275 lines
16 KiB
Python
275 lines
16 KiB
Python
|
#####################################################
|
||
|
|
# 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 our benchmark, while for the future benchmark file, please follow news from NATS-Bench (an extended version of 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
|
||
|
|
self.reset_time()
|
||
|
|
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()
|
||
|
|
self._avaliable_hps = set(['12', '200'])
|
||
|
|
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))
|
||
|
|
return 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, 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))
|
||
|
|
index = self.query_index_by_arch(index) # To avoid the input is a string or an instance of a arch object
|
||
|
|
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 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 if valid_info['all_time'] is not None else None
|
||
|
|
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 if test_info['all_time'] is not None else None
|
||
|
|
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 if valtest_info['all_time'] is not None else None
|
||
|
|
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
|
||
|
|
|