torchwrench.nn.functional.multiclass module¶

Helper functions for conversion between classes indices, onehot, names and probabilities for multiclass classification.

torchwrench.nn.functional.multiclass.index_to_name(index: ~typing.Sequence[int] | ~torch.Tensor | ~numpy.ndarray | ~typing.Sequence, idx_to_name: ~typing.Mapping[int, __SPHINX_IMMATERIAL_TYPE_VAR__V_T_Name] | ~typing.Iterable[__SPHINX_IMMATERIAL_TYPE_VAR__V_T_Name], *, is_number_fn: ~typing.Callable[[~typing.Any], bool] = <function is_number_like>) → list[T_Name][source]¶

Convert indices of labels to names using a mapping for multiclass classification.

Args:

indices: List of list of label indices. idx_to_name: Mapping to convert a class index to its name. is_number_fn: Type guard to check if a value is a scalar number. defaults to is_number_like.

torchwrench.nn.functional.multiclass.index_to_onehot(index: Iterable[int], num_classes: int, *, padding_idx: int | None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None) → BoolTensor2D[source]¶

torchwrench.nn.functional.multiclass.index_to_onehot(index: Iterable[Iterable[int]], num_classes: int, *, padding_idx: int | None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None) → BoolTensor3D

torchwrench.nn.functional.multiclass.index_to_onehot(index: Iterable, num_classes: int, *, padding_idx: int | None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, dtype: dtype | None | 'default' | str | DTypeEnum = torch.bool) → Tensor

Convert indices of labels to onehot boolean encoding for multiclass classification.

Args:

indices: List label indices.

Can be a nested list of indices, but it should be convertible to Tensor.

num_classes: Number maximal of unique classes. padding_idx: Class index to ignore. Output will contains only zeroes for this value. defaults to None. device: PyTorch device of the output tensor. dtype: PyTorch DType of the output tensor.

torchwrench.nn.functional.multiclass.name_to_index(name: list[T_Name], idx_to_name: Mapping[int, T_Name] | Iterable[T_Name]) → Tensor[source]¶

Convert names to indices of labels for multiclass classification.

Args:

names: List of list of label names. idx_to_name: Mapping to convert a class index to its name.

torchwrench.nn.functional.multiclass.name_to_onehot(name: list[T_Name], idx_to_name: Mapping[int, T_Name] | SupportsIterLen[T_Name], *, device: device | None | 'default' | 'cuda_if_available' | str | int = None, dtype: dtype | None | 'default' | str | DTypeEnum = torch.bool) → Tensor[source]¶

Convert names to onehot boolean encoding for multiclass classification.

Args:

names: List of list of label names. idx_to_name: Mapping to convert a class index to its name. device: PyTorch device of the output tensor. dtype: PyTorch DType of the output tensor.

torchwrench.nn.functional.multiclass.one_hot(index: Iterable, num_classes: int, *, padding_idx: int | None = None, device: device | None | 'default' | 'cuda_if_available' | str | int = None, dtype: dtype | None | 'default' | str | DTypeEnum = torch.bool) → Tensor[source]¶

Convert indices of labels to onehot boolean encoding for multiclass classification.

Args:

indices: List label indices.

Can be a nested list of indices, but it should be convertible to Tensor.

num_classes: Number maximal of unique classes. padding_idx: Class index to ignore. Output will contains only zeroes for this value. defaults to None. device: PyTorch device of the output tensor. dtype: PyTorch DType of the output tensor.

torchwrench.nn.functional.multiclass.onehot_to_index(onehot: T_TensorOrArray, *, padding_idx: int | None = None, dim: int = -1) → T_TensorOrArray[source]¶

Convert onehot boolean encoding to indices of labels for multiclass classification.

Args:

onehot: Onehot labels encoded as 2D matrix. padding_idx: Class index placeholder when input contains only zeroes. defaults to None. dim: Dimension of classes. defaults to -1.

torchwrench.nn.functional.multiclass.onehot_to_name(onehot: Tensor, idx_to_name: Mapping[int, T_Name] | Iterable[T_Name], *, dim: int = -1) → list[T_Name][source]¶

Convert onehot boolean encoding to names using a mapping for multiclass classification.

Args:

onehot: Onehot labels encoded as 2D matrix. idx_to_name: Mapping to convert a class index to its name. dim: Dimension of classes. defaults to -1.

torchwrench.nn.functional.multiclass.probs_to_index(probs: Tensor, *, dim: int = -1) → LongTensor[source]¶

Convert matrix of probabilities to indices of labels for multiclass classification.

Args:

probs: Output probabilities for each classes. dim: Dimension of classes. defaults to -1.

torchwrench.nn.functional.multiclass.probs_to_name(probs: Tensor, idx_to_name: Mapping[int, T_Name] | Iterable[T_Name], *, dim: int = -1) → list[T_Name][source]¶

Convert matrix of probabilities to labels names for multiclass classification.

Args:

probs: Output probabilities for each classes. idx_to_name: Mapping to convert a class index to its name. dim: Dimension of classes. defaults to -1.

torchwrench.nn.functional.multiclass.probs_to_onehot(probs: Tensor, *, dim: int = -1, device: device | None | 'default' | 'cuda_if_available' | str | int = None, dtype: dtype | None | 'default' | str | DTypeEnum = torch.bool) → Tensor[source]¶

Convert matrix of probabilities to onehot boolean encoding for multiclass classification.

Args:

probs: Output probabilities for each classes. dim: Dimension of classes. defaults to -1. device: PyTorch device of the output tensor. dtype: PyTorch DType of the output tensor.