def __init__(self, creator: TensorCreator,
params: DatasetSimplePointGravityParams, random: np.random):
super().__init__(creator.device)
self._random = random
self.point_pos = params.point_pos
self.attractor_distance = params.attractor_distance
self.output_data = creator.zeros(*params.canvas_shape,
device=self._device)
self._state = States.BLANK
self.move_strategy = params.move_strategy
self._frame_backbuffer = creator.zeros_like(self.output_data)
def __init__(self,
creator: TensorCreator,
params: DatasetAlphabetParams,
random: Optional[RandomState] = None):
super().__init__(creator.device)
self._validate_params(params)
random = random or np.random.RandomState()
# Generate all symbols
generator = AlphabetGenerator(params.padding_right)
all_symbols = generator.create_symbols(params.symbols)
self.all_symbols = creator.zeros_like(all_symbols)
self.all_symbols.copy_(all_symbols.to(creator.device))
# Create output tensors
shape = list(self.all_symbols.shape)
self.output_data = creator.zeros(shape[1:], device=creator.device)
self.output_label = creator.zeros(1,
dtype=torch.int64,
device=creator.device)
self.output_sequence_id = creator.zeros(1,
dtype=torch.int64,
device=creator.device)
self.output_sequence_id_one_hot = creator.zeros(
(1, len(params.sequence_probs.seqs)),
dtype=self._float_dtype,
device=creator.device)
if params.mode == DatasetAlphabetMode.SEQUENCE_PROBS:
seqs = [
self.convert_string_to_positions(params.symbols, seq)
for seq in params.sequence_probs.seqs
]
transition_probs = params.sequence_probs.transition_probs or SequenceGenerator.default_transition_probs(
seqs)
self.seq = SequenceGenerator(seqs, transition_probs, random=random)
self._current = next(self.seq)
self._n_symbols = shape[0]