751 lines
36 KiB
Python
751 lines
36 KiB
Python
#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
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############################################################################################
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# NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search, ICLR 2020 #
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############################################################################################
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# In this Python file, we define NASBenchMetaAPI, the abstract class for benchmark APIs.
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# We also define the class ArchResults, which contains all information of a single architecture trained by one kind of hyper-parameters on three datasets.
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# We also define the class ResultsCount, which contains all information of a single trial for a single architecture.
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############################################################################################
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# History:
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# [2020.06.30] The first version.
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#
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import os, abc, copy, random, torch, numpy as np
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from pathlib import Path
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from typing import List, Text, Union, Dict, Optional
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from collections import OrderedDict, defaultdict
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def remap_dataset_set_names(dataset, metric_on_set, verbose=False):
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"""re-map the metric_on_set to internal keys"""
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if verbose:
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print('Call internal function _remap_dataset_set_names with dataset={:} and metric_on_set={:}'.format(dataset, metric_on_set))
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if dataset == 'cifar10' and metric_on_set == 'valid':
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dataset, metric_on_set = 'cifar10-valid', 'x-valid'
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elif dataset == 'cifar10' and metric_on_set == 'test':
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dataset, metric_on_set = 'cifar10', 'ori-test'
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elif dataset == 'cifar10' and metric_on_set == 'train':
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dataset, metric_on_set = 'cifar10', 'train'
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elif (dataset == 'cifar100' or dataset == 'ImageNet16-120') and metric_on_set == 'valid':
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metric_on_set = 'x-valid'
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elif (dataset == 'cifar100' or dataset == 'ImageNet16-120') and metric_on_set == 'test':
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metric_on_set = 'x-test'
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if verbose:
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print(' return dataset={:} and metric_on_set={:}'.format(dataset, metric_on_set))
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return dataset, metric_on_set
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class NASBenchMetaAPI(metaclass=abc.ABCMeta):
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@abc.abstractmethod
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def __init__(self, file_path_or_dict: Optional[Union[Text, Dict]]=None, verbose: bool=True):
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"""The initialization function that takes the dataset file path (or a dict loaded from that path) as input."""
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def __getitem__(self, index: int):
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return copy.deepcopy(self.meta_archs[index])
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def arch(self, index: int):
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"""Return the topology structure of the `index`-th architecture."""
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if self.verbose:
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print('Call the arch function with index={:}'.format(index))
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assert 0 <= index < len(self.meta_archs), 'invalid index : {:} vs. {:}.'.format(index, len(self.meta_archs))
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return copy.deepcopy(self.meta_archs[index])
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def __len__(self):
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return len(self.meta_archs)
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def __repr__(self):
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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))
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@property
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def avaliable_hps(self):
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return list(copy.deepcopy(self._avaliable_hps))
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@property
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def used_time(self):
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return self._used_time
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def reset_time(self):
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self._used_time = 0
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def simulate_train_eval(self, arch, dataset, iepoch=None, hp='12', account_time=True):
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index = self.query_index_by_arch(arch)
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all_names = ('cifar10', 'cifar100', 'ImageNet16-120')
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assert dataset in all_names, 'Invalid dataset name : {:} vs {:}'.format(dataset, all_names)
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if dataset == 'cifar10':
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info = self.get_more_info(index, 'cifar10-valid', iepoch=iepoch, hp=hp, is_random=True)
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else:
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info = self.get_more_info(index, dataset, iepoch=iepoch, hp=hp, is_random=True)
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valid_acc, time_cost = info['valid-accuracy'], info['train-all-time'] + info['valid-per-time']
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latency = self.get_latency(index, dataset)
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if account_time:
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self._used_time += time_cost
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return valid_acc, latency, time_cost, self._used_time
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def random(self):
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"""Return a random index of all architectures."""
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return random.randint(0, len(self.meta_archs)-1)
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def query_index_by_arch(self, arch):
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""" This function is used to query the index of an architecture in the search space.
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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|';
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or an instance that has the 'tostr' function that can generate the architecture string;
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or it is directly an architecture index, in this case, we will check whether it is valid or not.
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This function will return the index.
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If return -1, it means this architecture is not in the search space.
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Otherwise, it will return an int in [0, the-number-of-candidates-in-the-search-space).
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"""
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if self.verbose:
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print('Call query_index_by_arch with arch={:}'.format(arch))
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if isinstance(arch, int):
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if 0 <= arch < len(self):
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return arch
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else:
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raise ValueError('Invalid architecture index {:} vs [{:}, {:}].'.format(arch, 0, len(self)))
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elif isinstance(arch, str):
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if arch in self.archstr2index: arch_index = self.archstr2index[ arch ]
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else : arch_index = -1
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elif hasattr(arch, 'tostr'):
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if arch.tostr() in self.archstr2index: arch_index = self.archstr2index[ arch.tostr() ]
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else : arch_index = -1
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else: arch_index = -1
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return arch_index
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def query_by_arch(self, arch, hp):
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# This is to make the current version be compatible with the old version.
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return self.query_info_str_by_arch(arch, hp)
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@abc.abstractmethod
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def reload(self, archive_root: Text = None, index: int = None):
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"""Overwrite all information of the 'index'-th architecture in the search space, where the data will be loaded from 'archive_root'.
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If index is None, overwrite all ckps.
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"""
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def clear_params(self, index: int, hp: Optional[Text]=None):
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"""Remove the architecture's weights to save memory.
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:arg
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index: the index of the target architecture
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hp: a flag to controll how to clear the parameters.
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-- None: clear all the weights in '01'/'12'/'90', which indicates the number of training epochs.
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-- '01' or '12' or '90': clear all the weights in arch2infos_dict[index][hp].
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"""
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if self.verbose:
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print('Call clear_params with index={:} and hp={:}'.format(index, hp))
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if hp is None:
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for key, result in self.arch2infos_dict[index].items():
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result.clear_params()
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else:
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if str(hp) not in self.arch2infos_dict[index]:
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raise ValueError('The {:}-th architecture only has hyper-parameters of {:} instead of {:}.'.format(index, list(self.arch2infos_dict[index].keys()), hp))
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self.arch2infos_dict[index][str(hp)].clear_params()
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@abc.abstractmethod
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def query_info_str_by_arch(self, arch, hp: Text='12'):
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"""This function is used to query the information of a specific architecture."""
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def _query_info_str_by_arch(self, arch, hp: Text='12', print_information=None):
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arch_index = self.query_index_by_arch(arch)
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if arch_index in self.arch2infos_dict:
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if hp not in self.arch2infos_dict[arch_index]:
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raise ValueError('The {:}-th architecture only has hyper-parameters of {:} instead of {:}.'.format(index, list(self.arch2infos_dict[arch_index].keys()), hp))
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info = self.arch2infos_dict[arch_index][hp]
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strings = print_information(info, 'arch-index={:}'.format(arch_index))
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return '\n'.join(strings)
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else:
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print ('Find this arch-index : {:}, but this arch is not evaluated.'.format(arch_index))
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return None
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def query_meta_info_by_index(self, arch_index, hp: Text = '12'):
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"""Return the ArchResults for the 'arch_index'-th architecture. This function is similar to query_by_index."""
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if self.verbose:
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print('Call query_meta_info_by_index with arch_index={:}, hp={:}'.format(arch_index, hp))
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if arch_index in self.arch2infos_dict:
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if hp not in self.arch2infos_dict[arch_index]:
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raise ValueError('The {:}-th architecture only has hyper-parameters of {:} instead of {:}.'.format(arch_index, list(self.arch2infos_dict[arch_index].keys()), hp))
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info = self.arch2infos_dict[arch_index][hp]
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else:
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raise ValueError('arch_index [{:}] does not in arch2infos'.format(arch_index))
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return copy.deepcopy(info)
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def query_by_index(self, arch_index: int, dataname: Union[None, Text] = None, hp: Text = '12'):
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""" This 'query_by_index' function is used to query information with the training of 01 epochs, 12 epochs, 90 epochs, or 200 epochs.
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------
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If hp=01, we train the model by 01 epochs (see config in configs/nas-benchmark/hyper-opts/01E.config)
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If hp=12, we train the model by 01 epochs (see config in configs/nas-benchmark/hyper-opts/12E.config)
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If hp=90, we train the model by 01 epochs (see config in configs/nas-benchmark/hyper-opts/90E.config)
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If hp=200, we train the model by 01 epochs (see config in configs/nas-benchmark/hyper-opts/200E.config)
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------
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If dataname is None, return the ArchResults
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else, return a dict with all trials on that dataset (the key is the seed)
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Options are 'cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120'.
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-- cifar10-valid : training the model on the CIFAR-10 training set.
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-- cifar10 : training the model on the CIFAR-10 training + validation set.
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-- cifar100 : training the model on the CIFAR-100 training set.
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-- ImageNet16-120 : training the model on the ImageNet16-120 training set.
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"""
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if self.verbose:
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print('Call query_by_index with arch_index={:}, dataname={:}, hp={:}'.format(arch_index, dataname, hp))
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info = self.query_meta_info_by_index(arch_index, hp)
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if dataname is None: return info
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else:
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if dataname not in info.get_dataset_names():
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raise ValueError('invalid dataset-name : {:} vs. {:}'.format(dataname, info.get_dataset_names()))
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return info.query(dataname)
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def find_best(self, dataset, metric_on_set, FLOP_max=None, Param_max=None, hp: Text = '12'):
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"""Find the architecture with the highest accuracy based on some constraints."""
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if self.verbose:
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print('Call find_best with dataset={:}, metric_on_set={:}, hp={:} | with #FLOPs < {:} and #Params < {:}'.format(dataset, metric_on_set, hp, FLOP_max, Param_max))
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dataset, metric_on_set = remap_dataset_set_names(dataset, metric_on_set, self.verbose)
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best_index, highest_accuracy = -1, None
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for i, arch_index in enumerate(self.evaluated_indexes):
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arch_info = self.arch2infos_dict[arch_index][hp]
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info = arch_info.get_compute_costs(dataset) # the information of costs
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flop, param, latency = info['flops'], info['params'], info['latency']
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if FLOP_max is not None and flop > FLOP_max : continue
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if Param_max is not None and param > Param_max: continue
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xinfo = arch_info.get_metrics(dataset, metric_on_set) # the information of loss and accuracy
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loss, accuracy = xinfo['loss'], xinfo['accuracy']
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if best_index == -1:
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best_index, highest_accuracy = arch_index, accuracy
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elif highest_accuracy < accuracy:
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best_index, highest_accuracy = arch_index, accuracy
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if self.verbose:
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print(' the best architecture : [{:}] {:} with accuracy={:.3f}%'.format(best_index, self.arch(best_index), highest_accuracy))
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return best_index, highest_accuracy
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def get_net_param(self, index, dataset, seed: Optional[int], hp: Text = '12'):
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"""
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This function is used to obtain the trained weights of the `index`-th architecture on `dataset` with the seed of `seed`
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Args [seed]:
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-- None : return a dict containing the trained weights of all trials, where each key is a seed and its corresponding value is the weights.
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-- a interger : return the weights of a specific trial, whose seed is this interger.
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Args [hp]:
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-- 01 : train the model by 01 epochs
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-- 12 : train the model by 12 epochs
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-- 90 : train the model by 90 epochs
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-- 200 : train the model by 200 epochs
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"""
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if self.verbose:
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print('Call the get_net_param function with index={:}, dataset={:}, seed={:}, hp={:}'.format(index, dataset, seed, hp))
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info = self.query_meta_info_by_index(index, hp)
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return info.get_net_param(dataset, seed)
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def get_net_config(self, index: int, dataset: Text):
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"""
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This function is used to obtain the configuration for the `index`-th architecture on `dataset`.
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Args [dataset] (4 possible options):
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-- cifar10-valid : training the model on the CIFAR-10 training set.
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-- cifar10 : training the model on the CIFAR-10 training + validation set.
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-- cifar100 : training the model on the CIFAR-100 training set.
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-- ImageNet16-120 : training the model on the ImageNet16-120 training set.
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This function will return a dict.
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========= Some examlpes for using this function:
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config = api.get_net_config(128, 'cifar10')
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"""
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if self.verbose:
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print('Call the get_net_config function with index={:}, dataset={:}.'.format(index, dataset))
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if index in self.arch2infos_dict:
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info = self.arch2infos_dict[index]
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else:
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raise ValueError('The arch_index={:} is not in arch2infos_dict.'.format(arch_index))
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info = next(iter(info.values()))
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results = info.query(dataset, None)
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results = next(iter(results.values()))
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return results.get_config(None)
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def get_cost_info(self, index: int, dataset: Text, hp: Text = '12') -> Dict[Text, float]:
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"""To obtain the cost metric for the `index`-th architecture on a dataset."""
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if self.verbose:
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print('Call the get_cost_info function with index={:}, dataset={:}, and hp={:}.'.format(index, dataset, hp))
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info = self.query_meta_info_by_index(index, hp)
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return info.get_compute_costs(dataset)
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def get_latency(self, index: int, dataset: Text, hp: Text = '12') -> float:
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"""
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To obtain the latency of the network (by default it will return the latency with the batch size of 256).
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:param index: the index of the target architecture
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:param dataset: the dataset name (cifar10-valid, cifar10, cifar100, ImageNet16-120)
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:return: return a float value in seconds
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"""
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if self.verbose:
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print('Call the get_latency function with index={:}, dataset={:}, and hp={:}.'.format(index, dataset, hp))
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cost_dict = self.get_cost_info(index, dataset, hp)
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return cost_dict['latency']
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@abc.abstractmethod
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def show(self, index=-1):
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"""This function will print the information of a specific (or all) architecture(s)."""
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def _show(self, index=-1, print_information=None) -> None:
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"""
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This function will print the information of a specific (or all) architecture(s).
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:param index: If the index < 0: it will loop for all architectures and print their information one by one.
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else: it will print the information of the 'index'-th architecture.
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:return: nothing
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"""
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if index < 0: # show all architectures
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print(self)
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for i, idx in enumerate(self.evaluated_indexes):
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print('\n' + '-' * 10 + ' The ({:5d}/{:5d}) {:06d}-th architecture! '.format(i, len(self.evaluated_indexes), idx) + '-'*10)
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print('arch : {:}'.format(self.meta_archs[idx]))
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for key, result in self.arch2infos_dict[index].items():
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strings = print_information(result)
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print('>' * 40 + ' {:03d} epochs '.format(result.get_total_epoch()) + '>' * 40)
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print('\n'.join(strings))
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print('<' * 40 + '------------' + '<' * 40)
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else:
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if 0 <= index < len(self.meta_archs):
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if index not in self.evaluated_indexes: print('The {:}-th architecture has not been evaluated or not saved.'.format(index))
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else:
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arch_info = self.arch2infos_dict[index]
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for key, result in self.arch2infos_dict[index].items():
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strings = print_information(result)
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print('>' * 40 + ' {:03d} epochs '.format(result.get_total_epoch()) + '>' * 40)
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print('\n'.join(strings))
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print('<' * 40 + '------------' + '<' * 40)
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else:
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print('This index ({:}) is out of range (0~{:}).'.format(index, len(self.meta_archs)))
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def statistics(self, dataset: Text, hp: Union[Text, int]) -> Dict[int, int]:
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"""This function will count the number of total trials."""
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if self.verbose:
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print('Call the statistics function with dataset={:} and hp={:}.'.format(dataset, hp))
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valid_datasets = ['cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120']
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if dataset not in valid_datasets:
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raise ValueError('{:} not in {:}'.format(dataset, valid_datasets))
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nums, hp = defaultdict(lambda: 0), str(hp)
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for index in range(len(self)):
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archInfo = self.arch2infos_dict[index][hp]
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dataset_seed = archInfo.dataset_seed
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if dataset not in dataset_seed:
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nums[0] += 1
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else:
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nums[len(dataset_seed[dataset])] += 1
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return dict(nums)
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class ArchResults(object):
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def __init__(self, arch_index, arch_str):
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self.arch_index = int(arch_index)
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self.arch_str = copy.deepcopy(arch_str)
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self.all_results = dict()
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self.dataset_seed = dict()
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self.clear_net_done = False
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def get_compute_costs(self, dataset):
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x_seeds = self.dataset_seed[dataset]
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results = [self.all_results[ (dataset, seed) ] for seed in x_seeds]
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flops = [result.flop for result in results]
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params = [result.params for result in results]
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latencies = [result.get_latency() for result in results]
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latencies = [x for x in latencies if x > 0]
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mean_latency = np.mean(latencies) if len(latencies) > 0 else None
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time_infos = defaultdict(list)
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for result in results:
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time_info = result.get_times()
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for key, value in time_info.items(): time_infos[key].append( value )
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info = {'flops' : np.mean(flops),
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'params' : np.mean(params),
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'latency': mean_latency}
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for key, value in time_infos.items():
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if len(value) > 0 and value[0] is not None:
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info[key] = np.mean(value)
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else: info[key] = None
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return info
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def get_metrics(self, dataset, setname, iepoch=None, is_random=False):
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"""
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This `get_metrics` function is used to obtain obtain the loss, accuracy, etc information on a specific dataset.
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If not specify, each set refer to the proposed split in NAS-Bench-201 paper.
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If some args return None or raise error, then it is not avaliable.
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========================================
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Args [dataset] (4 possible options):
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-- cifar10-valid : training the model on the CIFAR-10 training set.
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-- cifar10 : training the model on the CIFAR-10 training + validation set.
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-- cifar100 : training the model on the CIFAR-100 training set.
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-- ImageNet16-120 : training the model on the ImageNet16-120 training set.
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Args [setname] (each dataset has different setnames):
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-- When dataset = cifar10-valid, you can use 'train', 'x-valid', 'ori-test'
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------ 'train' : the metric on the training set.
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------ '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:
|
|
xkey = (dataset, seed)
|
|
if xkey in self.all_results:
|
|
return self.all_results[xkey].get_net_param()
|
|
else:
|
|
raise ValueError('key={:} not in {:}'.format(xkey, list(self.all_results.keys())))
|
|
|
|
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)
|
|
def _internal_query(xname):
|
|
if isinstance(self.eval_times,dict) and len(self.eval_times) > 0:
|
|
xtime = self.eval_times['{:}@{:}'.format(xname, iepoch)]
|
|
atime = sum([self.eval_times['{:}@{:}'.format(xname, i)] for i in range(iepoch+1)])
|
|
else:
|
|
xtime, atime = None, None
|
|
return {'iepoch' : iepoch,
|
|
'loss' : self.eval_losses['{:}@{:}'.format(xname, iepoch)],
|
|
'accuracy': self.eval_acc1es['{:}@{:}'.format(xname, iepoch)],
|
|
'cur_time': xtime,
|
|
'all_time': atime}
|
|
if name == 'valid':
|
|
return _internal_query('x-valid')
|
|
else:
|
|
return _internal_query(name)
|
|
|
|
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
|