Make Unet configurable

examples/Project.toml

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+[deps]
+Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
+FourierTools = "b18b359b-aebc-45ac-a139-9c0ccbb2871e"
+ImageIO = "82e4d734-157c-48bb-816b-45c225c6df19"
+IndexFunArrays = "613c443e-d742-454e-bfc6-1d7f8dd76566"
+Noise = "81d43f40-5267-43b7-ae1c-8b967f377efa"
+NDTools = "98581153-e998-4eef-8d0d-5ec2c052313d"
+TestImages = "5e47fb64-e119-507b-a336-dd2b206d9990"
+View5D = "90d841e0-6953-4e90-9f3a-43681da8e949"

examples/deconvolve.jl

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+# Example using U-net to deconvolve an image
+
+using UNet, Flux, TestImages, View5D, Noise, NDTools, FourierTools, IndexFunArrays
+
+img = 100f0 .* Float32.(testimage("resolution_test_512"))
+
+u = Unet(); 
+
+u = gpu(u);
+function loss(x, y)
+    return Flux.mse(u(x),y)
+end
+opt = Momentum()
+
+# selects a tile at a random (default) or predifined (ctr) position returning tile and center.
+function get_tile(img, tile_size=(128,128), ctr = (rand(tile_size[1]÷2:size(img,1)-tile_size[1]÷2),rand(tile_size[2]÷2:size(img,2)-tile_size[2]÷2)) )
+    return select_region(img,new_size=tile_size, center=ctr), ctr
+end
+
+R_max = 70;
+sz = size(img); psf = abs2.(ift(disc(Float32, sz, R_max))); psf ./= sum(psf); conv_img = conv_psf(img,psf);
+
+scale = 0.5f0/maximum(conv_img)
+patch = (128,128)
+for n in 1:2000
+    println("Iteration: $n")
+    myimg, pos = get_tile(conv_img,patch)
+    # image to denoise
+    # nimg1 = gpu(reshape(scale .* myimg,(size(myimg)...,1,1))); # gpu(scale.*reshape(poisson(myimg),(size(myimg)...,1,1)))
+    nimg1 = gpu(scale.*reshape(poisson(Float64.(myimg)),(size(myimg)...,1,1)))
+    # goal image (with noise)
+    pimg, pos = get_tile(img,patch,pos)
+    pimg = gpu(scale.*reshape(pimg,(size(myimg)...,1,1)))
+    rep = Iterators.repeated((nimg1, pimg), 4);
+    Flux.train!(loss, Flux.params(u), rep, opt)
+end
+
+# apply the net to the whole image instead:
+nimg = gpu(scale .* reshape(conv_img,(size(conv_img)...,1,1))); # gpu(scale.*reshape(poisson(conv_img),(size(conv_img)...,1,1)))
+nimg2 = gpu(scale.*reshape(poisson(Float64.(conv_img)),(size(conv_img)...,1,1)))
+# display the images using View5D
+@ve img nimg u(nimg) nimg2 u(nimg2)

examples/noise2noise.jl

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+# Example using U-net for a noise2noise problem
+
+using UNet, Flux, TestImages, View5D, Noise, NDTools
+
+img = 10.0 .* Float32.(testimage("resolution_test_512"))
+
+u = Unet(); 
+
+u = gpu(u);
+function loss(x, y)
+    # return mean(abs2.(u(x) .-y))
+    return Flux.mse(u(x),y)
+end
+opt = Momentum()
+
+# selects a tile at a random (default) or predifined (ctr) position returning tile and center.
+function get_tile(img, tile_size=(128,128), ctr = (rand(tile_size[1]÷2:size(img,1)-tile_size[1]÷2),rand(tile_size[2]÷2:size(img,2)-tile_size[2]÷2)) )
+    return select_region(img,new_size=tile_size, center=ctr), ctr
+end
+
+sz = size(img); 
+scale = 0.5/maximum(img)
+patch = (128,128)
+for n in 1:100
+    println("Iteration: $n")
+    myimg, pos = get_tile(img,patch)
+    # image to denoise
+    nimg1 = gpu(scale.*reshape(poisson(myimg),(size(myimg)...,1,1)))
+    # goal image (with noise)
+    nimg2 = gpu(scale.*reshape(poisson(myimg),(size(myimg)...,1,1)))
+    rep = Iterators.repeated((nimg1, nimg2), 1);
+    Flux.train!(loss, Flux.params(u), rep, opt)
+end
+
+# apply the net to the whole image instead:
+nimg = gpu(scale.*reshape(poisson(img),(size(img)...,1,1)));
+# display the images using View5D
+@ve img nimg u(nimg)