def load_from_meta(meta: dict) -> Component:
if 'load_path' in meta:
return load_from_meta_file(meta['load_path'])
cls = meta.get('class_path', None) or meta.get('classpath', None)
assert cls, f'{meta} doesn\'t contain classpath field'
cls = str_to_type(cls)
return cls.from_config(meta)
def from_meta(meta: dict, **kwargs):
"""
Parameters
----------
meta
kwargs
Returns
-------
KerasComponent
"""
cls = str_to_type(meta['class_path'])
obj: KerasComponent = cls()
assert 'load_path' in meta, f'{meta} doesn\'t contain load_path field'
obj.load(meta['load_path'])
return obj
def from_config(cls, config: dict):
"""
Args:
config:
kwargs:
config: dict:
Returns:
"""
cls = config.get('classpath', None)
assert cls, f'{config} doesn\'t contain classpath field'
cls = str_to_type(cls)
config = dict(config)
config.pop('classpath')
return cls(**config)
def _load_vocabs(vd, vocabs: dict, vocab_cls=Vocab):
"""
Args:
vd:
vocabs:
vocab_cls: Default class for the new vocab
"""
for key, value in vocabs.items():
if 'idx_to_token' in value:
cls = value.get('type', None)
if cls:
cls = str_to_type(cls)
else:
cls = vocab_cls
vocab = cls()
vocab.copy_from(value)
vd[key] = vocab
else: # nested Vocab
# noinspection PyTypeChecker
vd[key] = nested = VocabDict()
VocabDict._load_vocabs(nested, value, vocab_cls)
def from_config(config: dict, **kwargs):
"""Build an object from config.
Args:
config: A ``dict`` holding parameters for its constructor. It has to contain a `classpath` key,
which has a classpath str as its value. ``classpath`` will determine the type of object
being deserialized.
kwargs: Arguments not used.
Returns: A deserialized object.
"""
cls = config.get('classpath', None)
assert cls, f'{config} doesn\'t contain classpath field'
cls = str_to_type(cls)
deserialized_config = dict(config)
for k, v in config.items():
if isinstance(v, dict) and 'classpath' in v:
deserialized_config[k] = Configurable.from_config(v)
if cls.from_config == Configurable.from_config:
deserialized_config.pop('classpath')
return cls(**deserialized_config)
else:
return cls.from_config(deserialized_config)
def from_config(meta: dict, **kwargs):
cls = str_to_type(meta['classpath'])
component = load_from_meta(meta['component'])
return cls(component, meta['input_key'], meta['output_key'], **meta['kwargs'])
def from_config(meta: dict, **kwargs):
cls = str_to_type(meta['classpath'])
function = meta['function']
function = object_from_classpath(function)
return cls(function)
