import functools
from peewee import fn
from playhouse.shortcuts import model_to_dict
from nni.nas.benchmarks.utils import load_benchmark
from .model import Nb201TrialStats, Nb201TrialConfig, proxy
[docs]def query_nb201_trial_stats(arch, num_epochs, dataset, reduction=None, include_intermediates=False):
"""
Query trial stats of NAS-Bench-201 given conditions.
Parameters
----------
arch : dict or None
If a dict, it is in the format that is described in
:class:`nni.nas.benchmark.nasbench201.Nb201TrialConfig`. Only trial stats
matched will be returned. If none, all architectures in the database will be matched.
num_epochs : int or None
If int, matching results will be returned. Otherwise a wildcard.
dataset : str or None
If specified, can be one of the dataset available in :class:`nni.nas.benchmark.nasbench201.Nb201TrialConfig`.
Otherwise a wildcard.
reduction : str or None
If 'none' or None, all trial stats will be returned directly.
If 'mean', fields in trial stats will be averaged given the same trial config.
include_intermediates : boolean
If true, intermediate results will be returned.
Returns
-------
generator of dict
A generator of :class:`nni.nas.benchmark.nasbench201.Nb201TrialStats` objects,
where each of them has been converted into a dict.
"""
if proxy.obj is None:
proxy.initialize(load_benchmark('nasbench201'))
fields = []
if reduction == 'none':
reduction = None
if reduction == 'mean':
for field_name in Nb201TrialStats._meta.sorted_field_names:
if field_name not in ['id', 'config', 'seed']:
fields.append(fn.AVG(getattr(Nb201TrialStats, field_name)).alias(field_name))
elif reduction is None:
fields.append(Nb201TrialStats)
else:
raise ValueError('Unsupported reduction: \'%s\'' % reduction)
query = Nb201TrialStats.select(*fields, Nb201TrialConfig).join(Nb201TrialConfig)
conditions = []
if arch is not None:
conditions.append(Nb201TrialConfig.arch == arch)
if num_epochs is not None:
conditions.append(Nb201TrialConfig.num_epochs == num_epochs)
if dataset is not None:
conditions.append(Nb201TrialConfig.dataset == dataset)
if conditions:
query = query.where(functools.reduce(lambda a, b: a & b, conditions))
if reduction is not None:
query = query.group_by(Nb201TrialStats.config)
for trial in query:
if include_intermediates:
data = model_to_dict(trial)
# exclude 'trial' from intermediates as it is already available in data
data['intermediates'] = [
{k: v for k, v in model_to_dict(t).items() if k != 'trial'} for t in trial.intermediates
]
yield data
else:
yield model_to_dict(trial)