本文整理匯總了Python中Dataset.Dataset.kwargs_update_from_config方法的典型用法代碼示例。如果您正苦於以下問題：Python Dataset.kwargs_update_from_config方法的具體用法？Python Dataset.kwargs_update_from_config怎麽用？Python Dataset.kwargs_update_from_config使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類Dataset.Dataset的用法示例。
在下文中一共展示了Dataset.kwargs_update_from_config方法的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: load_data
# 需要導入模塊: from Dataset import Dataset [as 別名]
# 或者: from Dataset.Dataset import kwargs_update_from_config [as 別名]
def load_data(config, cache_byte_size, files_config_key, **kwargs):
"""
:type config: Config
:type cache_byte_size: int
:type chunking: str
:type seq_ordering: str
:rtype: (Dataset,int)
:returns the dataset, and the cache byte size left over if we cache the whole dataset.
"""
if not config.has(files_config_key):
return None, 0
if config.is_typed(files_config_key) and isinstance(config.typed_value(files_config_key), dict):
new_kwargs = config.typed_value(files_config_key)
assert isinstance(new_kwargs, dict)
kwargs.update(new_kwargs)
if 'cache_byte_size' not in new_kwargs:
if kwargs.get('class', None) == 'HDFDataset':
kwargs["cache_byte_size"] = cache_byte_size
Dataset.kwargs_update_from_config(config, kwargs)
data = init_dataset(kwargs)
else:
config_str = config.value(files_config_key, "")
data = init_dataset_via_str(config_str, config=config, cache_byte_size=cache_byte_size, **kwargs)
cache_leftover = 0
if isinstance(data, HDFDataset):
cache_leftover = data.definite_cache_leftover
return data, cache_leftover示例2: load_data
# 需要導入模塊: from Dataset import Dataset [as 別名]
# 或者: from Dataset.Dataset import kwargs_update_from_config [as 別名]
def load_data(config, cache_byte_size, files_config_key, **kwargs):
"""
:param Config config:
:param int cache_byte_size:
:param str files_config_key: such as "train" or "dev"
:param kwargs: passed on to init_dataset() or init_dataset_via_str()
:rtype: (Dataset,int)
:returns the dataset, and the cache byte size left over if we cache the whole dataset.
"""
if not config.bool_or_other(files_config_key, None):
return None, 0
kwargs = kwargs.copy()
kwargs.setdefault("name", files_config_key)
if config.is_typed(files_config_key) and isinstance(config.typed_value(files_config_key), dict):
config_opts = config.typed_value(files_config_key)
assert isinstance(config_opts, dict)
kwargs.update(config_opts)
if 'cache_byte_size' not in config_opts:
if kwargs.get('class', None) == 'HDFDataset':
kwargs["cache_byte_size"] = cache_byte_size
Dataset.kwargs_update_from_config(config, kwargs)
data = init_dataset(kwargs)
else:
config_str = config.value(files_config_key, "")
data = init_dataset_via_str(config_str, config=config, cache_byte_size=cache_byte_size, **kwargs)
cache_leftover = 0
if isinstance(data, HDFDataset):
cache_leftover = data.definite_cache_leftover
return data, cache_leftover示例3: benchmark
# 需要導入模塊: from Dataset import Dataset [as 別名]
# 或者: from Dataset.Dataset import kwargs_update_from_config [as 別名]
def benchmark(lstm_unit, use_gpu):
"""
:param str lstm_unit: e.g. "LSTMBlock", one of LstmCellTypes
:param bool use_gpu:
:return: runtime in seconds of the training itself, excluding initialization
:rtype: float
"""
device = {True: "GPU", False: "CPU"}[use_gpu]
key = "%s:%s" % (device, lstm_unit)
print(">>> Start benchmark for %s." % key)
config = Config()
config.update(make_config_dict(lstm_unit=lstm_unit, use_gpu=use_gpu))
dataset_kwargs = config.typed_value("train")
Dataset.kwargs_update_from_config(config, dataset_kwargs)
dataset = init_dataset(dataset_kwargs)
engine = Engine(config=config)
engine.init_train_from_config(config=config, train_data=dataset)
print(">>> Start training now for %s." % key)
start_time = time.time()
engine.train()
runtime = time.time() - start_time
print(">>> Runtime of %s: %s" % (key, hms_fraction(runtime)))
engine.finalize()
return runtime