torchoutil

Collection of functions and modules to help development in PyTorch.

Installation

pip install torchoutil

The only requirements are python>=3.8 and torch>=1.10.

Usage

Batch of padded sequences

import torch
from torchoutil import masked_mean

x = torch.as_tensor([1, 2, 3, 4])
mask = torch.as_tensor([True, True, False, False])
result = masked_mean(x, mask)
# result contains the mean of the values marked as True: 1.5

import torch
from torchoutil import lengths_to_non_pad_mask

x = torch.as_tensor([3, 1, 2])
pad_mask = lengths_to_non_pad_mask(x, max_len=4)
# tensor([[True, True, True, False],
#         [True, False, False, False],
#         [True, True, False, False]])

Multilabel conversions

import torch
from torchoutil import probs_to_names

probs = torch.as_tensor([[0.9, 0.1], [0.6, 0.9]])
names = probs_to_names(probs, threshold=0.5, idx_to_name={0: "Cat", 1: "Dog"})
# [["Cat"], ["Cat", "Dog"]]

import torch
from torchoutil import multihot_to_indices

multihot = torch.as_tensor([[1, 0, 0], [0, 1, 1], [0, 0, 0]])
indices = multihot_to_indices(multihot)
# [[0], [1, 2], []]

...and more tensor manipulations!

import torch
from torchoutil import insert_at_indices

x = torch.as_tensor([1, 2, 3, 4])
result = insert_at_indices(x, indices=[0, 2], values=5)
# result contains tensor with inserted values: tensor([5, 1, 2, 5, 3, 4])

import torch
from torchoutil import get_inverse_perm

perm = torch.randperm(10)
inv_perm = get_inverse_perm(perm)

x1 = torch.rand(10)
x2 = x1[perm]
x3 = x2[inv_perm]
# inv_perm are indices that allow us to get x1 from x3, i.e. x1 == x3 here

Contact

Maintainer:

• Étienne Labbé "Labbeti": labbeti.pub@gmail.com