torchoutil 0.1.0

Collection of functions and modules to help development in PyTorch.

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": [e-mail]