def input_types(self):
"""Input types definitions for AnguarLoss.
"""
return {
"logits": NeuralType(('B', 'D'), LogitsType()),
"labels": NeuralType(('B',), LabelsType()),
}
def output_types(self):
return OrderedDict({
"logits":
NeuralType(('B', 'D'), LogitsType()),
"embs":
NeuralType(('B', 'D'), AcousticEncodedRepresentation()),
})
def input_ports(self):
"""Returns definitions of module input ports.
"""
return {
"logits": NeuralType(axes=('B', 'D'), elements_type=LogitsType()),
"labels": NeuralType(axes=tuple('B'), elements_type=LabelsType()),
}
def input_types(self):
"""Returns definitions of module input ports.
"""
return {
"logits": NeuralType(('B', 'D'), LogitsType()),
"labels": NeuralType(('B'), LabelsType())
}
def output_types(self) -> Optional[Dict[str, NeuralType]]:
"""
Returns definitions of module output ports.
"""
if not self.log_softmax:
return {"logits": NeuralType(('B', 'T', 'C'), LogitsType())}
else:
return {"log_probs": NeuralType(('B', 'T', 'C'), LogprobsType())}
def input_types(self):
"""Returns definitions of module input ports.
"""
return {
"logits": NeuralType(('B', 'T', 'D'), LogitsType()),
"start_positions": NeuralType(tuple('B'), ChannelType()),
"end_positions": NeuralType(tuple('B'), ChannelType()),
}
def input_ports(self):
"""Returns definitions of module input ports.
"""
return {
# "logits": NeuralType({0: AxisType(BatchTag), 1: AxisType(ChannelTag), 2: AxisType(ChannelTag)}),
# "labels": NeuralType({0: AxisType(BatchTag), 1: AxisType(ChannelTag)}),
"logits": NeuralType(('B', 'D', 'D'), LogitsType()),
"labels": NeuralType(('B', 'D'), LabelsType()),
}
def output_types(self) -> Optional[Dict[str, NeuralType]]:
"""
Returns definitions of module output ports.
"""
return {
"logits": NeuralType(('B', 'T'), LogitsType()),
'hidden_states': NeuralType(('B', 'T', 'C'), ChannelType()),
}
def output_ports(self):
"""Returns definitions of module output ports.
point_outputs: outputs of the generator
gate_outputs: outputs of gating heads
"""
# return {
# 'point_outputs': NeuralType(
# {0: AxisType(BatchTag), 1: AxisType(TimeTag), 2: AxisType(ChannelTag), 3: AxisType(ChannelTag)}
# ),
# 'gate_outputs': NeuralType({0: AxisType(BatchTag), 1: AxisType(ChannelTag), 2: AxisType(ChannelTag)}),
# }
return {
'point_outputs': NeuralType(('B', 'T', 'D', 'D'), LogitsType()),
'gate_outputs': NeuralType(('B', 'D', 'D'), LogitsType()),
}
def output_ports(self):
"""Return definitions of module output ports.
Returns:
Module output ports.
"""
return {
# Variant type
'output_logit': NeuralType(('B', 'D'), LogitsType()),
}
def input_types(self):
"""Returns definitions of module input ports.
logit_intent_status: Output of SGD model
intent_status: intent label
logit_req_slot_status: Output of SGD model
requested_slot_status: Takes value 1 if the corresponding slot is requested, 0 otherwise
logit_cat_slot_status: Output of SGD model
categorical_slot_status: The status of each categorical slot in the service
logit_cat_slot_value_status: Output of SGD model
categorical_slot_value_status: Takes value 1 if the corresponding slot value is correct, 0 otherwise
logit_noncat_slot_status: Output of SGD model
noncategorical_slot_status: The status of each noncategorical slot in the service\
logit_spans: Output of SGD model
noncategorical_slot_value_start: The index of the starting subword corresponding to the slot span for a non-categorical slot value
noncategorical_slot_value_end: The index of the ending (inclusive) subword corresponding to the slot span for a non-categorical slot value
task_mask: Mask contains 1 if its the current task, 0 otherwise
"""
return {
"logit_intent_status": NeuralType(('B', 'T'), LogitsType()),
"intent_status": NeuralType(('B'), LabelsType()),
"logit_req_slot_status": NeuralType(('B', 'T'), LogitsType()),
"requested_slot_status": NeuralType(('B'), LabelsType()),
"logit_cat_slot_status": NeuralType(('B', 'T'), LogitsType()),
"categorical_slot_status": NeuralType(('B'), LabelsType()),
"logit_cat_slot_value_status": NeuralType(('B', 'T'), LogitsType()),
"categorical_slot_value_status": NeuralType(('B'), LabelsType()),
"logit_noncat_slot_status": NeuralType(('B', 'T'), LogitsType()),
"noncategorical_slot_status": NeuralType(('B'), LabelsType()),
"logit_spans": NeuralType(('B', 'T', 'D'), LogitsType()),
"noncategorical_slot_value_start": NeuralType(('B'), LabelsType()),
"noncategorical_slot_value_end": NeuralType(('B'), LabelsType()),
"task_mask": NeuralType(('B', 'T'), ChannelType()),
}
def input_ports(self):
"""Returns definitions of module input ports.
"""
return {
"logits":
NeuralType(['B'] + ['ANY'] * (self._logits_dim - 1), LogitsType()),
"labels":
NeuralType(['B'] + ['ANY'] * (self._logits_dim - 2), LabelsType()),
"loss_mask":
NeuralType(['B'] + ['ANY'] * (self._logits_dim - 2),
MaskType(),
optional=True),
}
def input_types(self):
"""Returns definitions of module input ports.
"""
return {
"logits":
NeuralType(["B"] + ["ANY"] * (self._logits_dim - 1), LogitsType()),
"labels": [
NeuralType(["B"] + ["ANY"] * (self._logits_dim - 2),
LabelsType())
],
"loss_mask":
NeuralType(["B"] + ["ANY"] * (self._logits_dim - 2),
MaskType(),
optional=True),
}
def input_ports(self):
"""Returns definitions of module input ports.
logits: 4d tensor of logits
labels: 3d tensor of labels
loss_mask: specifies the words to be considered in the loss calculation
"""
return {
"logits": NeuralType(('B', 'T', 'D', 'D'), LogitsType()),
"labels": NeuralType(('B', 'D', 'T'), LabelsType()),
"length_mask": NeuralType(('B', 'D'), LengthsType()),
}
def output_types(self) -> Optional[Dict[str, NeuralType]]:
"""
Returns definitions of module output ports.
"""
return {
"logit_intent_status": NeuralType(('B', 'T'), LogitsType()), #'B'
"logit_req_slot_status": NeuralType(('B', 'T'), LogitsType()), #'B'
"logit_cat_slot_status": NeuralType(('B', 'T'), LogitsType()),
"logit_cat_slot_value_status": NeuralType(('B', 'T'), LogitsType()), #'B'
"logit_noncat_slot_status": NeuralType(('B', 'T'), LogitsType()),
"logit_spans": NeuralType(('B', 'T', 'D'), LogitsType()),
}
def input_ports(self):
"""Returns definitions of module input ports.
logits: 4d tensor of logits
targets: 3d tensor of labels
loss_mask: specifies the words to be considered in the loss calculation
"""
return {
# "logits": NeuralType(
# {0: AxisType(BatchTag), 1: AxisType(TimeTag), 2: AxisType(ChannelTag), 3: AxisType(ChannelTag)}
# ),
# "targets": NeuralType({0: AxisType(BatchTag), 1: AxisType(ChannelTag), 2: AxisType(TimeTag)}),
# "loss_mask": NeuralType({0: AxisType(BatchTag), 1: AxisType(ChannelTag)}),
"logits": NeuralType(('B', 'T', 'D', 'D'), LogitsType()),
"targets": NeuralType(('B', 'D', 'T'), LabelsType()),
"loss_mask": NeuralType(('B', 'D'), LengthsType()),
}
def output_ports(self):
"""
Returns definitions of module output ports.
"""
# Return neural type.
if self._return_feature_maps:
return {
"outputs": NeuralType(
axes=(
AxisType(kind=AxisKind.Batch),
AxisType(kind=AxisKind.Channel, size=self._feature_map_depth),
AxisType(kind=AxisKind.Height, size=self._feature_map_height),
AxisType(kind=AxisKind.Width, size=self._feature_map_width),
),
elements_type=ImageFeatureValue(),
)
}
else:
return {
"outputs": NeuralType(
axes=(AxisType(kind=AxisKind.Batch), AxisType(kind=AxisKind.Any, size=self._output_size),),
elements_type=LogitsType(),
)
}
def output_types(self) -> Optional[Dict[str, NeuralType]]:
return {
"intent_logits": NeuralType(('B', 'D'), LogitsType()),
"slot_logits": NeuralType(('B', 'T', 'D'), LogitsType()),
}
def output_types(self):
return OrderedDict({"logits": NeuralType(('B', 'D'), LogitsType())})
def output_types(self) -> Optional[Dict[str, NeuralType]]:
if not self.log_softmax:
return {"logits": NeuralType(('B', 'D'), LogitsType())}
else:
return {"log_probs": NeuralType(('B', 'D'), LogprobsType())}
def output_types(self) -> Optional[Dict[str, NeuralType]]:
return {
"punct_logits": NeuralType(('B', 'T', 'C'), LogitsType()),
"capit_logits": NeuralType(('B', 'T', 'C'), LogitsType()),
}
def output_types(self) -> Optional[Dict[str, NeuralType]]:
return {"logits": NeuralType(('B', 'D'), LogitsType())}
