def query_info_str_by_arch(self, arch, hp: Text = '12'):
"""Query the information of a specific architecture.
Args:
arch: it can be an architecture index or an architecture string.
hp: the hyperparamete indicator, could be 01, 12, or 90. The difference
between these three configurations are the number of training epochs.
Returns:
ArchResults instance
"""
if self.verbose:
print('{:} Call query_info_str_by_arch with arch={:}'
'and hp={:}'.format(time_string(), arch, hp))
return self._query_info_str_by_arch(arch, hp, print_information)
def __init__(self,
file_path_or_dict: Optional[Union[Text, Dict[Text, Any]]] = None,
fast_mode: bool = False,
verbose: bool = True):
"""The initialization function that takes the dataset file path (or a dict loaded from that path) as input."""
self._all_base_names = ALL_BASE_NAMES
self.filename = None
self._search_space_name = 'size'
self._fast_mode = fast_mode
self._archive_dir = None
self.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]))
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))
if isinstance(file_path_or_dict, str):
file_path_or_dict = str(file_path_or_dict)
if verbose:
print('{:} Try to create the NATS-Bench (size) api '
'from {:} with fast_mode={:}'.format(
time_string(), file_path_or_dict, fast_mode))
if not nats_is_file(file_path_or_dict) and not nats_is_dir(
file_path_or_dict):
raise ValueError('{:} is neither a file or a dir.'.format(
file_path_or_dict))
self.filename = os.path.basename(file_path_or_dict)
if fast_mode:
if nats_is_file(file_path_or_dict):
raise ValueError('fast_mode={:} must feed the path for directory '
': {:}'.format(fast_mode, file_path_or_dict))
else:
self._archive_dir = file_path_or_dict
else:
if nats_is_dir(file_path_or_dict):
raise ValueError('fast_mode={:} must feed the path for file '
': {:}'.format(fast_mode, file_path_or_dict))
else:
file_path_or_dict = pickle_load(file_path_or_dict)
elif isinstance(file_path_or_dict, dict):
file_path_or_dict = copy.deepcopy(file_path_or_dict)
self.verbose = verbose
if isinstance(file_path_or_dict, dict):
keys = ('meta_archs', 'arch2infos', 'evaluated_indexes')
for key in keys:
if key not in file_path_or_dict:
raise ValueError('Can not find key[{:}] in the dict'.format(key))
self.meta_archs = copy.deepcopy(file_path_or_dict['meta_archs'])
# NOTE(xuanyidong): This is a dict mapping each architecture to a dict,
# where the key is #epochs and the value is ArchResults
self.arch2infos_dict = collections.OrderedDict()
self._avaliable_hps = set()
for xkey in sorted(list(file_path_or_dict['arch2infos'].keys())):
all_infos = file_path_or_dict['arch2infos'][xkey]
hp2archres = collections.OrderedDict()
for hp_key, results in all_infos.items():
hp2archres[hp_key] = ArchResults.create_from_state_dict(results)
self._avaliable_hps.add(hp_key) # save the avaliable hyper-parameter
self.arch2infos_dict[xkey] = hp2archres
self.evaluated_indexes = set(file_path_or_dict['evaluated_indexes'])
elif self.archive_dir is not None:
benchmark_meta = pickle_load('{:}/meta.{:}'.format(
self.archive_dir, PICKLE_EXT))
self.meta_archs = copy.deepcopy(benchmark_meta['meta_archs'])
self.arch2infos_dict = collections.OrderedDict()
self._avaliable_hps = set()
self.evaluated_indexes = set()
else:
raise ValueError('file_path_or_dict [{:}] must be a dict or archive_dir '
'must be set'.format(type(file_path_or_dict)))
self.archstr2index = {}
for idx, arch in enumerate(self.meta_archs):
if arch in self.archstr2index:
raise ValueError('This [{:}]-th arch {:} already in the '
'dict ({:}).'.format(
idx, arch, self.archstr2index[arch]))
self.archstr2index[arch] = idx
if self.verbose:
print('{:} Create NATS-Bench (size) done with {:}/{:} architectures '
'avaliable.'.format(time_string(),
len(self.evaluated_indexes),
len(self.meta_archs)))
def get_more_info(self,
index,
dataset,
iepoch=None,
hp: Text = '12',
is_random: bool = True):
"""Return the metric for the `index`-th architecture.
Args:
index: the architecture index.
dataset:
'cifar10-valid' : using the proposed train set of CIFAR-10 as the training set
'cifar10' : using the proposed train+valid set of CIFAR-10 as the training set
'cifar100' : using the proposed train set of CIFAR-100 as the training set
'ImageNet16-120' : using the proposed train set of ImageNet-16-120 as the training set
iepoch: the index of training epochs from 0 to 11/199.
When iepoch=None, it will return the metric for the last training epoch
When iepoch=11, it will return the metric for the 11-th training epoch (starting from 0)
hp: indicates different hyper-parameters for training
When hp=01, it trains the network with 01 epochs and the LR decayed from 0.1 to 0 within 01 epochs
When hp=12, it trains the network with 01 epochs and the LR decayed from 0.1 to 0 within 12 epochs
When hp=90, it trains the network with 01 epochs and the LR decayed from 0.1 to 0 within 90 epochs
is_random:
When is_random=True, the performance of a random architecture will be returned
When is_random=False, the performanceo of all trials will be averaged.
Returns:
a dict, where key is the metric name and value is its value.
"""
if self.verbose:
print('{:} Call the get_more_info function with index={:}, dataset={:}, '
'iepoch={:}, hp={:}, and is_random={:}.'.format(
time_string(), index, dataset, iepoch, hp, is_random))
index = self.query_index_by_arch(index) # To avoid the input is a string or an instance of a arch object
self._prepare_info(index)
if index not in self.arch2infos_dict:
raise ValueError('Did not find {:} from arch2infos_dict.'.format(index))
archresult = self.arch2infos_dict[index][str(hp)]
# if randomly select one trial, select the seed at first
if isinstance(is_random, bool) and is_random:
seeds = archresult.get_dataset_seeds(dataset)
is_random = random.choice(seeds)
# collect the training information
train_info = archresult.get_metrics(
dataset, 'train', iepoch=iepoch, is_random=is_random)
total = train_info['iepoch'] + 1
xinfo = {
'train-loss': train_info['loss'],
'train-accuracy': train_info['accuracy'],
'train-per-time': train_info['all_time'] / total,
'train-all-time': train_info['all_time']
}
# collect the evaluation information
if dataset == 'cifar10-valid':
valid_info = archresult.get_metrics(
dataset, 'x-valid', iepoch=iepoch, is_random=is_random)
try:
test_info = archresult.get_metrics(
dataset, 'ori-test', iepoch=iepoch, is_random=is_random)
except Exception as unused_e: # pylint: disable=broad-except
test_info = None
valtest_info = None
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 Exception as unused_e: # pylint: disable=broad-except
test_info = None
try: # collect results on the proposed validation set
valid_info = archresult.get_metrics(
dataset, 'x-valid', iepoch=iepoch, is_random=is_random)
except Exception as unused_e: # pylint: disable=broad-except
valid_info = None
try:
if dataset != 'cifar10':
valtest_info = archresult.get_metrics(
dataset, 'ori-test', iepoch=iepoch, is_random=is_random)
else:
valtest_info = None
except Exception as unused_e: # pylint: disable=broad-except
valtest_info = None
if valid_info is not None:
xinfo['valid-loss'] = valid_info['loss']
xinfo['valid-accuracy'] = valid_info['accuracy']
xinfo['valid-per-time'] = valid_info['all_time'] / total
xinfo['valid-all-time'] = valid_info['all_time']
if test_info is not None:
xinfo['test-loss'] = test_info['loss']
xinfo['test-accuracy'] = test_info['accuracy']
xinfo['test-per-time'] = test_info['all_time'] / total
xinfo['test-all-time'] = test_info['all_time']
if valtest_info is not None:
xinfo['valtest-loss'] = valtest_info['loss']
xinfo['valtest-accuracy'] = valtest_info['accuracy']
xinfo['valtest-per-time'] = valtest_info['all_time'] / total
xinfo['valtest-all-time'] = valtest_info['all_time']
return xinfo
def 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 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
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 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
