Minor refactoring of specialized kernel size logic for convolution/correlation kernels

[ajtulloch]

There is a significant amount of code duplicating the logic of

If kernel rows and columns are equal and in [3,4,...,13]:
  instantiate the kernel with the given dimensions
else
  instantiate the kernel with 0.

Thus, we can create a macro which encapsulates this logic. I thought this refactoring slightly improves the readability of the code for the heavy duplication. It also allows us to reduce the file size by ~30%.

[soumith]

i like this. so much cleaner.

[clementfarabet]

yeah definitely an improvement :-)

Is the code tested? Can I just go ahead and merge?

[ajtulloch]

It compiles, but I'm not sure on how to test it - any advice? My only CUDA-enabled hardware is my laptop's NVIDIA GT 650M.

[soumith]

You could run the tests here: https://github.com/torch/cunn/tree/master/test

[ajtulloch]

I'm having issues getting the tests running - it appears to be the interaction with the NVIDIA CUDA libraries and OS X Mavericks. I'll try again tomorrow, but I'll also try spinning up a Linux GPU box.

If @soumith or @clementfarabet have the time and inclination, it may be quicker for you to patch this and see if the tests pass on your setup. It's a mechanical change that makes it hard to introduce bugs that don't manifest at compile time, but it's definitely worth checking.

[ajtulloch]

Tests passed:

tulloch at ajt92c in ~/Code/torch/cunn (master)
∴ th test/test.lua 
Running 38 tests
______________________________________  ==> Done Completed 56 asserts in 38 tests with 0 errors
--------------------------------------------------------------------------------

SpatialConvolution.backward 26x114x34 o 13x8 -> 33x102x27:       average speedup is 0.14095951177748
SpatialMaxPoolingCUDA.backward 32x48x120x120 o 4x4 -> 32x48x30x30:       average speedup is 0.41406701457109
SpatialMaxPooling.backward 41x188x244 o 4x4 -> 41x47x61:         average speedup is 0.80568943223228
SoftMax.backward 24 -> 24:       average speedup is 0.1255230125523
LogSoftMax.backward 21 -> 21:    average speedup is 0.10204081632653
SpatialSubSampling.backward 52x248x208 o 4x4 -> 52x62x52:        average speedup is 0.34685705906902
SpatialConvolution.backward 8x1x28x34 o 3x8 -> 8x13x26x27:       average speedup is 0.13519833708644
SpatialLPPooling.backward (P=2 only) 31x99x136 o 3x4 -> 31x33x34:        average speedup is 1.3775691970403
SoftMax forward 78 -> 78:        average speedup is 0.026968716289105
Max.backward 995x8:      average speedup is 0.34328358208955
SpatialMaxPooling.backward 8x17x64x180 o 2x3 -> 8x17x32x60:      average speedup is 1.1033724340176
Sqrt.backward 31 -> 31:          average speedup is 0.38636363636364
Square forward 92 -> 92:         average speedup is 0.22826086956522
MSECriterion2 4251 :     average speedup is 0.43875278396437
SpatialSubSampling.forward 5x43x98x350 o 2x4 -> 5x43x33x174:     average speedup is 1.7876310272537
SpatialSubSampling.backward 9x5x156x168 o 4x3 -> 9x5x39x56:      average speedup is 1.0902288412641
SpatialLPPooling.forward (P=2 only) 5x808x582 o 4x3 -> 5x202x194:        average speedup is 5.9070947809776
Sqrt forward 40 -> 40:   average speedup is 0.079497907949791
Threshold.backward 73 -> 73:     average speedup is 0.28099173553719
Square.backward 6 -> 6:          average speedup is 0.1984126984127
SpatialConvolution.forward 19x223x388 o 12x4 -> 18x212x193 [s: 1x2]:     average speedup is 0.61608696142845
MSECriterion 4251 :      average speedup is 0.3218954248366
SpatialMaxPooling.forward 27x279x261 o 3x3 -> 27x93x87:          average speedup is 3.8389252203583
SpatialConvolutionCUDA.forward 32x16x36x36 o 6x6 -> 32x32x16x16 [s: 2x2]:        average speedup is 31.217450039596
Max forward 50x96:       average speedup is 0.046189376443418
Sigmoid.backward 17 -> 17:       average speedup is 0.22826086956522
Threshold forward 50 -> 50:      average speedup is 0.36708860759494
SpatialMaxPoolingCUDA.forward 32x32x60x60 o 2x2 -> 32x32x30x30:          average speedup is 13.250921861282
SpatialMaxPooling.forward 6x47x108x916 o 2x4 -> 6x47x54x229:     average speedup is 4.5511924774112
SpatialSubSampling.forward 38x712x402 o 4x2 -> 38x237x101:       average speedup is 1.9903135610146
Abs forward 41 -> 41:    average speedup is 0.026315789473684
SpatialConvolution.backward 32x4x12x12 o 8x8 -> 32x32x5x5:       average speedup is 5.3264571629213
Abs.backward 51 -> 51:   average speedup is 0.32954545454545
LogSoftMax forward 68 -> 68:     average speedup is 0.018338727076591
Tanh.backward 91 -> 91:          average speedup is 0.27173913043478
Sigmoid forward 25 -> 25:        average speedup is 0.16190476190476
Tanh forward 70 -> 70:   average speedup is 0.050086355785838
SpatialConvolution.forward 7x25x20x21 o 8x4 -> 7x52x13x18 [s: 1x1]:      average speedup is 0.1608589350089

[soumith]

awesome! thanks.

[ajtulloch]

Did you want any further testing, etc?

[soumith]

@andresy @clementfarabet are we waiting on this?

[clementfarabet]

Ok just merged it. I'm going to start using it now.

[ajtulloch]

Thanks!