def unroll(self,
length,
inputs,
begin_state=None,
layout='NTC',
merge_outputs=None):
self.reset()
inputs, axis = _normalize_sequence(length, inputs, layout, True)
if axis == 1:
warnings.warn("NTC layout detected. Consider using "
"TNC for FusedRNNCell for faster speed")
inputs = symbol.swapaxes(inputs, dim1=0, dim2=1)
else:
assert axis == 0, "Unsupported layout %s" % layout
if begin_state is None:
begin_state = self.begin_state()
states = begin_state
if self._mode == 'lstm':
states = {'state': states[0], 'state_cell': states[1]} # pylint: disable=redefined-variable-type
else:
states = {'state': states[0]}
rnn = symbol.RNN(data=inputs,
parameters=self._parameter,
state_size=self._num_hidden,
num_layers=self._num_layers,
bidirectional=self._bidirectional,
p=self._dropout,
state_outputs=self._get_next_state,
mode=self._mode,
name=self._prefix + 'rnn',
**states)
if not self._get_next_state:
outputs, states = rnn, []
elif self._mode == 'lstm':
outputs, states = rnn[0], [rnn[1], rnn[2]]
else:
outputs, states = rnn[0], [rnn[1]]
if axis == 1:
outputs = symbol.swapaxes(outputs, dim1=0, dim2=1)
outputs, _ = _normalize_sequence(length, outputs, layout,
merge_outputs)
return outputs, states
def _normalize_sequence(length, inputs, layout, merge, in_layout=None):
assert inputs is not None, \
"unroll(inputs=None) has been deprecated. " \
"Please create input variables outside unroll."
axis = layout.find('T')
in_axis = in_layout.find('T') if in_layout is not None else axis
if isinstance(inputs, symbol.Symbol):
if merge is False:
assert len(inputs.list_outputs()) == 1, \
"unroll doesn't allow grouped symbol as input. Please convert " \
"to list with list(inputs) first or let unroll handle splitting."
inputs = list(
symbol.split(inputs,
axis=in_axis,
num_outputs=length,
squeeze_axis=1))
else:
assert length is None or len(inputs) == length
if merge is True:
inputs = [symbol.expand_dims(i, axis=axis) for i in inputs]
inputs = symbol.Concat(*inputs, dim=axis)
in_axis = axis
if isinstance(inputs, symbol.Symbol) and axis != in_axis:
inputs = symbol.swapaxes(inputs, dim0=axis, dim1=in_axis)
return inputs, axis