torchwrench.nn.functional.segments module¶

torchwrench.nn.functional.segments.activity_to_segments(x: Tensor) → LongTensor[source]¶

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.

torchwrench.nn.functional.segments.activity_to_segments_list(x: Tensor) → list[tuple[int, int]] | list[source]¶

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

Example¶

`python >>> segments = [(3, 6), (8, 9)] >>> segments_list_to_activity(segments) ... tensor([False, False, False, True, True, True, False, False, True]) `

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)

torchwrench.nn.functional.segments.segments_to_activity(x: Tensor, maxsize: int | None = None) → BoolTensor[source]¶

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)

torchwrench.nn.functional.segments.segments_to_segments_list(segments: Tensor, maxsize: int | tuple[int, ...] | None = None) → list[tuple[int, int]] | list[source]¶

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.