nni.nas.benchmarks.nasbench101.query 源代码

# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.

import functools

from peewee import fn
from playhouse.shortcuts import model_to_dict

from nni.nas.benchmarks.utils import load_benchmark
from .model import Nb101TrialStats, Nb101TrialConfig, proxy
from .graph_util import hash_module, infer_num_vertices


[文档]def query_nb101_trial_stats(arch, num_epochs, isomorphism=True, reduction=None, include_intermediates=False): """ Query trial stats of NAS-Bench-101 given conditions. Parameters ---------- arch : dict or None If a dict, it is in the format that is described in :class:`nni.nas.benchmark.nasbench101.Nb101TrialConfig`. 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. isomorphism : boolean Whether to match essentially-same architecture, i.e., architecture with the same graph-invariant hash value. 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.nasbench101.Nb101TrialStats` objects, where each of them has been converted into a dict. """ if proxy.obj is None: proxy.initialize(load_benchmark('nasbench101')) fields = [] if reduction == 'none': reduction = None if reduction == 'mean': for field_name in Nb101TrialStats._meta.sorted_field_names: if field_name not in ['id', 'config']: fields.append(fn.AVG(getattr(Nb101TrialStats, field_name)).alias(field_name)) elif reduction is None: fields.append(Nb101TrialStats) else: raise ValueError('Unsupported reduction: \'%s\'' % reduction) query = Nb101TrialStats.select(*fields, Nb101TrialConfig).join(Nb101TrialConfig) conditions = [] if arch is not None: if isomorphism: num_vertices = infer_num_vertices(arch) conditions.append(Nb101TrialConfig.hash == hash_module(arch, num_vertices)) else: conditions.append(Nb101TrialConfig.arch == arch) if num_epochs is not None: conditions.append(Nb101TrialConfig.num_epochs == num_epochs) if conditions: query = query.where(functools.reduce(lambda a, b: a & b, conditions)) if reduction is not None: query = query.group_by(Nb101TrialStats.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)