This pacakge provides a generic UNet implemented in Julia.
The package is built on top of Flux.jl, and therefore can be extended as needed
julia> u = Unet()
UNet:
ConvDown(64, 64)
ConvDown(128, 128)
ConvDown(256, 256)
ConvDown(512, 512)
UNetConvBlock(1, 3)
UNetConvBlock(3, 64)
UNetConvBlock(64, 128)
UNetConvBlock(128, 256)
UNetConvBlock(256, 512)
UNetConvBlock(512, 1024)
UNetConvBlock(1024, 1024)
UNetUpBlock(1024, 512)
UNetUpBlock(1024, 256)
UNetUpBlock(512, 128)
UNetUpBlock(256, 64)The default input channel dimension is expected to be 1 ie. grayscale. To support different channel images, you can pass the channels to Unet.
julia> u = Unet(3) # for RGB imagesThe input size can be any power of two sized batch. Something like (256,256, channels, batch_size).
The default output channel dimension is the input channel dimension. So, 1 for a Unet() and e.g. 3 for a Unet(3).
The output channel dimension can be set by supplying a second argument:
julia> u = Unet(3, 5) # 3 input channels, 5 output channels.To train the model on UNet, it is as simple as calling gpu on the model.
julia> u = Unet();
julia> u = gpu(u);
julia> r = gpu(rand(Float32, 256, 256, 1, 1));
julia> size(u(r))
(256, 256, 1, 1)Training UNet is a breeze too.
You can define your own loss function, or use Flux binary cross entropy implementation.
using UNet, Flux, Base.Iterators
import Flux.Losses.binarycrossentropy
device = gpu #cpu
function loss(x, y)
op = clamp.(u(x), 0.001f0, 1.f0)
binarycrossentropy(op,y)
end
u = Unet() |> device
w = rand(Float32, 256, 256, 1, 1) |> device
w′ = rand(Float32, 256, 256, 1, 1) |> device
rep = Iterators.repeated((w, w′), 10)
opt = ADAM()
Flux.train!(loss, Flux.params(u), rep, opt, cb = () -> @show(loss(w, w′)))The package is an implementation of the paper, and all credits of the model itself go to the respective authors.