CODEWITHSUNDEEP
numpycudatheanoconvolution
Cheng Guo
Cheng Guo

Reputation: 353

Why are the convolution outputs calculated with theano and numpy not the same?

I made a simple example ipython notebook to calculate convolution with theano and with numpy, however the results are different. Does anybody know where is the mistake?

import theano
import numpy
from theano.sandbox.cuda import dnn
import theano.tensor as T

Define the input image x0:

x0 = numpy.array([[[[  7.61323881,   0.        ,   0.        ,   0.        ,
            0.        ,   0.        ],
         [ 25.58142853,   0.        ,   0.        ,   0.        ,
            0.        ,   0.        ],
         [  7.51445341,   0.        ,   0.        ,   0.        ,
            0.        ,   0.        ],
         [  0.        ,  12.74498367,   4.96315479,   0.        ,
            0.        ,   0.        ],
         [  0.        ,   0.        ,   0.        ,   0.        ,
            0.        ,   0.        ],
         [  0.        ,   0.        ,   0.        ,   0.        ,
            0.        ,   0.        ]]]], dtype='float32')

x0.shape
# (1, 1, 6, 6)

Define the convolution kernel:

w0 = numpy.array([[[[-0.0015835 , -0.00088091,  0.00226375,  0.00378434,  0.00032208,
          -0.00396959],
         [-0.000179  ,  0.00030951,  0.00113849,  0.00012536, -0.00017198,
          -0.00318825],
         [-0.00263921, -0.00383847, -0.00225416, -0.00250589, -0.00149073,
          -0.00287099],
         [-0.00149283, -0.00312137, -0.00431571, -0.00394508, -0.00165113,
          -0.0012118 ],
         [-0.00167376, -0.00169753, -0.00373235, -0.00337372, -0.00025546,
           0.00072154],
         [-0.00141197, -0.00099017, -0.00091934, -0.00226817, -0.0024105 ,
          -0.00333713]]]], dtype='float32')

w0.shape
# (1, 1, 6, 6)

Calculate the convolution with theano and cudnn:

X = T.tensor4('input')
W = T.tensor4('W')
conv_out = dnn.dnn_conv(img=X, kerns=W)
convolution = theano.function([X, W], conv_out)
numpy.array(convolution(x0, w0))
# array([[[[-0.04749081]]]], dtype=float32)

Calculate convolution with numpy (note the result is different):

numpy.sum(x0 * w0)
# -0.097668208

Upvotes: 0

Views: 202

Answers (1)

egpbos
egpbos

Reputation: 502

I'm not exactly sure what kind of convolution you are trying to compute, but it seems to me that numpy.sum(x0*w0) might not be the way to do it. Does this help?

import numpy as np
# ... define x0 and w0 like in your example ...
np_convolution = np.fft.irfftn(np.fft.rfftn(x0) * np.fft.rfftn(w0))

The last element of the resulting array, i.e. np_convolution[-1,-1,-1,-1] is -0.047490807560833327, which seems to be the answer you're looking for in your notebook.

Upvotes: 3

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