from collections import namedtuple
from torch.autograd import Variable
PackedSequence_ = namedtuple('PackedSequence', ['data', 'batch_sizes'])
"""Holds the data and list of batch_sizes of a packed sequence.
All RNN modules accept packed sequences as inputs.
Instances of this class should never be created manually. They are meant
to be instantiated by functions like :func:`pack_padded_sequence`.
data (Variable): Variable containing packed sequence
batch_sizes (list[int]): list of integers holding information about
the batch size at each sequence step
[docs]def pack_padded_sequence(input, lengths, batch_first=False):
"""Packs a Variable containing padded sequences of variable length.
Input can be of size ``TxBx*`` where T is the length of the longest sequence
(equal to ``lengths``), B is the batch size, and * is any number of
dimensions (including 0). If ``batch_first`` is True ``BxTx*`` inputs are expected.
The sequences should be sorted by length in a decreasing order, i.e.
``input[:,0]`` should be the longest sequence, and ``input[:,B-1]`` the
This function accept any input that has at least two dimensions. You
can apply it to pack the labels, and use the output of the RNN with
them to compute the loss directly. A Variable can be retrieved from
a :class:`PackedSequence` object by accessing its ``.data`` attribute.
input (Variable): padded batch of variable length sequences.
lengths (list[int]): list of sequences lengths of each batch element.
batch_first (bool, optional): if True, the input is expected in BxTx*
a :class:`PackedSequence` object
if lengths[-1] <= 0:
raise ValueError("length of all samples has to be greater than 0, "
"but found an element in 'lengths' that is <=0")
input = input.transpose(0, 1)
steps = 
batch_sizes = 
lengths_iter = reversed(lengths)
current_length = next(lengths_iter)
batch_size = input.size(1)
if len(lengths) != batch_size:
raise ValueError("lengths array has incorrect size")
for step, step_value in enumerate(input, 1):
while step == current_length:
new_length = next(lengths_iter)
current_length = None
if current_length > new_length: # remember that new_length is the preceding length in the array
raise ValueError("lengths array has to be sorted in decreasing order")
batch_size -= 1
current_length = new_length
if current_length is None:
return PackedSequence(torch.cat(steps), batch_sizes)
[docs]def pad_packed_sequence(sequence, batch_first=False):
"""Pads a packed batch of variable length sequences.
It is an inverse operation to :func:`pack_padded_sequence`.
The returned Variable's data will be of size TxBx*, where T is the length
of the longest sequence and B is the batch size. If ``batch_size`` is True,
the data will be transposed into BxTx* format.
Batch elements will be ordered decreasingly by their length.
sequence (PackedSequence): batch to pad
batch_first (bool, optional): if True, the output will be in BxTx* format.
Tuple of Variable containing the padded sequence, and a list of lengths
of each sequence in the batch.
var_data, batch_sizes = sequence
max_batch_size = batch_sizes
output = var_data.data.new(len(batch_sizes), max_batch_size, *var_data.size()[1:]).zero_()
output = Variable(output)
lengths = 
data_offset = 0
prev_batch_size = batch_sizes
for i, batch_size in enumerate(batch_sizes):
output[i, :batch_size] = var_data[data_offset:data_offset + batch_size]
data_offset += batch_size
dec = prev_batch_size - batch_size
if dec > 0:
lengths.extend((i,) * dec)
prev_batch_size = batch_size
lengths.extend((i + 1,) * batch_size)
output = output.transpose(0, 1)
return output, lengths