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2020-10-15 19:56:20 +11:00
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lib/nats_bench/api_size.py
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@@ -1,65 +1,84 @@
#####################################################
# 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 (the name is NATS-sss-[version]-[md5].pickle.pbz2) #
# [2020.08.31] NATS-sss-v1_0-50262.pickle.pbz2 #
#####################################################################################
import os, copy, random, numpy as np
from typing import List, Text, Union, Dict, Optional
from collections import OrderedDict, defaultdict
from .api_utils import time_string
from .api_utils import pickle_load
from .api_utils import ArchResults
from .api_utils import NASBenchMetaAPI
from .api_utils import remap_dataset_set_names
from .api_utils import nats_is_dir
from .api_utils import nats_is_file
from .api_utils import PICKLE_EXT
# 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 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)]
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):
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)
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']))
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']))
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']))
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 size search space in NATS-Bench.
"""
class NATSsize(NASBenchMetaAPI):
"""This is the class for the API of size search space in NATS-Bench."""
""" 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, fast_mode: bool=False, verbose: bool=True):
self.ALL_BASE_NAMES = ALL_BASE_NAMES
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
@@ -67,25 +86,36 @@ class NATSsize(NASBenchMetaAPI):
self.reset_time()
if file_path_or_dict is None:
if self._fast_mode:
self._archive_dir = os.path.join(os.environ['TORCH_HOME'], '{:}-simple'.format(ALL_BASE_NAMES[-1]))
self._archive_dir = os.path.join(
os.environ['TORCH_HOME'], '{:}-simple'.format(ALL_BASE_NAMES[-1]))
else:
file_path_or_dict = os.path.join(os.environ['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))
file_path_or_dict = os.path.join(
os.environ['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))
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))
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))
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):
@@ -93,68 +123,95 @@ class NATSsize(NASBenchMetaAPI):
self.verbose = verbose
if isinstance(file_path_or_dict, dict):
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)
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'])
# This is a dict mapping each architecture to a dict, where the key is #epochs and the value is ArchResults
self.arch2infos_dict = OrderedDict()
# 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 = OrderedDict()
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))
benchmark_meta = pickle_load('{:}/meta.{:}'.format(
self.archive_dir, PICKLE_EXT))
self.meta_archs = copy.deepcopy(benchmark_meta['meta_archs'])
self.arch2infos_dict = OrderedDict()
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)))
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):
assert arch not in self.archstr2index, 'This [{:}]-th arch {:} already in the dict ({:}).'.format(idx, arch, self.archstr2index[arch])
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)))
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'):
""" This function is used to query the information of a specific architecture
'arch' can be an architecture index or an architecture string
When hp=01, the hyper-parameters used to train a model are in 'configs/nas-benchmark/hyper-opts/01E.config'
When hp=12, the hyper-parameters used to train a model are in 'configs/nas-benchmark/hyper-opts/12E.config'
When hp=90, the hyper-parameters used to train a model are in 'configs/nas-benchmark/hyper-opts/90E.config'
The difference between these three configurations are the number of training epochs.
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))
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: Text, iepoch=None, hp='12', is_random=True):
"""This function will return the metric for the `index`-th architecture
`dataset` indicates the dataset:
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` indicates the index of training epochs from 0 to 11/199.
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
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`
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))
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:
@@ -165,38 +222,47 @@ class NATSsize(NASBenchMetaAPI):
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)
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']}
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)
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 = 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
else:
try: # collect results on the proposed test set
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)
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 = 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:
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)
valtest_info = archresult.get_metrics(
dataset, 'ori-test', iepoch=iepoch, is_random=is_random)
else:
valtest_info = None
except:
except Exception as unused_e: # pylint: disable=broad-except
valtest_info = None
if valid_info is not None:
xinfo['valid-loss'] = valid_info['loss']
@@ -216,11 +282,5 @@ class NATSsize(NASBenchMetaAPI):
return xinfo
def show(self, index: int = -1) -> None:
"""
This function will print the information of a specific (or all) architecture(s).
:param index: If the index < 0: it will loop for all architectures and print their information one by one.
else: it will print the information of the 'index'-th architecture.
:return: nothing
"""
"""Print the information of a specific (or all) architecture(s)."""
self._show(index, print_information)