def __init__(self, params: DatasetAlphabetParams, seed: Optional[int] = None, n_worlds: int = 1,
name: str = "DatasetAlphabet"):
super().__init__(name=name, outputs=MultiDatasetAlphabetOutputs(self),
memory_blocks=MultiDatasetAlphabetInternals(self))
self._params = params.clone()
self._seed = seed
self._n_worlds = n_worlds
def __init__(self, creator: TensorCreator, params: DatasetAlphabetParams, n_worlds: int,
random: Optional[RandomState] = None):
super().__init__(creator.device)
self._validate_params(params)
self.n_worlds = n_worlds
self._units = [DatasetAlphabetUnit(creator, params.clone(), random) for _ in range(self.n_worlds)]
def stacked(tensor):
size = [self.n_worlds] + list(tensor.shape)
return creator.zeros(size, dtype=tensor.dtype, device=tensor.device)
self._first_unit = self._units[0]
# Create output tensors
self.output_data = stacked(self._first_unit.output_data)
self.output_label = stacked(self._first_unit.output_label)
self.output_sequence_id = stacked(self._first_unit.output_sequence_id)
self.output_sequence_id_one_hot = stacked(self._first_unit.output_sequence_id_one_hot)
self.all_symbols = self._first_unit.all_symbols