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nagadomi
parent
06246e0d78
commit
1f18d1919a
280
lib/srcnn.lua
280
lib/srcnn.lua
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@ -710,281 +710,8 @@ function srcnn.upconv_refine(backend, ch)
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return model
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end
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-- cascade u-net
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function srcnn.cunet_v1(backend, ch)
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function unet_branch(insert, backend, n_input, n_output, depad)
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local block = nn.Sequential()
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local pooling = SpatialConvolution(backend, n_input, n_input, 2, 2, 2, 2, 0, 0) -- downsampling
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--block:add(w2nn.Print())
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block:add(pooling)
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block:add(insert)
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block:add(SpatialFullConvolution(backend, n_output, n_output, 2, 2, 2, 2, 0, 0))-- upsampling
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local parallel = nn.ConcatTable(2)
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parallel:add(nn.SpatialZeroPadding(-depad, -depad, -depad, -depad))
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parallel:add(block)
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local model = nn.Sequential()
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model:add(parallel)
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model:add(nn.JoinTable(2))
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return model
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end
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function unet_conv(n_input, n_middle, n_output)
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local model = nn.Sequential()
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model:add(SpatialConvolution(backend, n_input, n_middle, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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model:add(SpatialConvolution(backend, n_middle, n_output, 3, 3, 1, 1, 0, 0))
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return model
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end
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function unet(backend, ch, deconv)
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--
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local block1 = unet_conv(128, 256, 128)
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local block2 = nn.Sequential()
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block2:add(unet_conv(32, 64, 128))
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block2:add(unet_branch(block1, backend, 128, 128, 4))
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block2:add(unet_conv(128*2, 64, 32))
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local model = nn.Sequential()
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model:add(unet_conv(ch, 32, 32))
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model:add(unet_branch(block2, backend, 32, 32, 16))
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model:add(SpatialConvolution(backend, 64, 128, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1))
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if deconv then
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model:add(SpatialFullConvolution(backend, 128, ch, 4, 4, 2, 2, 3, 3))
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else
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model:add(SpatialConvolution(backend, 128, ch, 3, 3, 1, 1, 0, 0))
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end
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return model
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end
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local model = nn.Sequential()
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local con = nn.ConcatTable()
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local aux_con = nn.ConcatTable()
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model:add(unet(backend, ch, true))
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con:add(unet(backend, ch, false))
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con:add(nn.SpatialZeroPadding(-20, -20, -20, -20))
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aux_con:add(nn.Sequential():add(nn.CAddTable()):add(w2nn.InplaceClip01())) -- cascaded unet output
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aux_con:add(nn.Sequential():add(nn.SelectTable(2)):add(w2nn.InplaceClip01())) -- single unet output
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model:add(con)
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model:add(aux_con)
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model:add(w2nn.AuxiliaryLossTable(1)) -- auxiliary loss for single unet output
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model.w2nn_arch_name = "cunet_v1"
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model.w2nn_offset = 60
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model.w2nn_scale_factor = 2
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model.w2nn_channels = ch
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model.w2nn_resize = true
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-- 72, 128, 256 are valid
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--model.w2nn_input_size = 128
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return model
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end
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-- cascade u-net
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function srcnn.cunet_v2(backend, ch)
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function unet_branch(insert, backend, n_input, n_output, depad)
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local block = nn.Sequential()
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local pooling = SpatialConvolution(backend, n_input, n_input, 2, 2, 2, 2, 0, 0) -- downsampling
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--block:add(w2nn.Print())
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block:add(pooling)
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block:add(insert)
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block:add(SpatialFullConvolution(backend, n_output, n_output, 2, 2, 2, 2, 0, 0))-- upsampling
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local parallel = nn.ConcatTable(2)
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parallel:add(nn.SpatialZeroPadding(-depad, -depad, -depad, -depad))
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parallel:add(block)
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local model = nn.Sequential()
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model:add(parallel)
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model:add(nn.CAddTable(2))
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return model
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end
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function unet_conv(n_input, n_middle, n_output)
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local model = nn.Sequential()
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model:add(SpatialConvolution(backend, n_input, n_middle, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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model:add(SpatialConvolution(backend, n_middle, n_output, 3, 3, 1, 1, 0, 0))
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return model
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end
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-- res unet
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function unet(backend, ch, deconv)
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local block1 = unet_conv(128, 256, 128)
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local block2 = nn.Sequential()
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block2:add(unet_conv(64, 128, 128))
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block2:add(unet_branch(block1, backend, 128, 128, 4))
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block2:add(unet_conv(128, 128, 64))
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local model = nn.Sequential()
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model:add(nn.SpatialZeroPadding(-1, -1, -1, -1))
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model:add(SpatialConvolution(backend, ch, 64, 3, 3, 1, 1, 0, 0))
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model:add(unet_branch(block2, backend, 64, 64, 16))
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if deconv then
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model:add(SpatialConvolution(backend, 64, 128, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1))
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model:add(SpatialFullConvolution(backend, 128, 64, 4, 4, 2, 2, 3, 3))
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else
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model:add(SpatialConvolution(backend, 64, 64, 3, 3, 1, 1, 0, 0))
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end
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return model
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end
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local model = nn.Sequential()
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local con = nn.ConcatTable()
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local aux_con = nn.ConcatTable()
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model:add(unet(backend, ch, true))
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con:add(unet(backend, 64, false))
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con:add(nn.SpatialZeroPadding(-19, -19, -19, -19))
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model:add(con)
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model:add(nn.CAddTable())
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model:add(nn.LeakyReLU(0.1, true))
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model:add(SpatialConvolution(backend, 64, ch, 3, 3, 1, 1, 0, 0))
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model.w2nn_arch_name = "cunet_v2"
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model.w2nn_offset = 60
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model.w2nn_scale_factor = 2
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model.w2nn_channels = ch
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model.w2nn_resize = true
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-- 72, 128, 256 are valid
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--model.w2nn_input_size = 128
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return model
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end
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-- cascade u-net
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function srcnn.cunet_v3(backend, ch)
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function unet_branch(insert, backend, n_input, n_output, depad)
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local block = nn.Sequential()
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local pooling = SpatialConvolution(backend, n_input, n_input, 2, 2, 2, 2, 0, 0) -- downsampling
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--block:add(w2nn.Print())
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block:add(pooling)
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block:add(insert)
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block:add(SpatialFullConvolution(backend, n_output, n_output, 2, 2, 2, 2, 0, 0))-- upsampling
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local parallel = nn.ConcatTable(2)
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parallel:add(nn.SpatialZeroPadding(-depad, -depad, -depad, -depad))
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parallel:add(block)
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local model = nn.Sequential()
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model:add(parallel)
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model:add(nn.CAddTable())
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return model
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end
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function unet_conv(n_input, n_middle, n_output)
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local model = nn.Sequential()
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model:add(SpatialConvolution(backend, n_input, n_middle, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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model:add(SpatialConvolution(backend, n_middle, n_output, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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return model
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end
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function unet(backend, ch, deconv)
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local block1 = unet_conv(128, 256, 128)
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local block2 = nn.Sequential()
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block2:add(unet_conv(64, 64, 128))
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block2:add(unet_branch(block1, backend, 128, 128, 4))
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block2:add(unet_conv(128, 64, 64))
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local model = nn.Sequential()
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model:add(unet_conv(ch, 32, 64))
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model:add(unet_branch(block2, backend, 64, 64, 16))
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if deconv then
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model:add(SpatialConvolution(backend, 64, 128, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1))
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model:add(SpatialFullConvolution(backend, 128, 64, 4, 4, 2, 2, 3, 3))
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end
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return model
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end
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local model = nn.Sequential()
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local con = nn.ConcatTable()
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model:add(unet(backend, ch, true))
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model:add(nn.ConcatTable():add(unet(backend, 64, false)):add(nn.SpatialZeroPadding(-18, -18, -18, -18)))
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model:add(nn.CAddTable())
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model:add(SpatialConvolution(backend, 64, 64, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU())
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model:add(SpatialConvolution(backend, 64, ch, 3, 3, 1, 1, 0, 0))
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model:add(w2nn.InplaceClip01())
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model.w2nn_arch_name = "cunet_v3"
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model.w2nn_offset = 60
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model.w2nn_scale_factor = 2
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model.w2nn_channels = ch
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model.w2nn_resize = true
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-- 72, 128, 256 are valid
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--model.w2nn_input_size = 128
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return model
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end
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-- cascade u-net
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function srcnn.cunet_v4(backend, ch)
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function upconv_3(backend, n_input, n_output)
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local model = nn.Sequential()
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model:add(SpatialConvolution(backend, n_input, 32, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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model:add(SpatialConvolution(backend, 32, 32, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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model:add(SpatialFullConvolution(backend, 32, n_output, 4, 4, 2, 2, 3, 3):noBias())
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return model
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end
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function unet_branch(insert, backend, n_input, n_output, depad)
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local block = nn.Sequential()
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local pooling = SpatialConvolution(backend, n_input, n_input, 2, 2, 2, 2, 0, 0) -- downsampling
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--block:add(w2nn.Print())
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block:add(pooling)
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block:add(insert)
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block:add(SpatialFullConvolution(backend, n_output, n_output, 2, 2, 2, 2, 0, 0))-- upsampling
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local parallel = nn.ConcatTable(2)
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parallel:add(nn.SpatialZeroPadding(-depad, -depad, -depad, -depad))
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parallel:add(block)
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local model = nn.Sequential()
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model:add(parallel)
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model:add(nn.CAddTable())
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return model
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end
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function unet_conv(n_input, n_middle, n_output)
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local model = nn.Sequential()
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model:add(SpatialConvolution(backend, n_input, n_middle, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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model:add(SpatialConvolution(backend, n_middle, n_output, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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return model
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end
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function unet(backend, ch)
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local block1 = unet_conv(128, 256, 128)
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local block2 = nn.Sequential()
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block2:add(unet_conv(64, 64, 128))
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block2:add(unet_branch(block1, backend, 128, 128, 4))
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block2:add(unet_conv(128, 64, 64))
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local model = nn.Sequential()
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model:add(SpatialConvolution(backend, ch, 64, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1, true))
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model:add(unet_branch(block2, backend, 64, 64, 16))
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return model
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end
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local model = nn.Sequential()
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local con = nn.ConcatTable()
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local aux_con = nn.ConcatTable()
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model:add(upconv_3(backend, ch, 64))
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con:add(unet(backend, 32))
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--con:add(nn.SpatialZeroPadding(-20, -20, -20, -20))
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aux_con:add(nn.Sequential():add(nn.CAddTable()):add(w2nn.InplaceClip01())) -- cascaded unet output
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aux_con:add(nn.Sequential():add(nn.SelectTable(2)):add(w2nn.InplaceClip01())) -- single output
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model:add(conn)
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model:add(nn.CAddTable())
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model:add(SpatialConvolution(backend, 64, 64, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU())
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model:add(SpatialConvolution(backend, 64, ch, 3, 3, 1, 1, 0, 0))
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model:add(w2nn.InplaceClip01())
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model.w2nn_arch_name = "cunet_v3"
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model.w2nn_offset = 60
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model.w2nn_scale_factor = 2
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model.w2nn_channels = ch
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model.w2nn_resize = true
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-- 72, 128, 256 are valid
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--model.w2nn_input_size = 128
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return model
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end
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function srcnn.cunet_v6(backend, ch)
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-- cascaded residual channel attention unet
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function srcnn.upcunet(backend, ch)
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function unet_branch(insert, backend, n_input, n_output, depad)
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local block = nn.Sequential()
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local con = nn.ConcatTable(2)
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@ -1044,6 +771,7 @@ function srcnn.cunet_v6(backend, ch)
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local con = nn.ConcatTable()
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local aux_con = nn.ConcatTable()
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-- 2 cascade
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model:add(unet(backend, ch, true))
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con:add(unet(backend, ch, false))
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con:add(nn.SpatialZeroPadding(-20, -20, -20, -20))
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@ -1055,7 +783,7 @@ function srcnn.cunet_v6(backend, ch)
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model:add(aux_con)
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model:add(w2nn.AuxiliaryLossTable(1)) -- auxiliary loss for single unet output
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model.w2nn_arch_name = "cunet_v6"
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model.w2nn_arch_name = "upcunet"
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model.w2nn_offset = 60
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model.w2nn_scale_factor = 2
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model.w2nn_channels = ch
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