def output_types(self) -> Optional[Dict[str, NeuralType]]:
"""Returns definitions of module output ports.
"""
output_types = {
'audio_signal':
NeuralType(
('B', 'T'),
AudioSignal(freq=self._sample_rate) if self is not None
and hasattr(self, '_sample_rate') else AudioSignal(),
),
'a_sig_length':
NeuralType(tuple('B'), LengthsType()),
}
if self.is_regression_task:
output_types.update({
'targets':
NeuralType(tuple('B'), RegressionValuesType()),
'targets_length':
NeuralType(tuple('B'), LengthsType()),
})
else:
output_types.update({
'label':
NeuralType(tuple('B'), LabelsType()),
'label_length':
NeuralType(tuple('B'), LengthsType()),
})
return output_types
def input_types(self):
"""Returns definitions of module input ports.
"""
return {
"preds": NeuralType(tuple('B'), RegressionValuesType()),
"labels": NeuralType(tuple('B'), LabelsType()),
}
def input_ports(self):
"""Returns definitions of module input ports.
preds:
0: AxisType(RegressionTag)
labels:
0: AxisType(RegressionTag)
"""
return {
"preds": NeuralType(tuple('B'), RegressionValuesType()),
"labels": NeuralType(tuple('B'), LabelsType()),
}
def output_types(self) -> Optional[Dict[str, NeuralType]]:
"""Returns definitions of module output ports.
"""
return {
'input_ids':
NeuralType(('B', 'T'), ChannelType()),
'segment_ids':
NeuralType(('B', 'T'), ChannelType()),
'input_mask':
NeuralType(('B', 'T'), MaskType()),
"labels":
NeuralType(
tuple('B'),
RegressionValuesType()
if self.task_name == 'sts-b' else CategoricalValuesType()),
}
def output_types(self) -> Optional[Dict[str, NeuralType]]:
return {"preds": NeuralType(tuple('B'), RegressionValuesType())}