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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.08 #
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# 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-tss-[version]-[md5].pickle.pbz2) #
# [2020.08.31] NATS-tss-v1_0-3ffb9.pickle.pbz2 #
##############################################################################
# pylint: disable=line-too-long
"""The API for topology search space in NATS-Bench."""
import collections
import copy
import os
import random
from typing import Any, Dict, List, Optional, Text, Union
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
import numpy as np
ALL_BASE_NAMES = ['NATS-tss-v1_0-3ffb9']
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')
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
class NATStopology(NASBenchMetaAPI):
"""This is the class for the API of topology 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 = 'topology'
self._fast_mode = fast_mode
self._archive_dir = None
self._full_train_epochs = 200
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 (topology) 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 (topology) 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 (topology) 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 12 or 200. 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."""
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 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 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 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[Any]:
"""Shows how to read the string-based architecture encoding.
Args:
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|
Returns:
a list of tuple, contains multiple (op, input_node_index) pairs.
[USAGE]
It is the same as the `str2structure` func in AutoDL-Projects:
`github.com/D-X-Y/AutoDL-Projects/lib/models/cell_searchs/genotypes.py`
```
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 unused_i, node_str in enumerate(node_strs):
inputs = list(filter(lambda x: x != '', node_str.split('|'))) # pylint: disable=g-explicit-bool-comparison
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:
"""Convert the string-based architecture encoding to the encoding strategy in NAS-Bench-101.
Args:
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 topology search space for NATS-BENCH.
the default value should be be consistent with this line https://github.com/D-X-Y/AutoDL-Projects/blob/main/lib/models/cell_operations.py#L24
Returns:
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 the topology search space in NATS-BENCH, 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('|'))) # pylint: disable=g-explicit-bool-comparison
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