data will be loaded from 'archive_root'.
If index is None, overwrite all ckps.
"""
def clear_params(self, index: int, hp: Optional[Text]=None):
"""Remove the architecture's weights to save memory.
:arg
index: the index of the target architecture
hp: a flag to controll how to clear the parameters.
-- None: clear all the weights in '01'/'12'/'90', which indicates the number of training epochs.
-- '01' or '12' or '90': clear all the weights in arch2infos_dict[index][hp].
"""
if self.verbose:
print('Call clear_params with index={:} and hp={:}'.format(index, hp))
if hp is None:
for key, result in self.arch2infos_dict[index].items():
result.clear_params()
else:
if str(hp) not in self.arch2infos_dict[index]:
raise ValueError('The {:}-th architecture only has hyper-parameters of {:} instead of {:}.'.format(index, list(self.arch2infos_dict[index].keys()), hp))
self.arch2infos_dict[index][str(hp)].clear_params()
@abc.abstractmethod
def query_info_str_by_arch(self, arch, hp: Text='12'):
"""This function is used to query the information of a specific architecture."""
def _query_info_str_by_arch(self, arch, hp: Text='12', print_information=None):
arch_index = self.query_index_by_arch(arch)
if arch_index in self.arch2infos_dict:
if hp not in self.arch2infos_dict[arch_index]:
raise ValueError('The {:}-th architecture only has hyper-parameters of {:} instead of {:}.'.format(index, list(self.arch2infos_dict[arch_index].keys()), hp))
info = self.arch2infos_dict[arch_index][hp]
strings = print_information(info, 'arch-index={:}'.format(arch_index))
return '\n'.join(strings)
else:
print ('Find this arch-index : {:}, but this arch is not evaluated.'.format(arch_index))
return None
def query_meta_info_by_index(self, arch_index, hp: Text = '12'):
"""Return the ArchResults for the 'arch_index'-th architecture. This function is similar to query_by_index."""
if self.verbose:
print('Call query_meta_info_by_index with arch_index={:}, hp={:}'.format(arch_index, hp))
if arch_index in self.arch2infos_dict:
if hp not in self.arch2infos_dict[arch_index]:
raise ValueError('The {:}-th architecture only has hyper-parameters of {:} instead of {:}.'.format(arch_index, list(self.arch2infos_dict[arch_index].keys()), hp))
info = self.arch2infos_dict[arch_index][hp]
else:
raise ValueError('arch_index [{:}] does not in arch2infos'.format(arch_index))
return copy.deepcopy(info)
def query_by_index(self, arch_index: int, dataname: Union[None, Text] = None, hp: Text = '12'):
""" This 'query_by_index' function is used to query information with the training of 01 epochs, 12 epochs, 90 epochs, or 200 epochs.
------
If hp=01, we train the model by 01 epochs (see config in configs/nas-benchmark/hyper-opts/01E.config)
If hp=12, we train the model by 01 epochs (see config in configs/nas-benchmark/hyper-opts/12E.config)
If hp=90, we train the model by 01 epochs (see config in configs/nas-benchmark/hyper-opts/90E.config)
If hp=200, we train the model by 01 epochs (see config in configs/nas-benchmark/hyper-opts/200E.config)
------
If dataname is None, return the ArchResults
else, return a dict with all trials on that dataset (the key is the seed)
Options are 'cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120'.
-- 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.
"""
if self.verbose:
print('Call query_by_index with arch_index={:}, dataname={:}, hp={:}'.format(arch_index, dataname, hp))
info = self.query_meta_info_by_index(arch_index, hp)
if dataname is None: return info
else:
if dataname not in info.get_dataset_names():
raise ValueError('invalid dataset-name : {:} vs. {:}'.format(dataname, info.get_dataset_names()))
return info.query(dataname)
def find_best(self, dataset, metric_on_set, FLOP_max=None, Param_max=None, hp: Text = '12'):
"""Find the architecture with the highest accuracy based on some constraints."""
if self.verbose:
print('Call find_best with dataset={:}, metric_on_set={:}, hp={:} | with #FLOPs < {:} and #Params < {:}'.format(dataset, metric_on_set, hp, FLOP_max, Param_max))
dataset, metric_on_set = remap_dataset_set_names(dataset, metric_on_set, self.verbose)
best_index, highest_accuracy = -1, None
for i, arch_index in enumerate(self.evaluated_indexes):
arch_info = self.arch2infos_dict[arch_index][hp]
info = arch_info.get_compute_costs(dataset) # the information of costs
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
xinfo = arch_info.get_metrics(dataset, metric_on_set) # the information of loss and accuracy
loss, accuracy = xinfo['loss'], xinfo['accuracy']
if best_index == -1:
best_index, highest_accuracy = arch_index, accuracy
elif highest_accuracy < accuracy:
best_index, highest_accuracy = arch_index, accuracy
if self.verbose:
print(' the best architecture : [{:}] {:} with accuracy={:.3f}%'.format(best_index, self.arch(best_index), highest_accuracy))
return best_index, highest_accuracy
def get_net_param(self, index, dataset, seed: Optional[int], hp: Text = '12'):
"""
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 [hp]:
-- 01 : train the model by 01 epochs
-- 12 : train the model by 12 epochs
-- 90 : train the model by 90 epochs
-- 200 : train the model by 200 epochs
"""
if self.verbose:
print('Call the get_net_param function with index={:}, dataset={:}, seed={:}, hp={:}'.format(index, dataset, seed, hp))
info = self.query_meta_info_by_index(index, hp)
return info.get_net_param(dataset, seed)
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')
"""
if self.verbose:
print('Call the get_net_config function with index={:}, dataset={:}.'.format(index, dataset))
if index in self.arch2infos_dict:
info = self.arch2infos_dict[index]
else:
raise ValueError('The arch_index={:} is not in arch2infos_dict.'.format(arch_index))
info = next(iter(info.values()))
results = info.query(dataset, None)
results = next(iter(results.values()))
return results.get_config(None)
def get_cost_info(self, index: int, dataset: Text, hp: Text = '12') -> Dict[Text, float]:
"""To obtain the cost metric for the `index`-th architecture on a dataset."""
if self.verbose:
print('Call the get_cost_info function with index={:}, dataset={:}, and hp={:}.'.format(index, dataset, hp))
info = self.query_meta_info_by_index(index, hp)
return info.get_compute_costs(dataset)
def get_latency(self, index: int, dataset: Text, hp: Text = '12') -> 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
"""
if self.verbose:
print('Call the get_latency function with index={:}, dataset={:}, and hp={:}.'.format(index, dataset, hp))
cost_dict = self.get_cost_info(index, dataset, hp)
return cost_dict['latency']
@abc.abstractmethod
def show(self, index=-1):
"""This function will print the information of a specific (or all) architecture(s)."""
def _show(self, index=-1, print_information=None) -> 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 architecture.
:return: nothing
"""
if index < 0: # show all architectures
print(self)
for i, idx in enumerate(self.evaluated_indexes):
print('\n' + '-' * 10 + ' The ({:5d}/{:5d}) {:06d}-th architecture! '.format(i, len(self.evaluated_indexes), idx) + '-'*10)
print('arch : {:}'.format(self.meta_archs[idx]))
for key, result in self.arch2infos_dict[index].items():
strings = print_information(result)
print('>' * 40 + ' {:03d} epochs '.format(result.get_total_epoch()) + '>' * 40)
print('\n'.join(strings))
print('<' * 40 + '------------' + '<' * 40)
else:
if 0 <= index < len(self.meta_archs):
if index not in self.evaluated_indexes: print('The {:}-th architecture has not been evaluated or not saved.'.format(index))
else:
arch_info = self.arch2infos_dict[index]
for key, result in self.arch2infos_dict[index].items():
strings = print_information(result)
print('>' * 40 + ' {:03d} epochs '.format(result.get_total_epoch()) + '>' * 40)
print('\n'.join(strings))
print('<' * 40 + '------------' + '<' * 40)
else:
print('This index ({:}) is out of range (0~{:}).'.format(index, len(self.meta_archs)))
def statistics(self, dataset: Text, hp: Union[Text, int]) -> Dict[int, int]:
"""This function will count the number of total trials."""
if self.verbose:
print('Call the statistics function with dataset={:} and hp={:}.'.format(dataset, hp))
valid_datasets = ['cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120']
if dataset not in valid_datasets:
raise ValueError('{:} not in {:}'.format(dataset, valid_datasets))
nums, hp = defaultdict(lambda: 0), str(hp)
for index in range(len(self)):
archInfo = self.arch2infos_dict[index][hp]
dataset_seed = archInfo.dataset_seed
if dataset not in dataset_seed:
nums[0] += 1
else:
nums[len(dataset_seed[dataset])] += 1
return dict(nums)
class ArchResults(object):
def __init__(self, arch_index, arch_str):
self.arch_index = int(arch_index)
self.arch_str = copy.deepcopy(arch_str)
self.all_results = dict()
self.dataset_seed = dict()
self.clear_net_done = False
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]
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()
for key, value in time_info.items(): time_infos[key].append( value )
info = {'flops' : np.mean(flops),
'params' : np.mean(params),
'latency': mean_latency}
for key, value in time_infos.items():
if len(value) > 0 and value[0] is not None:
info[key] = np.mean(value)
else: info[key] = None
return info
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)
for result in results:
if setname == 'train':
info = result.get_train(iepoch)
else:
info = result.get_eval(setname, iepoch)
for key, value in info.items(): infos[key].append( value )
return_info = dict()
if isinstance(is_random, bool) and is_random: # randomly select one
index = random.randint(0, len(results)-1)
for key, value in infos.items(): return_info[key] = value[index]
elif isinstance(is_random, bool) and not is_random: # average
for key, value in infos.items():
if len(value) > 0 and value[0] is not None:
return_info[key] = np.mean(value)
else: return_info[key] = None
elif isinstance(is_random, int): # specify the seed
if is_random not in x_seeds: raise ValueError('can not find random seed ({:}) from {:}'.format(is_random, x_seeds))
index = x_seeds.index(is_random)
for key, value in infos.items(): return_info[key] = value[index]
else:
raise ValueError('invalid value for is_random: {:}'.format(is_random))
return return_info
def show(self, is_print=False):
return print_information(self, None, is_print)
def get_dataset_names(self):
return list(self.dataset_seed.keys())
def get_dataset_seeds(self, dataset):
return copy.deepcopy( self.dataset_seed[dataset] )
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()
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 {:} with seed={:} : {:}'.format(dataset, seed, latency))
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():
epochss += [self.all_results[(xdata, seed)].get_total_epoch() for seed in x_seeds]
elif isinstance(dataset, str):
x_seeds = self.dataset_seed[dataset]
epochss = [self.all_results[(dataset, seed)].get_total_epoch() for seed in x_seeds]
else:
raise ValueError('invalid dataset={:}'.format(dataset))
if len(set(epochss)) > 1: raise ValueError('Each trial mush have the same number of training epochs : {:}'.format(epochss))
return epochss[-1]
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}
else:
return self.all_results[(dataset, seed)]
def arch_idx_str(self):
return '{:06d}'.format(self.arch_index)
def update(self, dataset_name, seed, result):
if dataset_name not in self.dataset_seed:
self.dataset_seed[dataset_name] = []
assert seed not in self.dataset_seed[dataset_name], '{:}-th arch alreadly has this seed ({:}) on {:}'.format(self.arch_index, seed, dataset_name)
self.dataset_seed[ dataset_name ].append( seed )
self.dataset_seed[ dataset_name ] = sorted( self.dataset_seed[ dataset_name ] )
assert (dataset_name, seed) not in self.all_results
self.all_results[ (dataset_name, seed) ] = result
self.clear_net_done = False
def state_dict(self):
state_dict = dict()
for key, value in self.__dict__.items():
if key == 'all_results': # contain the class of ResultsCount
xvalue = dict()
assert isinstance(value, dict), 'invalid type of value for {:} : {:}'.format(key, type(value))
for _k, _v in value.items():
assert isinstance(_v, ResultsCount), 'invalid type of value for {:}/{:} : {:}'.format(key, _k, type(_v))
xvalue[_k] = _v.state_dict()
else:
xvalue = value
state_dict[key] = xvalue
return state_dict
def load_state_dict(self, state_dict):
new_state_dict = dict()
for key, value in state_dict.items():
if key == 'all_results': # to convert to the class of ResultsCount
xvalue = dict()
assert isinstance(value, dict), 'invalid type of value for {:} : {:}'.format(key, type(value))
for _k, _v in value.items():
xvalue[_k] = ResultsCount.create_from_state_dict(_v)
else: xvalue = value
new_state_dict[key] = xvalue
self.__dict__.update(new_state_dict)
@staticmethod
def create_from_state_dict(state_dict_or_file):
x = ArchResults(-1, -1)
if isinstance(state_dict_or_file, str): # a file path
state_dict = torch.load(state_dict_or_file, map_location='cpu')
elif isinstance(state_dict_or_file, dict):
state_dict = state_dict_or_file
else:
raise ValueError('invalid type of state_dict_or_file : {:}'.format(type(state_dict_or_file)))
x.load_state_dict(state_dict)
return x
# This function is used to clear the weights saved in each 'result'
# This can help reduce the memory footprint.
def clear_params(self):
for key, result in self.all_results.items():
del result.net_state_dict
result.net_state_dict = None
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))
"""
This class (ResultsCount) is used to save the information of one trial for a single architecture.
I did not write much comment for this class, because it is the lowest-level class in NAS-Bench-201 API, which will be rarely called.
If you have any question regarding this class, please open an issue or email me.
"""
class ResultsCount(object):
def __init__(self, name, state_dict, train_accs, train_losses, params, flop, arch_config, seed, epochs, latency):
self.name = name
self.net_state_dict = state_dict
self.train_acc1es = copy.deepcopy(train_accs)
self.train_acc5es = None
self.train_losses = copy.deepcopy(train_losses)
self.train_times = None
self.arch_config = copy.deepcopy(arch_config)
self.params = params
self.flop = flop
self.seed = seed
self.epochs = epochs
self.latency = latency
# evaluation results
self.reset_eval()
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 = {}
self.eval_times = {}
self.eval_losses = {}
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:
assert data_name not in self.eval_names, '{:} has already been added into eval-names'.format(data_name)
self.eval_names.append( data_name )
for iepoch in range(self.epochs):
xkey = '{:}@{:}'.format(data_name, iepoch)
self.eval_acc1es[ xkey ] = accs[ xkey ]
self.eval_losses[ xkey ] = losses[ xkey ]
self.eval_times [ xkey ] = times[ xkey ]
def update_OLD_eval(self, name, accs, losses): # old version
assert name not in self.eval_names, '{:} has already added'.format(name)
self.eval_names.append( name )
for iepoch in range(self.epochs):
if iepoch in accs:
self.eval_acc1es['{:}@{:}'.format(name,iepoch)] = accs[iepoch]
self.eval_losses['{:}@{:}'.format(name,iepoch)] = losses[iepoch]
def __repr__(self):
num_eval = len(self.eval_names)
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))
def get_total_epoch(self):
return copy.deepcopy(self.epochs)
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)}
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)
except:
time_info['T-{:}@epoch'.format(name)] = None
time_info['T-{:}@total'.format(name)] = None
return time_info
def get_eval_set(self):
return self.eval_names
# get the training information
def get_train(self, iepoch=None):
if iepoch is None: iepoch = self.epochs-1
assert 0 <= iepoch < self.epochs, 'invalid iepoch={:} < {:}'.format(iepoch, self.epochs)
if self.train_times is not None:
xtime = self.train_times[iepoch]
atime = sum([self.train_times[i] for i in range(iepoch+1)])
else: xtime, atime = None, None
return {'iepoch' : iepoch,
'loss' : self.train_losses[iepoch],
'accuracy': self.train_acc1es[iepoch],
'cur_time': xtime,
'all_time': atime}
def get_eval(self, name, iepoch=None):
"""Get the evaluation information ; there could be multiple evaluation sets (identified by the 'name' argument)."""
if iepoch is None: iepoch = self.epochs-1
assert 0 <= iepoch < self.epochs, 'invalid iepoch={:} < {:}'.format(iepoch, self.epochs)
if isinstance(self.eval_times,dict) and len(self.eval_times) > 0:
xtime = self.eval_times['{:}@{:}'.format(name,iepoch)]
atime = sum([self.eval_times['{:}@{:}'.format(name,i)] for i in range(iepoch+1)])
else: xtime, atime = None, None
return {'iepoch' : iepoch,
'loss' : self.eval_losses['{:}@{:}'.format(name,iepoch)],
'accuracy': self.eval_acc1es['{:}@{:}'.format(name,iepoch)],
'cur_time': xtime,
'all_time': atime}
def get_net_param(self, clone=False):
if clone: return copy.deepcopy(self.net_state_dict)
else: return self.net_state_dict
def get_config(self, str2structure):
"""This function is used to obtain the config dict for this architecture."""
if str2structure is None:
# In this case, this is NAS-Bench-301
if 'name' in self.arch_config and self.arch_config['name'] == 'infer.shape.tiny':
return {'name': 'infer.shape.tiny', 'channels': self.arch_config['channels'],
'genotype': self.arch_config['genotype'], 'num_classes': self.arch_config['class_num']}
# In this case, this is NAS-Bench-201
else:
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:
# In this case, this is NAS-Bench-301
if 'name' in self.arch_config and self.arch_config['name'] == 'infer.shape.tiny':
return {'name': 'infer.shape.tiny', 'channels': self.arch_config['channels'],
'genotype': str2structure(self.arch_config['genotype']), 'num_classes': self.arch_config['class_num']}
# In this case, this is NAS-Bench-201
else:
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):
_state_dict = {key: value for key, value in self.__dict__.items()}
return _state_dict
def load_state_dict(self, state_dict):
self.__dict__.update(state_dict)
@staticmethod
def create_from_state_dict(state_dict):
x = ResultsCount(None, None, None, None, None, None, None, None, None, None)
x.load_state_dict(state_dict)
return x