##################################################### # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.08 # ############################################################################## # NATS-Bench: Benchmarking NAS algorithms for Architecture Topology and Size # ############################################################################## # The history of benchmark files are as follows, # # where the format is (the name is NATS-sss-[version]-[md5].pickle.pbz2) # # [2020.08.31] NATS-sss-v1_0-50262.pickle.pbz2 # ############################################################################## # pylint: disable=line-too-long """The API for size search space in NATS-Bench.""" import collections import copy import os import random from typing import Dict, Optional, Text, Union, Any from nats_bench.api_utils import ArchResults from nats_bench.api_utils import NASBenchMetaAPI from nats_bench.api_utils import get_torch_home from nats_bench.api_utils import nats_is_dir from nats_bench.api_utils import nats_is_file from nats_bench.api_utils import PICKLE_EXT from nats_bench.api_utils import pickle_load from nats_bench.api_utils import time_string ALL_BASE_NAMES = ['NATS-sss-v1_0-50262'] def print_information(information, extra_info=None, show=False): """print out the information of a given ArchResults.""" 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 dataset in 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 class NATSsize(NASBenchMetaAPI): """This is the class for the API of size search space in NATS-Bench.""" def __init__(self, file_path_or_dict: Optional[Union[Text, Dict[Text, Any]]] = None, fast_mode: bool = False, verbose: bool = True): """The initialization function that takes the dataset file path (or a dict loaded from that path) as input.""" self._all_base_names = ALL_BASE_NAMES self.filename = None self._search_space_name = 'size' self._fast_mode = fast_mode self._archive_dir = None self._full_train_epochs = 90 self.reset_time() if file_path_or_dict is None: if self._fast_mode: self._archive_dir = os.path.join( get_torch_home(), '{:}-simple'.format(ALL_BASE_NAMES[-1])) else: file_path_or_dict = os.path.join( get_torch_home(), '{:}.{:}'.format( ALL_BASE_NAMES[-1], PICKLE_EXT)) print('{:} Try to use the default NATS-Bench (size) path from ' 'fast_mode={:} and path={:}.'.format(time_string(), self._fast_mode, file_path_or_dict)) if isinstance(file_path_or_dict, str): file_path_or_dict = str(file_path_or_dict) if verbose: print('{:} Try to create the NATS-Bench (size) api ' 'from {:} with fast_mode={:}'.format( time_string(), file_path_or_dict, fast_mode)) if not nats_is_file(file_path_or_dict) and not nats_is_dir( file_path_or_dict): raise ValueError('{:} is neither a file or a dir.'.format( file_path_or_dict)) self.filename = os.path.basename(file_path_or_dict) if fast_mode: if nats_is_file(file_path_or_dict): raise ValueError('fast_mode={:} must feed the path for directory ' ': {:}'.format(fast_mode, file_path_or_dict)) else: self._archive_dir = file_path_or_dict else: if nats_is_dir(file_path_or_dict): raise ValueError('fast_mode={:} must feed the path for file ' ': {:}'.format(fast_mode, file_path_or_dict)) else: file_path_or_dict = pickle_load(file_path_or_dict) elif isinstance(file_path_or_dict, dict): file_path_or_dict = copy.deepcopy(file_path_or_dict) self.verbose = verbose if isinstance(file_path_or_dict, dict): keys = ('meta_archs', 'arch2infos', 'evaluated_indexes') for key in keys: if key not in file_path_or_dict: raise ValueError('Can not find key[{:}] in the dict'.format(key)) self.meta_archs = copy.deepcopy(file_path_or_dict['meta_archs']) # NOTE(xuanyidong): This is a dict mapping each architecture to a dict, # where the key is #epochs and the value is ArchResults self.arch2infos_dict = collections.OrderedDict() self._avaliable_hps = set() for xkey in sorted(list(file_path_or_dict['arch2infos'].keys())): all_infos = file_path_or_dict['arch2infos'][xkey] hp2archres = collections.OrderedDict() for hp_key, results in all_infos.items(): hp2archres[hp_key] = ArchResults.create_from_state_dict(results) self._avaliable_hps.add(hp_key) # save the avaliable hyper-parameter self.arch2infos_dict[xkey] = hp2archres self.evaluated_indexes = set(file_path_or_dict['evaluated_indexes']) elif self.archive_dir is not None: benchmark_meta = pickle_load('{:}/meta.{:}'.format( self.archive_dir, PICKLE_EXT)) self.meta_archs = copy.deepcopy(benchmark_meta['meta_archs']) self.arch2infos_dict = collections.OrderedDict() self._avaliable_hps = set() self.evaluated_indexes = set() else: raise ValueError('file_path_or_dict [{:}] must be a dict or archive_dir ' 'must be set'.format(type(file_path_or_dict))) self.archstr2index = {} for idx, arch in enumerate(self.meta_archs): if arch in self.archstr2index: raise ValueError('This [{:}]-th arch {:} already in the ' 'dict ({:}).'.format( idx, arch, self.archstr2index[arch])) self.archstr2index[arch] = idx if self.verbose: print('{:} Create NATS-Bench (size) done with {:}/{:} architectures ' 'avaliable.'.format(time_string(), len(self.evaluated_indexes), len(self.meta_archs))) def query_info_str_by_arch(self, arch, hp: Text = '12'): """Query the information of a specific architecture. Args: arch: it can be an architecture index or an architecture string. hp: the hyperparamete indicator, could be 01, 12, or 90. The difference between these three configurations are the number of training epochs. Returns: ArchResults instance """ if self.verbose: print('{:} Call query_info_str_by_arch with arch={:}' 'and hp={:}'.format(time_string(), arch, hp)) return self._query_info_str_by_arch(arch, hp, print_information) def get_more_info(self, index, dataset, iepoch=None, hp: Text = '12', is_random: bool = True): """Return the metric for the `index`-th architecture. Args: index: the architecture index. 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: 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. Returns: a dict, where key is the metric name and value is its value. """ if self.verbose: print('{:} Call the get_more_info function with index={:}, dataset={:}, ' 'iepoch={:}, hp={:}, and is_random={:}.'.format( time_string(), index, dataset, iepoch, hp, is_random)) index = self.query_index_by_arch(index) # To avoid the input is a string or an instance of a arch object self._prepare_info(index) if index not in self.arch2infos_dict: raise ValueError('Did not find {:} from arch2infos_dict.'.format(index)) archresult = self.arch2infos_dict[index][str(hp)] # if randomly select one trial, select the seed at first if isinstance(is_random, bool) and is_random: seeds = archresult.get_dataset_seeds(dataset) is_random = random.choice(seeds) # collect the training information train_info = archresult.get_metrics( dataset, 'train', iepoch=iepoch, is_random=is_random) total = train_info['iepoch'] + 1 xinfo = { 'train-loss': train_info['loss'], 'train-accuracy': train_info['accuracy'], 'train-per-time': train_info['all_time'] / total, '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 Exception as unused_e: # pylint: disable=broad-except test_info = None valtest_info = None xinfo['comment'] = 'In this dict, train-loss/accuracy/time is the metric on the train set of CIFAR-10. The test-loss/accuracy/time is the performance of the CIFAR-10 test set after training on the train set by {:} epochs. The per-time and total-time indicate the per epoch and total time costs, respectively.'.format(hp) else: if dataset == 'cifar10': xinfo['comment'] = 'In this dict, train-loss/accuracy/time is the metric on the train+valid sets of CIFAR-10. The test-loss/accuracy/time is the performance of the CIFAR-10 test set after training on the train+valid sets by {:} epochs. The per-time and total-time indicate the per epoch and total time costs, respectively.'.format(hp) 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 Exception as unused_e: # pylint: disable=broad-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 Exception as unused_e: # pylint: disable=broad-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 Exception as unused_e: # pylint: disable=broad-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: """Print the information of a specific (or all) architecture(s).""" self._show(index, print_information)