def forward(
self, # type: ignore
inputs: torch.Tensor,
sequence_length: Optional[Union[torch.LongTensor,
List[int]]] = None,
initial_state_fw: Optional[State] = None,
initial_state_bw: Optional[State] = None,
time_major: bool = False,
return_cell_output: bool = False,
return_output_size: bool = False):
r"""Encodes the inputs.
Args:
inputs: A 3D Tensor of shape ``[batch_size, max_time, dim]``.
The first two dimensions
``batch_size`` and ``max_time`` may be exchanged if
``time_major`` is `True`.
sequence_length (optional): A 1D :tensor:`LongTensor` of shape
``[batch_size]``.
Sequence lengths of the batch inputs. Used to copy-through
state and zero-out outputs when past a batch element's sequence
length.
initial_state_fw: (optional): Initial state of the forward RNN.
initial_state_bw: (optional): Initial state of the backward RNN.
time_major (bool): The shape format of the :attr:`inputs` and
:attr:`outputs` Tensors. If `True`, these tensors are of shape
``[max_time, batch_size, depth]``. If `False` (default),
these tensors are of shape ``[batch_size, max_time, depth]``.
return_cell_output (bool): Whether to return the output of the RNN
cell. This is the results prior to the output layer.
return_output_size (bool): Whether to return the output size of the
RNN cell. This is the results after the output layer.
Returns:
- By default (both ``return_cell_output`` and ``return_output_size``
are `False`), returns a pair :attr:`(outputs, final_state)`
- :attr:`outputs`: A tuple ``(outputs_fw, outputs_bw)``
containing the forward and the backward RNN outputs, each of
which is of shape ``[batch_size, max_time, output_dim]``
if ``time_major`` is `False`, or
``[max_time, batch_size, output_dim]`` if ``time_major``
is `True`.
If RNN cell output is a (nested) tuple of Tensors, then
``outputs_fw`` and ``outputs_bw`` will be a (nested) tuple
having the same structure as the cell output.
- :attr:`final_state`: A tuple
``(final_state_fw, final_state_bw)`` containing the final
states of the forward and backward RNNs, each of which is a
Tensor of shape ``[batch_size] + cell.state_size``, or a
(nested) tuple of Tensors if ``cell.state_size`` is a (nested)
tuple.
- If ``return_cell_output`` is `True`, returns a triple
:attr:`(outputs, final_state, cell_outputs)` where
- :attr:`cell_outputs`: A tuple
``(cell_outputs_fw, cell_outputs_bw)`` containing the outputs
by the forward and backward RNN cells prior to the output
layers, having the same structure with :attr:`outputs` except
for the ``output_dim``.
- If ``return_output_size`` is `True`, returns a tuple
:attr:`(outputs, final_state, output_size)` where
- :attr:`output_size`: A tuple
``(output_size_fw, output_size_bw)`` containing the size of
``outputs_fw`` and ``outputs_bw``, respectively.
Take ``*_fw`` for example, ``output_size_fw`` is a (possibly
nested tuple of) int. If a single int or an int array, then
``outputs_fw`` has shape
``[batch/time, time/batch] + output_size_fw``. If a (nested)
tuple, then ``output_size_fw`` has the same structure as
``outputs_fw``. The same applies to ``output_size_bw``.
- If both ``return_cell_output`` and ``return_output_size`` are
`True`, returns
:attr:`(outputs, final_state, cell_outputs, output_size)`.
"""
cell_outputs, states = bidirectional_dynamic_rnn(
cell_fw=self._cell_fw,
cell_bw=self._cell_bw,
inputs=inputs,
sequence_length=sequence_length,
initial_state_fw=initial_state_fw,
initial_state_bw=initial_state_bw,
time_major=time_major)
outputs_fw, output_size_fw = _forward_output_layers(
inputs=cell_outputs[0],
output_layer=self._output_layer_fw,
time_major=time_major,
sequence_length=sequence_length)
outputs_bw, output_size_bw = _forward_output_layers(
inputs=cell_outputs[1],
output_layer=self._output_layer_bw,
time_major=time_major,
sequence_length=sequence_length)
outputs = (outputs_fw, outputs_bw)
output_size = (output_size_fw, output_size_bw)
returns = (outputs, states)
if return_cell_output:
returns += (cell_outputs, ) # type: ignore
if return_output_size:
returns += (output_size, ) # type: ignore
return returns
def test_bidirectional_dynamic_rnn_initial_state(self):
r"""Tests :meth:`~texar.utils.rnn.bidirectional_dynamic_rnn`.
"""
inputs = torch.rand(self._batch_size, self._max_time, self._input_size)
rnn_initial_state_fw = torch.rand(self._batch_size, self._hidden_size)
rnn_initial_state_bw = torch.rand(self._batch_size, self._hidden_size)
lstm_initial_state_fw = (torch.rand(self._batch_size,
self._hidden_size),
torch.rand(self._batch_size,
self._hidden_size))
lstm_initial_state_bw = (torch.rand(self._batch_size,
self._hidden_size),
torch.rand(self._batch_size,
self._hidden_size))
# RNN
outputs, output_state = bidirectional_dynamic_rnn(
cell_fw=self._rnn_fw,
cell_bw=self._rnn_bw,
inputs=inputs,
sequence_length=None,
initial_state_fw=rnn_initial_state_fw,
initial_state_bw=rnn_initial_state_bw,
time_major=False)
self.assertIsInstance(outputs, tuple)
self.assertEqual(
outputs[0].shape,
torch.Size([self._batch_size, self._max_time, self._hidden_size]))
self.assertEqual(
outputs[1].shape,
torch.Size([self._batch_size, self._max_time, self._hidden_size]))
self.assertIsInstance(output_state, tuple)
self.assertEqual(output_state[0].shape,
torch.Size([self._batch_size, self._hidden_size]))
self.assertEqual(output_state[1].shape,
torch.Size([self._batch_size, self._hidden_size]))
# LSTM
outputs, output_state = bidirectional_dynamic_rnn(
cell_fw=self._lstm_fw,
cell_bw=self._lstm_bw,
inputs=inputs,
sequence_length=None,
initial_state_fw=lstm_initial_state_fw,
initial_state_bw=lstm_initial_state_bw,
time_major=False)
self.assertIsInstance(outputs, tuple)
self.assertEqual(
outputs[0].shape,
torch.Size([self._batch_size, self._max_time, self._hidden_size]))
self.assertEqual(
outputs[1].shape,
torch.Size([self._batch_size, self._max_time, self._hidden_size]))
self.assertIsInstance(output_state, tuple)
self.assertIsInstance(output_state[0], tuple)
self.assertEqual(output_state[0][0].shape,
torch.Size([self._batch_size, self._hidden_size]))
self.assertEqual(output_state[0][1].shape,
torch.Size([self._batch_size, self._hidden_size]))
self.assertIsInstance(output_state[1], tuple)
self.assertEqual(output_state[1][0].shape,
torch.Size([self._batch_size, self._hidden_size]))
self.assertEqual(output_state[1][1].shape,
torch.Size([self._batch_size, self._hidden_size]))
# GRU
outputs, output_state = bidirectional_dynamic_rnn(
cell_fw=self._gru_fw,
cell_bw=self._gru_bw,
inputs=inputs,
sequence_length=None,
initial_state_fw=rnn_initial_state_fw,
initial_state_bw=rnn_initial_state_bw,
time_major=False)
self.assertIsInstance(outputs, tuple)
self.assertEqual(
outputs[0].shape,
torch.Size([self._batch_size, self._max_time, self._hidden_size]))
self.assertEqual(
outputs[1].shape,
torch.Size([self._batch_size, self._max_time, self._hidden_size]))
self.assertIsInstance(output_state, tuple)
self.assertEqual(output_state[0].shape,
torch.Size([self._batch_size, self._hidden_size]))
self.assertEqual(output_state[1].shape,
torch.Size([self._batch_size, self._hidden_size]))
