TorchToolkit

Hi 👋, this is a small Python package containing useful functions to use with PyTorch. It includes utilities, metrics and sampling methods to use during and after training a model.

Feel free to use it, take the code for your projects, and raise an issue if you have question or a pull request if you want to contribute.

pip install torchtoolkit

It requires Python 3.8 or above.

Simplest example:

from torchtoolkit.metrics import Accuracy
from torch import randint, randn
from pathlib import Path

acc = Accuracy(mode='top_k', top_kp=5)
for _ in range(10):
    res = randn((16, 32))
    expected = randint(0, 32, (16, ))
    acc(res, expected)  # saving results
acc.save(Path('path', 'to', 'save', 'file.csv'))
acc.analyze()

I built it for my own usage, so you won't find documentation besides the docstring.

Metadata

Release files for torchtoolkit 0.0.4

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Source distribution for torchtoolkit 0.0.4
File	Size	Uploaded	Provenance
torchtoolkit-0.0.4.tar.gz	14.9 kB	Dec 10, 2022	Without provenance	Details

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Table of built distributions (wheels) for torchtoolkit 0.0.4
File	Interpreter	ABI	Platform	Reset
torchtoolkit-0.0.4-py3-none-any.whl 13.9 kB Dec 10, 2022 Without provenance	Python 3	none	any	Details

Total release size: 28.8 kB

Release files / torchtoolkit-0.0.4.tar.gz

Download URL	torchtoolkit-0.0.4.tar.gz
Size	14.9 kB
Tags	Source
SHA-256 checksum How to use checksums	`1aa7351afe41a834e724bbae162eb023f76f8f1cd3569e01a33bb6b5ed73ad24`
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Upload date	Dec 10, 2022
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Uploaded via	`twine/4.0.2 CPython/3.9.16`

Release files / torchtoolkit-0.0.4-py3-none-any.whl

Download URL	torchtoolkit-0.0.4-py3-none-any.whl
Size	13.9 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`680586004127abd99de48c0525d36349ccf35fc3fb7fa90dd338640ad31650a2`
BLAKE2b-256 checksum How to use checksums	`9430af7e2f7953289a6877e600809f863ddf4649b6e753d3674178f2f7ede504`
Upload date	Dec 10, 2022
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.2 CPython/3.9.16`

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