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.

注意

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].

变量

• 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.

返回类型

自我

注意

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, /)

返回值的出现次数。

data: Tensor

Alias for field number 0

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

返回值的第一个索引。

如果值不存在，则引发 ValueError。

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.

返回类型

布尔值

sorted_indices: Optional[Tensor]

Alias for field number 2

to(dtype: dtype, non_blocking: bool = ..., copy: bool = ...) → Self

to(device: Optional[Union[str, device, int]] = ..., dtype: Optional[dtype] = ..., non_blocking: bool = ..., copy: bool = ...) → Self

to(other: Tensor, non_blocking: bool = ..., copy: bool = ...) → Self

对self.data执行 dtype 和/或 device 转换。

它具有与 torch.Tensor.to() 相似的签名，除了像 non_blocking 和 copy 这样的可选参数应该作为 kwargs 传递，而不是 args，否则它们将不应用于索引张量。

注意

如果 self.data 张量已经具有正确的 torch.dtype 和 torch.device，则返回 self。否则，返回具有所需配置的副本。

unsorted_indices: Optional[Tensor]

字段编号 3 的别名