def __init__(
self,
params: Union[str, List[str]],
trainable: bool,
params_strict: bool = True,
start_epoch: float = -1.0,
end_epoch: float = -1.0,
):
super(TrainableParamsModifier, self).__init__(
start_epoch=-1,
end_epoch=-1,
end_comparator=-1,
)
self._params = self._validate_params(params)
self._layer_names = [get_layer_name_from_param(p) for p in self._params]
self._trainable = convert_to_bool(trainable)
self._params_strict = convert_to_bool(params_strict)
self._vars_to_trainable_orig = {}
self.validate()
def __init__(
self,
params: Union[str, List[str]],
init_sparsity: float,
final_sparsity: float,
start_epoch: float,
end_epoch: float,
update_frequency: float,
inter_func: str = "cubic",
log_types: Union[str, List[str]] = ALL_TOKEN,
mask_type: Union[str, List[int]] = "unstructured",
leave_enabled: bool = True,
):
super(GMPruningModifier, self).__init__(
log_types=log_types,
start_epoch=start_epoch,
min_start=-1.0,
end_epoch=end_epoch,
min_end=0.0,
end_comparator=1,
update_frequency=update_frequency,
min_frequency=-1.0,
)
self._params = validate_str_iterable(params, "{} for params".format(
self.__class__.__name__)) # type: List[str]
self._layer_names = [
get_layer_name_from_param(p) for p in self._params
]
self._init_sparsity = init_sparsity
self._final_sparsity = final_sparsity
self._leave_enabled = convert_to_bool(leave_enabled)
self._inter_func = inter_func
self._mask_type = mask_type
self._leave_enabled = convert_to_bool(leave_enabled)
self._prune_op_vars = None
self._update_ready = None
self._sparsity = None
self._mask_initializer = None
self._masked_layers = []
self.validate()
def __init__(
self,
params: Union[str, List[str]],
start_epoch: float = -1,
end_epoch: float = -1,
log_types: Union[str, List[str]] = ALL_TOKEN,
):
super(ConstantPruningModifier, self).__init__(
log_types=log_types,
start_epoch=start_epoch,
end_epoch=end_epoch,
end_comparator=None,
)
self._params = validate_str_iterable(
params, "{} for params".format(self.__class__.__name__)
) # type: List[str]
self._layer_names = [get_layer_name_from_param(p) for p in self._params]
self._masked_layers = []
self._sparsity_scheduler = None
self._mask_creator = load_mask_creator("unstructured")
def __init__(
self,
params: Union[str, List[str]],
init_sparsity: float,
final_sparsity: float,
start_epoch: float,
end_epoch: float,
update_frequency: float,
inter_func: str = "cubic",
log_types: Union[str, List[str]] = ALL_TOKEN,
mask_type: Union[str, List[int]] = "unstructured",
leave_enabled: bool = True,
):
super(GMPruningModifier, self).__init__(
params=params,
init_sparsity=init_sparsity,
final_sparsity=final_sparsity,
start_epoch=start_epoch,
end_epoch=end_epoch,
update_frequency=update_frequency,
inter_func=inter_func,
log_types=log_types,
mask_type=mask_type,
leave_enabled=leave_enabled,
min_start=-1.0,
min_end=0.0,
end_comparator=1,
min_frequency=-1.0,
)
self._layer_names = [get_layer_name_from_param(p) for p in self._params]
self._prune_op_vars = None
self._update_ready = None
self._sparsity = None
self._mask_initializer = None
self._masked_layers = []