PackedSequence

class torch.nn.utils.rnn.PackedSequence(data, batch_sizes=None, sorted_indices=None, unsorted_indices=None)

Holds the data and list of batch_sizes of a packed sequence.

All RNN modules accept packed sequences as inputs.

Note

Instances of this class should never be created manually. They are meant to be instantiated by functions like pack_padded_sequence().

Batch sizes represent the number elements at each sequence step in the batch, not the varying sequence lengths passed to pack_padded_sequence(). For instance, given data abc and x the PackedSequence would contain data axbc with batch_sizes=[2,1,1].

Variables:

• data (Tensor) – Tensor containing packed sequence

• batch_sizes (Tensor) – Tensor of integers holding information about the batch size at each sequence step

• sorted_indices (Tensor, optional) – Tensor of integers holding how this PackedSequence is constructed from sequences.

• unsorted_indices (Tensor, optional) – Tensor of integers holding how this to recover the original sequences with correct order.

Return type:

Self

Note

data can be on arbitrary device and of arbitrary dtype. sorted_indices and unsorted_indices must be torch.int64 tensors on the same device as data.

However, batch_sizes should always be a CPU torch.int64 tensor.

This invariant is maintained throughout PackedSequence class, and all functions that construct a PackedSequence in PyTorch (i.e., they only pass in tensors conforming to this constraint).

batch_sizes: Tensor

Alias for field number 1

count(value, /)

Return number of occurrences of value.

data: Tensor

Alias for field number 0

index(value, start=0, stop=9223372036854775807, /)

Return first index of value.

Raises ValueError if the value is not present.

property is_cuda: bool

Return true if self.data stored on a gpu.

is_pinned()

Return true if self.data stored on in pinned memory.

Return type:

bool

sorted_indices: Tensor | None

Alias for field number 2

to(*args, **kwargs)

Perform dtype and/or device conversion on self.data.

It has similar signature as torch.Tensor.to()

Note

If the self.data Tensor already has the correct torch.dtype and torch.device, then self is returned. Otherwise, returns a copy with the desired configuration.

Return type:

Self

unsorted_indices: Tensor | None

Alias for field number 3