torch/cunn — pull request #6 by ajtulloch (2014-04-04 16:31:38+00:00)
Title: Fix compiler warnings in SpatialConvolutionCUDA.cu

Just some minor cleanups.

```
Running 38 tests
______________________________________ ==> Done Completed 56 asserts in 38 tests with 0 errors
--------------------------------------------------------------------------------

SpatialMaxPooling.forward 9x32x362x332 o 2x4 -> 9x32x181x83: average speedup is 4.7224495937406
SoftMax forward 37 -> 37: average speedup is 0.17469879518072
LogSoftMax.backward 39 -> 39: average speedup is 0.12274368231047
SpatialSubSampling.forward 7x42x258x106 o 2x4 -> 7x42x65x35: average speedup is 0.8765923566879
Tanh.backward 56 -> 56: average speedup is 0.14529914529915
SpatialConvolution.forward 12x5x35x51 o 15x5 -> 12x21x11x47 [s: 2x1]: average speedup is 0.16042928627864
Tanh forward 6 -> 6: average speedup is 0.0063067608476287
Sigmoid forward 45 -> 45: average speedup is 0.030266343825666
Threshold forward 97 -> 97: average speedup is 0.38532110091743
Sqrt forward 10 -> 10: average speedup is 0.04113475177305
SpatialLPPooling.backward (P=2 only) 9x102x86 o 2x2 -> 9x51x43: average speedup is 2.0689655172414
SpatialConvolution.backward 32x8x15x15 o 7x7 -> 32x32x5x5: average speedup is 8.6660210293304
Sigmoid.backward 30 -> 30: average speedup is 0.2
Threshold.backward 66 -> 66: average speedup is 0.036755386565272
Max.backward 620x98: average speedup is 0.12307692307692
Sqrt.backward 94 -> 94: average speedup is 0.304
SpatialMaxPooling.backward 6x38x138x100 o 3x2 -> 6x38x46x50: average speedup is 0.88458819370802
LogSoftMax forward 26 -> 26: average speedup is 0.0625
SpatialConvolution.forward 3x290x135 o 4x14 -> 57x144x122 [s: 2x1]: average speedup is 0.27754361471604
SpatialConvolution.backward 23x137x57 o 13x12 -> 27x125x46: average speedup is 0.13236147438238
SoftMax.backward 7 -> 7: average speedup is 0.11261261261261
Square.backward 88 -> 88: average speedup is 0.34862385321101
MSECriterion 3410 : average speedup is 0.059097978227061
SpatialLPPooling.forward (P=2 only) 23x672x244 o 4x2 -> 23x168x122: average speedup is 9.208041958042
SpatialConvolutionCUDA.forward 32x8x70x70 o 8x8 -> 32x32x63x63 [s: 1x1]: average speedup is 11.186765313119
SpatialSubSampling.forward 11x718x456 o 4x4 -> 11x239x227: average speedup is 2.2360584395399
SpatialMaxPooling.forward 51x354x444 o 2x2 -> 51x177x222: average speedup is 7.6214793485108
SpatialMaxPooling.backward 9x164x66 o 4x2 -> 9x41x33: average speedup is 0.47185430463576
SpatialSubSampling.backward 5x34x126x118 o 2x2 -> 5x34x63x59: average speedup is 1.2965170223396
Abs forward 80 -> 80: average speedup is 0.0336
SpatialConvolution.backward 8x1x58x54 o 13x8 -> 8x55x46x47: average speedup is 0.13817821618874
Abs.backward 18 -> 18: average speedup is 0.32608695652174
SpatialMaxPoolingCUDA.backward 32x16x124x124 o 4x4 -> 32x16x31x31: average speedup is 0.47886466426998
SpatialSubSampling.backward 12x183x168 o 3x4 -> 12x61x42: average speedup is 0.3054620405359
MSECriterion2 3410 : average speedup is 0.25850340136054
SpatialMaxPoolingCUDA.forward 32x32x96x96 o 3x3 -> 32x32x32x32: average speedup is 12.043622569938
Square forward 66 -> 66: average speedup is 0.10596026490066
Max forward 321x54: average speedup is 0.30489731437599
```