Im trying to port a piece of tensorflow code originally written by VinceMarron 12 to pytorch but am yet to replicate the same results. The tf code is as follows:
d=tf.range(kerneldim, dtype=tf.float32)
d=tf.expand_dims((d-d[kerneldim//2])**2,0)
dist=d+tf.transpose(d)
kernel=tf.exp(-dist*(1./(2.*sigma_sq)))
kernel = kernel/tf.reduce_sum(kernel)
kernel = tf.reshape(kernel, (kerneldim, kerneldim,1,1))
kernel = tf.tile(kernel, (1,1,3,1))

return tf.nn.depthwise_conv2d(x, kernel, [1,1,1,1], padding='SAME')

My attempt at doing the same operation in pytorch:
d = torch.arange(0.,float(kerneldim))
d = torch.unsqueeze((d-d[kernel_size//2])**2,0)
dist = d+d.transpose(0,1)
kernel = torch.exp(-dist*(1./(2.*sigma_sq)))
kernel /= torch.sum(kernel)
kernel = kernel.view(1,kerneldim,kerneldim)
kernel = kernel.repeat(3,1,1,1)


conv = nn.Conv2d(3,3,kerneldim,padding=7,groups=3,bias=False)

c.weight = torch.nn.Parameter(kernel)

return conv(Variable(x,volatile=True)).data

I am getting very different results. It runs but does not replicate the tensorflow results.
I have not had the time to dive deeper into depthwise convolutions. I am also really terrible at tensorflow.
As I do not have enough time to research these things more in depth I just tried to port the code directly.
This is something im doing in my free time for fun so if you want to help me please dont waste too much of your time on me as this is in no way critical.
Thanks in advance!