Skip to main content

Discrete Cosine Transform (DCT) for pytorch

Project description

DCT (Discrete Cosine Transform) for pytorch

Build Status codecov PyPI version PyPI version PyPI status GitHub license

This library implements DCT in terms of the built-in FFT operations in pytorch so that back propagation works through it, on both CPU and GPU. For more information on DCT and the algorithms used here, see Wikipedia and the paper by J. Makhoul. This StackExchange article might also be helpful.

The following are currently implemented:

  • 1-D DCT-I and its inverse (which is a scaled DCT-I)
  • 1-D DCT-II and its inverse (which is a scaled DCT-III)
  • 2-D DCT-II and its inverse (which is a scaled DCT-III)
  • 3-D DCT-II and its inverse (which is a scaled DCT-III)

Install

pip install torch-dct

Requires torch>=0.4.1 (lower versions are probably OK but I haven't tested them).

You can run test by getting the source and run pytest. To run the test you also need scipy installed.

Usage

import torch
import torch_dct as dct

x = torch.randn(200)
X = dct.dct(x)   # DCT-II done through the last dimension
y = dct.idct(X)  # scaled DCT-III done through the last dimension
assert (torch.abs(x - y)).sum() < 1e-10  # x == y within numerical tolerance

dct.dct1 and dct.idct1 are for DCT-I and its inverse. The usage is the same.

Just replace dct and idct by dct_2d, dct_3d, idct_2d, idct_3d, etc to get the multidimensional versions.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

torch-dct-0.1.6.tar.gz (4.8 kB view hashes)

Uploaded Source

Built Distribution

torch_dct-0.1.6-py3-none-any.whl (5.1 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page