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        "author": "Ziyang Hu",
        "author_email": "hu.ziyang@cantab.net",
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        "description": "# DCT (Discrete Cosine Transform) for pytorch\n\n[![Build Status](https://travis-ci.com/zh217/torch-dct.svg?branch=master)](https://travis-ci.com/zh217/torch-dct)\n[![codecov](https://codecov.io/gh/zh217/torch-dct/branch/master/graph/badge.svg)](https://codecov.io/gh/zh217/torch-dct)\n[![PyPI version](https://img.shields.io/pypi/v/torch-dct.svg)](https://pypi.python.org/pypi/torch-dct/)\n[![PyPI version](https://img.shields.io/pypi/pyversions/torch-dct.svg)](https://pypi.python.org/pypi/torch-dct/)\n[![PyPI status](https://img.shields.io/pypi/status/torch-dct.svg)](https://pypi.python.org/pypi/torch-dct/)\n[![GitHub license](https://img.shields.io/github/license/zh217/torch-dct.svg)](https://github.com/zh217/torch-dct/blob/master/LICENSE)\n\n\nThis library implements DCT in terms of the built-in FFT operations in pytorch so that\nback propagation works through it, on both CPU and GPU. For more information on\nDCT and the algorithms used here, see \n[Wikipedia](https://en.wikipedia.org/wiki/Discrete_cosine_transform) and the paper by\n[J. Makhoul](https://ieeexplore.ieee.org/document/1163351/). This\n[StackExchange article](https://dsp.stackexchange.com/questions/2807/fast-cosine-transform-via-fft)\nmight also be helpful.\n\nThe following are currently implemented:\n\n* 1-D DCT-I and its inverse (which is a scaled DCT-I)\n* 1-D DCT-II and its inverse (which is a scaled DCT-III)\n* 2-D DCT-II and its inverse (which is a scaled DCT-III)\n* 3-D DCT-II and its inverse (which is a scaled DCT-III)\n\n## Install\n\n```\npip install torch-dct\n```\n\nRequires `torch>=0.4.1` (lower versions are probably OK but I haven't tested them).\n\nYou can run test by getting the source and run `pytest`. To run the test you also\nneed `scipy` installed.\n\n## Usage\n\n```python\nimport numpy as np\nimport torch\nimport torch_dct as dct\n\nx = torch.tensor(np.random.normal(size=(1, 200)))\nX = dct.dct(x)   # DCT-II done through the last dimension\ny = dct.idct(X)  # scaled DCT-III done through the last dimension\nassert (torch.abs(x - y)).sum() < 1e-10  # x == y within numerical tolerance\n```\n\n`dct.dct1` and `dct.idct1` are for DCT-I and its inverse. The usage is the same.\n\nJust replace `dct` and `idct` by `dct_2d`, `dct_3d`, `idct_2d`, `idct_3d`, etc\nto get the multidimensional versions.\n\n",
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