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Discrete Cosine Transform (DCT) for pytorch

Project description

DCT (Discrete Cosine Transform) for pytorch

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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 numpy as np
import torch
import torch_dct as dct

x = torch.tensor(np.random.normal(size=(1, 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.

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Filename, size & hash SHA256 hash help File type Python version Upload date
torch_dct-0.1.5-py3-none-any.whl (4.8 kB) Copy SHA256 hash SHA256 Wheel py3 Sep 22, 2018
torch-dct-0.1.5.tar.gz (3.3 kB) Copy SHA256 hash SHA256 Source None Sep 22, 2018

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