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from typing import Optional, Callable, Union | |
from torch.nn import Module | |
from tha3.module.module_factory import ModuleFactory | |
from tha3.nn.init_function import create_init_function | |
from tha3.nn.nonlinearity_factory import resolve_nonlinearity_factory | |
from tha3.nn.normalization import NormalizationLayerFactory | |
from tha3.nn.spectral_norm import apply_spectral_norm | |
def wrap_conv_or_linear_module(module: Module, | |
initialization_method: Union[str, Callable[[Module], Module]], | |
use_spectral_norm: bool): | |
if isinstance(initialization_method, str): | |
init = create_init_function(initialization_method) | |
else: | |
init = initialization_method | |
return apply_spectral_norm(init(module), use_spectral_norm) | |
class BlockArgs: | |
def __init__(self, | |
initialization_method: Union[str, Callable[[Module], Module]] = 'he', | |
use_spectral_norm: bool = False, | |
normalization_layer_factory: Optional[NormalizationLayerFactory] = None, | |
nonlinearity_factory: Optional[ModuleFactory] = None): | |
self.nonlinearity_factory = resolve_nonlinearity_factory(nonlinearity_factory) | |
self.normalization_layer_factory = normalization_layer_factory | |
self.use_spectral_norm = use_spectral_norm | |
self.initialization_method = initialization_method | |
def wrap_module(self, module: Module) -> Module: | |
return wrap_conv_or_linear_module(module, self.get_init_func(), self.use_spectral_norm) | |
def get_init_func(self) -> Callable[[Module], Module]: | |
if isinstance(self.initialization_method, str): | |
return create_init_function(self.initialization_method) | |
else: | |
return self.initialization_method | |