torchoutil.nn.functional.segments module

activity_to_segments(

x: Tensor,

) → LongTensor

Extracts segments start and end positions from a boolean activity/mask tensor.

Example 1

>>> x = torch.as_tensor([0, 1, 1, 0, 0, 1, 1, 1, 1, 0]).bool()
>>> starts, ends = extract_segments(x)
>>> starts
... tensor([1, 5])
>>> ends
... tensor([3, 9])

Example 2

>>> x = torch.as_tensor([[1, 1, 1, 0], [1, 0, 0, 1]]).bool()
>>> indices, starts, ends = extract_segments(x)
>>> indices
... tensor([0, 1, 1])
>>> starts
... tensor([0, 0, 3])
>>> ends
... tensor([3, 1, 4])

Args:

x: (…, N) bool tensor containing D dims

Returns:

segments: (D+1, M) tensor, where M is the total number of segments

When D > 1, segments also contains indices of the source column for each start and end value. See Example 2 for details.

activity_to_segments_list(

x: Tensor,

) → List[Tuple[int, int]] | list

extract_segments(

x: Tensor,

) → LongTensor

Extracts segments start and end positions from a boolean activity/mask tensor.

Example 1

>>> x = torch.as_tensor([0, 1, 1, 0, 0, 1, 1, 1, 1, 0]).bool()
>>> starts, ends = extract_segments(x)
>>> starts
... tensor([1, 5])
>>> ends
... tensor([3, 9])

Example 2

>>> x = torch.as_tensor([[1, 1, 1, 0], [1, 0, 0, 1]]).bool()
>>> indices, starts, ends = extract_segments(x)
>>> indices
... tensor([0, 1, 1])
>>> starts
... tensor([0, 0, 3])
>>> ends
... tensor([3, 1, 4])

Args:

x: (…, N) bool tensor containing D dims

Returns:

segments: (D+1, M) tensor, where M is the total number of segments

When D > 1, segments also contains indices of the source column for each start and end value. See Example 2 for details.

segments_list_to_activity(

segments_list: List[Tuple[int, int]] | Tensor | list,

maxsize: int | None = None,

*,

device: device | None | Literal['default', 'cuda_if_available'] | str | int = None,

) → BoolTensor

Convert list of (start, end) tuples to activity boolean tensor.

Args:

segments_list: list of (start, end) tuples of shape (*, N, 2). maxsize: Optional max size. If None, use segments_list.max(). defaults to None. device: Optional output device. If None and segments_list is a tensor, it will use the same device. defaults to None.

Returns:

activity boolean tensor of shape (*, maxsize)

segments_to_activity(

x: Tensor,

maxsize: int | None = None,

) → BoolTensor

Convert stacked list/tensor of starts end stops separated to activity boolean tensor.

Args:

x: (*, 2, N) tensor maxsize: Optional max size. If None, use x.max().

Returns:

activity boolean tensor of shape (*, maxsize)

segments_to_list(

segments: Tensor,

maxsize: int | Tuple[int, ...] | None = None,

) → List[Tuple[int, int]] | list

Convert stacked list/tensor of starts end stops separated to list of (start, end) tuples.

Args:

x: (2+C, N) tensor, where C defines indices in dimensions of a segments for 3D activity tensors. maxsize: Optional max size. If None, use x.max().

Returns:

list of (start, end) tuples of shape (*, N, 2).

note: (*) corresponds to C batched dimensions.

segments_to_segments_list(

segments: Tensor,

maxsize: int | Tuple[int, ...] | None = None,

) → List[Tuple[int, int]] | list

Convert stacked list/tensor of starts end stops separated to list of (start, end) tuples.

Args:

x: (2+C, N) tensor, where C defines indices in dimensions of a segments for 3D activity tensors. maxsize: Optional max size. If None, use x.max().

Returns:

list of (start, end) tuples of shape (*, N, 2).

note: (*) corresponds to C batched dimensions.