def __init__(self,
args: Namespace,
index=None,
trainer: SimpleTrainer = None,
load_path: str = None,
method: AbstractMethod = None,
**kwargs):
super().__init__(args, index=index, **kwargs)
# ensure all required parameters are available
if (method is None) and (trainer is not None):
method = trainer.get_method()
reg_kwargs = Register.get_my_kwargs(self.__class__)
if reg_kwargs.get('requires_trainer'):
assert isinstance(
trainer,
SimpleTrainer), "%s needs a trainer" % self.__class__.__name__
assert isinstance(
load_path, str
), "%s needs a path to load weights" % self.__class__.__name__
if reg_kwargs.get('requires_method'):
assert isinstance(
method,
AbstractMethod), "%s needs a method" % self.__class__.__name__
assert isinstance(method.get_network(), SearchUninasNetwork),\
"%s's method must use a search network" % self.__class__.__name__
self.method = method
self.trainer = trainer
self.load_path = load_path
def meta_args_to_add(cls) -> [MetaArgument]:
"""
list meta arguments to add to argparse for when this class is chosen,
classes specified in meta arguments may have their own respective arguments
"""
kwargs = Register.get_my_kwargs(cls)
aug_sets = Register.augmentation_sets.filter_match_all(on_images=kwargs.get('images'))
return super().meta_args_to_add() + [
MetaArgument('cls_augmentations', aug_sets, help_name='data augmentation'),
]
def meta_args_to_add(cls) -> [MetaArgument]:
"""
list meta arguments to add to argparse for when this class is chosen,
classes specified in meta arguments may have their own respective arguments
"""
kwargs = Register.get_my_kwargs(cls)
methods = Register.methods.filter_match_all(search=kwargs.get('search'))
return super().meta_args_to_add() + [
MetaArgument('cls_device', Register.devices_managers, help_name='device manager', allowed_num=1),
MetaArgument('cls_trainer', Register.trainers, help_name='trainer', allowed_num=1),
MetaArgument('cls_method', methods, help_name='method', allowed_num=1),
]
def meta_args_to_add(cls, num_optimizers=1, search=True) -> [MetaArgument]:
"""
list meta arguments to add to argparse for when this class is chosen,
classes specified in meta arguments may have their own respective arguments
"""
kwargs = Register.get_my_kwargs(cls)
metrics = Register.metrics.filter_match_all(
distill=kwargs.get('distill'))
criteria = Register.criteria.filter_match_all(
distill=kwargs.get('distill'))
networks = Register.networks.filter_match_all(search=search)
return super().meta_args_to_add() + [
MetaArgument('cls_data',
Register.data_sets,
help_name='data set',
allowed_num=1),
MetaArgument(
'cls_network', networks, help_name='network', allowed_num=1),
MetaArgument('cls_criterion',
criteria,
help_name='criterion',
allowed_num=1),
MetaArgument('cls_metrics', metrics, help_name='training metric'),
MetaArgument('cls_initializers',
Register.initializers,
help_name='weight initializer'),
MetaArgument('cls_regularizers',
Register.regularizers,
help_name='regularizer'),
MetaArgument('cls_optimizers',
Register.optimizers,
help_name='optimizer',
allow_duplicates=True,
allowed_num=num_optimizers,
use_index=True),
MetaArgument('cls_schedulers',
Register.schedulers,
help_name='scheduler',
allow_duplicates=True,
allowed_num=(0, num_optimizers),
use_index=True),
]
def num_classes(cls) -> int:
kwargs = Register.get_my_kwargs(cls)
assert kwargs.get('classification', False)
return cls.label_shape.num_features()
def is_classification(cls) -> bool:
kwargs = Register.get_my_kwargs(cls)
return kwargs.get('classification', False)
def is_on_images(cls) -> bool:
kwargs = Register.get_my_kwargs(cls)
return kwargs.get('images', False)
def is_single_path(cls) -> bool:
return Register.get_my_kwargs(cls).get('single_path', False)
def is_external(self) -> bool:
return Register.get_my_kwargs(self.__class__).get('external')
def is_tabular(self) -> bool:
return Register.get_my_kwargs(self.__class__).get('tabular', False)