Need help for calculating convolution output with stride

[russellfei]

The convolution network modules part:

3 :
{
padding : 3
kW : 7
nInputPlane : 8
gradBias : CudaTensor - size: 8
dW : 1
gradWeight : CudaTensor - size: 8x392
output : CudaTensor - size: 1x8x61x61
fgradInput : CudaTensor - size: 61x61
finput : CudaTensor - size: 392x3721
bias : CudaTensor - size: 8
weight : CudaTensor - size: 8x392
nOutputPlane : 8
gradInput : CudaTensor - empty
kH : 7
dH : 1
}
4 :
{
padding : 2
kW : 7
nInputPlane : 8
gradBias : CudaTensor - size: 12
dW : 1
gradWeight : CudaTensor - size: 12x392
output : CudaTensor - size: 1x12x59x59
fgradInput : CudaTensor - size: 59x59
finput : CudaTensor - size: 392x3481
bias : CudaTensor - size: 12
weight : CudaTensor - size: 12x392
nOutputPlane : 12
gradInput : CudaTensor - empty
kH : 7
dH : 1

}

As is listed above, the output size of layer 3 is 8x61x61

The layer 4 is defined as nn.SpatialConvolutionMM(8,12,7,7,1,1,3))

from which we should expected output is

(61+3-7)/1+1 = 58,

however, the module info shows that the output is 12x59x59.

Anyone can help me figure out this?

thx~

[russellfei]

All right, no one replied. Well, I have got one idea to discriminate the stride and padding mode for nn.SpatialconvolutionMM and nn.SpatialConvolutionCUDA. Keep in mind that SpatialConvolutionMM support stride-mode only in CUDA ( great hint from @soumith ).

To keep short, just list conclusions here:

1. nn.SpatialConvolutionMM( nInputPlane, nOutPlane, kW, kH, dW, dH, padding ) computes output size according to

out_W = ( inWidth + 2*padding - kW ) / dW + 1

1. nn.SpatialConvolutionCUDA( nInputPlane, nOutPlane, kW, kH, dW, dH, padding, partialSum ) use the equation below, I have no idea about partialSum, how to use it? @_@

out_W = ( inWidth + padding - kW ) / dW + 1

1. Thanks to @soumith , the SpatialConvolutionMM uses BDHW layout while SpatialConvolutionCUDA uses 'DHWB' layout.