#!/usr/bin/env python
# -*- coding: utf-8 -*-
from typing import Any, Dict, List, Union
import torch
from torch import Tensor
from torch.types import Number
from torchoutil.core.make import DeviceLike, GeneratorLike, as_device, as_generator
from torchoutil.types import LongTensor1D, Tensor1D, is_builtin_number
[docs]def get_inverse_perm(indices: Tensor, dim: int = -1) -> Tensor:
"""Return inverse permutation indices.
The output will be a tensor of shape (..., N).
Args:
indices: Original permutation indices as tensor of shape (..., N).
dim: Dimension of indices. defaults to -1.
Example 1
----------
>>> x = torch.as_tensor([2, 4, 8, 10])
>>> indices = torch.randperm(len(x))
>>> x = x[indices]
>>> # x is now shuffled, to get back the original order we need the indices
>>> inv_indices = get_inverse_perm(indices)
>>> x_reordered = x[inv_indices]
>>> x_reordered
... tensor([2, 4, 8, 10])
"""
arange = torch.arange(
indices.shape[dim],
dtype=indices.dtype,
device=indices.device,
)
arange = arange.expand(*indices.shape)
indices_inv = torch.empty_like(indices)
indices_inv = indices_inv.scatter(dim, indices, arange)
return indices_inv
[docs]def randperm_diff(
size: int,
generator: GeneratorLike = None,
device: DeviceLike = None,
) -> LongTensor1D:
"""This function ensure that every value i cannot be the element at index i.
The output will be a tensor of shape (size,).
Args:
size: The number of indices. Cannot be < 2.
seed: The seed or torch.Generator used to generate permutation.
device: The PyTorch device of the output indices tensor.
Example 1
----------
>>> torch.randperm(5)
tensor([1, 4, 2, 5, 0]) # 2 is the element of index 2 !
>>> randperm_diff(5)
tensor([2, 0, 4, 1, 3])
"""
if size < 2:
raise ValueError(f"Invalid argument {size=} < 2 for randperm_diff.")
device = as_device(device)
generator = as_generator(generator)
perm_kws: Dict[str, Any] = dict(generator=generator, device=device)
arange = torch.arange(size, device=device)
perm = torch.randperm(size, **perm_kws)
while perm.eq(arange).any():
perm = torch.randperm(size, **perm_kws)
return perm # type: ignore
[docs]def get_perm_indices(x1: Tensor, x2: Tensor) -> LongTensor1D:
"""Find permutation between two vectors t1 and t2 which contains values from 0 to N-1.
Example 1::
-----------
>>> x1 = torch.as_tensor([0, 1, 2, 4, 3, 6, 5, 7])
>>> x2 = torch.as_tensor([0, 2, 1, 4, 3, 5, 6, 7])
>>> indices = get_perm_indices(x1, x2)
>>> torch.equal(x1, x2[indices])
True
"""
indices = (x1[..., None, :] == x2[..., :, None]).short().argmax(dim=-2)
return indices # type: ignore
[docs]def insert_at_indices(
x: Tensor,
indices: Union[Tensor, List, Number],
values: Union[Number, Tensor],
) -> Tensor1D:
"""Insert value(s) in vector at specified indices.
Example 1::
-----------
>>> x = torch.as_tensor([1, 1, 2, 2, 2, 3])
>>> indices = torch.as_tensor([2, 5])
>>> values = 4
>>> insert_values(x, indices, values)
tensor([1, 1, 4, 2, 2, 2, 4, 3])
"""
if x.ndim != 1:
msg = f"Invalid argument number of dims. (found {x.ndim=} but expected 1)"
raise ValueError(msg)
device = x.device
if isinstance(indices, Tensor):
pass
elif is_builtin_number(indices):
indices = torch.as_tensor([indices], device=device, dtype=torch.long)
elif isinstance(indices, list):
indices = torch.as_tensor(indices, device=device, dtype=torch.long)
else:
raise TypeError(f"Invalid argument type {type(indices)=}.")
out = torch.empty((x.shape[0] + indices.shape[0]), dtype=x.dtype, device=device)
indices = indices + torch.arange(
indices.shape[0], device=indices.device, dtype=indices.dtype
)
out[indices] = values
mask = torch.full((out.shape[0],), True, dtype=torch.bool)
mask[indices] = False
out[mask] = x
return out # type: ignore
[docs]def remove_at_indices(
x: Tensor,
indices: Union[Tensor, List, Number],
) -> Tensor1D:
"""Remove value(s) in vector at specified indices."""
if x.ndim != 1:
msg = f"Invalid argument number of dims. (found {x.ndim=} but expected 1)"
raise ValueError(msg)
device = x.device
if isinstance(indices, Tensor):
pass
elif is_builtin_number(indices):
indices = torch.as_tensor([indices], device=device, dtype=torch.long)
elif isinstance(indices, list):
indices = torch.as_tensor(indices, device=device, dtype=torch.long)
else:
raise TypeError(f"Invalid argument type {type(indices)=}.")
indices = indices + torch.arange(
indices.shape[0], device=device, dtype=indices.dtype
)
mask = torch.full((x.shape[0],), True, dtype=torch.bool)
mask[indices] = False
out = x[mask]
return out # type: ignore