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dct_cuda's Issues

Enhancement on the project

Generality

  • The 2d implementation should also support 1d input.
  • What if input is not a power of 2

Specificity

  • Optimization for cases where M = N = 2^x

FFT implementation

Documentation

  • Add comments and documentations

Building on Colab

Hi, I'm quite new to using Torch with CUDA kernels. I'm struggling to build it on Colab, do you have any advice or resources you can point me to?

Thank you so much!

Result is different with opencv

Thanks for your great work!
I cloned your project ,and try to use it to do something . Then I try to generate a dct result by opencv , I used cv2.dct,then ,write the result to harddrive ad you did in script "src/dct_pytorch.py" ,but the results are quite different ,see below ,it's produced by your bin file bin/dct2d_cuda

[I] Error! Results are incorrect.
index: 0, result: 20.012664, GT: 5123.242097, scale: 0.003906
index: 1, result: -0.013377, GT: -4.842896, scale: 0.002762
index: 2, result: 0.011195, GT: 4.052967, scale: 0.002762
index: 3, result: -0.001353, GT: -0.489889, scale: 0.002762
index: 4, result: 0.001098, GT: 0.397660, scale: 0.002762
index: 5, result: 0.001302, GT: 0.471319, scale: 0.002762
index: 6, result: -0.001707, GT: -0.617927, scale: 0.002762
index: 7, result: 0.002289, GT: 0.828868, scale: 0.002762
index: 8, result: 0.009278, GT: 3.359035, scale: 0.002762
index: 9, result: -0.000801, GT: -0.289940, scale: 0.002762
index: 10, result: 0.005129, GT: 1.856900, scale: 0.002762
index: 11, result: 0.009032, GT: 3.269778, scale: 0.002762
index: 12, result: -0.000099, GT: -0.035807, scale: 0.002762
index: 13, result: 0.001421, GT: 0.514459, scale: 0.002762
index: 14, result: 0.010225, GT: 3.701966, scale: 0.002762
index: 15, result: -0.007186, GT: -2.601680, scale: 0.002762
index: 16, result: 0.000024, GT: 0.008796, scale: 0.002762
index: 17, result: -0.011907, GT: -4.310903, scale: 0.002762
index: 18, result: -0.010924, GT: -3.954782, scale: 0.002762
index: 19, result: -0.010288, GT: -3.724749, scale: 0.002762
index: 20, result: -0.005418, GT: -1.961669, scale: 0.002762
index: 21, result: 0.010174, GT: 3.683218, scale: 0.002762
index: 22, result: -0.002510, GT: -0.908892, scale: 0.002762
index: 23, result: -0.002990, GT: -1.082518, scale: 0.002762
index: 24, result: -0.010692, GT: -3.870906, scale: 0.002762
index: 25, result: -0.001337, GT: -0.483995, scale: 0.002762
index: 26, result: -0.002254, GT: -0.816180, scale: 0.002762
index: 27, result: 0.008054, GT: 2.916009, scale: 0.002762
index: 28, result: -0.003147, GT: -1.139196, scale: 0.002762
index: 29, result: 0.000077, GT: 0.027921, scale: 0.002762
index: 30, result: -0.008187, GT: -2.964190, scale: 0.002762
index: 31, result: 0.012992, GT: 4.703643, scale: 0.002762
index: 32, result: 0.006220, GT: 2.251814, scale: 0.002762
index: 33, result: 0.006298, GT: 2.280054, scale: 0.002762
index: 34, result: 0.008639, GT: 3.127580, scale: 0.002762
index: 35, result: 0.018885, GT: 6.837267, scale: 0.002762
index: 36, result: 0.001276, GT: 0.462017, scale: 0.002762
index: 37, result: -0.004175, GT: -1.511421, scale: 0.002762
index: 38, result: 0.001766, GT: 0.639508, scale: 0.002762
index: 39, result: 0.016754, GT: 6.065430, scale: 0.002762
index: 40, result: 0.008063, GT: 2.919008, scale: 0.002762
index: 41, result: -0.015651, GT: -5.666278, scale: 0.002762
index: 42, result: -0.001663, GT: -0.602239, scale: 0.002762
index: 43, result: 0.006152, GT: 2.227197, scale: 0.002762
index: 44, result: -0.001554, GT: -0.562456, scale: 0.002762
index: 45, result: 0.010431, GT: 3.776602, scale: 0.002762
index: 46, result: 0.002027, GT: 0.733966, scale: 0.002762
index: 47, result: 0.003972, GT: 1.438157, scale: 0.002762
index: 48, result: -0.007528, GT: -2.725495, scale: 0.002762
index: 49, result: -0.005630, GT: -2.038441, scale: 0.002762
index: 50, result: -0.007534, GT: -2.727680, scale: 0.002762
index: 51, result: 0.005743, GT: 2.079118, scale: 0.002762
index: 52, result: 0.004871, GT: 1.763623, scale: 0.002762
index: 53, result: -0.019159, GT: -6.936130, scale: 0.002762
index: 54, result: 0.002538, GT: 0.918893, scale: 0.002762
index: 55, result: 0.001079, GT: 0.390727, scale: 0.002762
index: 56, result: -0.004035, GT: -1.460880, scale: 0.002762
index: 57, result: -0.002547, GT: -0.922088, scale: 0.002762
index: 58, result: -0.003600, GT: -1.303184, scale: 0.002762
index: 59, result: -0.005225, GT: -1.891828, scale: 0.002762
index: 60, result: 0.001384, GT: 0.501159, scale: 0.002762
index: 61, result: 0.015481, GT: 5.604717, scale: 0.002762
index: 62, result: 0.003500, GT: 1.267265, scale: 0.002762
index: 63, result: -0.004546, GT: -1.645787, scale: 0.002762
[D] dct 2D takes 220.885 ms
[D] dct 2D (1024 * 1024) takes average 220.897 ms

I want to know the reson ,and how can I produce the same result as OpenCV do ?
Looking forward to your answers !
THX !!!!

Some ideas of improvement on dct and idct

  • multiplying scale in precomputeExpk

  • zero paddings to avoid branch divergence

  • in-place or out-of-place cufft, especially in idct

  • number of threads in idct M/2 * N/2 or M/2 * (N/2+1)

  • other improvements based on profiling

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