May have future issues in input size in basic Unet

[Andrew-Ma-2001]

Hi, when I was implementing the pytorch model in the class U_net, I found that with some values of input size (for example 700x700), there could be a problem inside

d5 = torch.cat((e4, d5), dim=1)

where e4 and d5 may not be the same size, instead they will be different only with one. I guess is with the up conv that in some covs the calculate of size is using int(), some are larger than .5 so it increase 1.

• For people who want to solve this quick, I recommend to change the size to (400,400) or (800,800) which will work

[bigmb]

Thanks for clarifying for others about this issue. It's because when you go deep in the network, it tries to divide in multiples of 2. So if you go 4 layers deep, that has to be divisible by 16.