def build(cls, params, sub_cls=None, controller=None):
layer_cls_names = try_get_attr(params,
f'{cls.prefix_name()}_layer_names')
layer_clses = [
Layer.load_cls(layer_cls_name)
for layer_cls_name in layer_cls_names
]
layer_params = cls.collect_layer_params(layer_cls_names)
layer_args = {
name: try_get_attr(params, f'{cls.prefix_name()}_layer_{name}s')
for name in layer_params.keys()
}
layer_args = {
key: value
for key, value in layer_args.items() if value is not None
}
layers = list()
for idx, layer_cls in enumerate(layer_clses):
layer_kwargs = {
name: layer_arg[idx]
for name, layer_arg in layer_args.items()
}
layer_kwargs = load_func_kwargs(layer_kwargs, layer_cls.__init__)
layers.append(layer_cls(**layer_kwargs))
kwargs = load_func_params(params, cls.__init__, cls.prefix_name())
kwargs[f'{cls.prefix_name()}_layers'] = layers
return cls.default_build(kwargs, controller=controller)
def build(cls, params, sub_cls=None, controller=None):
kwargs = load_func_params(params, cls.__init__, cls.prefix_name())
for net_name in cls.net_names:
net = Net.build(params, sub_cls=try_get_attr(params, f'{cls.prefix_name()}_{net_name}_cls'),
controller=controller)
kwargs[f'{cls.prefix_name()}_{net_name}'] = net
return cls.default_build(kwargs, controller=controller)
def build(cls, params, sub_cls=None, controller=None):
dataset_cls = Dataset.load_cls(params.dataset_cls)
q_vocab, _ = dataset_cls.load_combined_vocab()
kwargs = load_func_params(params, cls.__init__, cls.prefix_name())
kwargs[f'{cls.prefix_name()}_q_vocab'] = q_vocab
return cls.default_build(kwargs, controller=controller)