Hello everyone,
I've trying to implement a deconvolution in a convnet and I'm stuck because
I'm mainly confused. I've been searching the forum and I've found different
way to implement it and I'm not quite sure which one is the correct or how
to properly implement it. I was hopping that maybe you guys can shed some
light into this issue.
As I've mentioned I have a convnet with 4*[conv>rely->pool] layers and the
next step is to implement a deconvolution.
So far I've found the following ways to implement it. I'll start with what
I initially started to do but was confused. (Also the terminology is a bit
confused is it a deconvolution or a transposed convolution?)
This the output from my 4th layer in the convnet. In this stage the input
image is of size 28x28 giving out 1024 feature maps.
h4 = relu(conv2d(h3_pool, W[5], mode='valid'))
h4 = relu(conv2d(h4, W[6], mode='valid'))
h4_pool = pool_2d(h3, ds=(2,2), st=(2, 2))
And now I want to implement an upscale/deconvolution where the result would
be and image of dimension 56x56 providing 1024 feature maps.
This what I tried to do:
upscale1 = T.extra_ops.repeat(h4, 2, axis=3)
upscale1 = T.extra_ops.repeat(h4, 2, axis=2)
concat1 = T.concatenate([upscale1, h4], axis=1)
deconv1 = T.nnet.abstract_conv.AbstractConv2d_gradInputs(concat1, kshp=2x2,
border_mode='valid', subsample=(56, 56), filter_flip=False)
output1 = deconv1(filters, input, output_shape[2:])
In this case I don't know what to use for kasha, is the weight matrix from
h4 or h4_pool?
Am I using subsampling correct?
I am also totally lost in what should I use for the arguments of deconv1()?
filters? input?, output_shape?
The other ways I've seen people do this is the following:
shp = h4.shape
upsample = T.zeros((shp[0], shp[1], shp[2] * 2, shp[3] * 2), dtype=h4.dtype)
upsample = T.set_subtensor(upsample[:, :, ::2, ::2], h4)
upsampled_convolution = T.nnet.conv2d(upsample, filters.dimshuffle(1, 0, 2,
3)[:, :, ::-1, ::-1], border_mode='full')
f = theano.function([h4], upsampled_convolution)
Another way is by using some dummy convolution and the gradient of it if
I'm not mistaken. To be honest I don't really understand the below method.
def deconv(X, w, subsample=(1, 1), border_mode=(0, 0), conv_mode='conv'):
"""
sets up dummy convolutional forward pass and uses its grad as deconv
currently only tested/working with same padding
"""
img = gpu_contiguous(X)
kerns = gpu_contiguous(w)
desc = GpuDnnConvDesc(border_mode=border_mode, subsample=subsample,
conv_mode=conv_mode)(gpu_alloc_empty(img.shape[0], kerns.shape[1],
img.shape[2]*subsample[0], img.shape[3]*subsample[1]).shape, kerns.shape)
out = gpu_alloc_empty(img.shape[0], kerns.shape[1],
img.shape[2]*subsample[0], img.shape[3]*subsample[1])
d_img = GpuDnnConvGradI()(kerns, img, out, desc)
return d_img
Any help or explanation will be much appreciated.
Thanks!
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