Add cunet_s
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3e4058821e
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110
lib/srcnn.lua
110
lib/srcnn.lua
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@ -573,24 +573,122 @@ function srcnn.cunet(backend, ch)
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return model
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end
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-- small version of cunet
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function srcnn.upcunet_s(backend, ch)
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-- Residual U-Net
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local function unet(backend, ch, deconv)
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local block1 = unet_conv(backend, 128, 256, 128, true)
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local block2 = nn.Sequential()
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block2:add(unet_conv(backend, 32, 64, 128, true))
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block2:add(unet_branch(backend, block1, backend, 128, 128, 4))
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block2:add(unet_conv(backend, 128, 64, 32, true))
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local model = nn.Sequential()
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model:add(unet_conv(backend, ch, 32, 32, true))
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model:add(unet_branch(backend, block2, backend, 32, 32, 16))
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model:add(SpatialConvolution(backend, 32, 64, 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, 64, ch, 4, 4, 2, 2, 3, 3))
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else
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model:add(SpatialConvolution(backend, 64, 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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-- 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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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 = "upcunet_s"
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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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model.w2nn_valid_input_size = {}
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for i = 76, 512, 4 do
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table.insert(model.w2nn_valid_input_size, i)
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end
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return model
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end
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function srcnn.cunet_s(backend, ch)
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local function unet(backend, ch)
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local block1 = unet_conv(backend, 128, 256, 128, true)
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local block2 = nn.Sequential()
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block2:add(unet_conv(backend, 32, 64, 128, true))
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block2:add(unet_branch(backend, block1, backend, 128, 128, 4))
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block2:add(unet_conv(backend, 128, 64, 32, true))
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local model = nn.Sequential()
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model:add(unet_conv(backend, ch, 32, 32, true))
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model:add(unet_branch(backend, block2, backend, 32, 32, 16))
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model:add(SpatialConvolution(backend, 32, 64, 3, 3, 1, 1, 0, 0))
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model:add(nn.LeakyReLU(0.1))
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model:add(SpatialConvolution(backend, 64, ch, 3, 3, 1, 1, 0, 0))
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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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-- 2 cascade
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model:add(unet(backend, ch))
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con:add(unet(backend, ch))
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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_s"
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model.w2nn_offset = 40
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model.w2nn_scale_factor = 1
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model.w2nn_channels = ch
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model.w2nn_resize = false
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model.w2nn_valid_input_size = {}
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for i = 100, 512, 4 do
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table.insert(model.w2nn_valid_input_size, i)
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end
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return model
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end
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local function bench()
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local sys = require 'sys'
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cudnn.benchmark = true
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local model = nil
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local arch = {"upconv_7", "upcunet","vgg_7", "cunet"}
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local arch = {"upconv_7", "upcunet","upcunet_s", "vgg_7", "cunet", "cunet_s"}
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local backend = "cudnn"
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local ch = 3
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local batch_size = 1
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local crop_size = 512
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for k = 1, #arch do
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model = srcnn[arch[k]](backend, 3):cuda()
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model = srcnn[arch[k]](backend, ch):cuda()
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model:evaluate()
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local dummy = nil
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-- warn
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for i = 1, 20 do
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local x = torch.Tensor(4, 3, 172, 172):uniform():cuda()
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local x = torch.Tensor(batch_size, ch, crop_size, crop_size):uniform():cuda()
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model:forward(x)
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end
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t = sys.clock()
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for i = 1, 20 do
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local x = torch.Tensor(4, 3, 172, 172):uniform():cuda()
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local x = torch.Tensor(batch_size, ch, crop_size, crop_size):uniform():cuda()
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local z = model:forward(x)
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if dummy == nil then
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dummy = z:clone()
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@ -623,10 +721,10 @@ function srcnn.create(model_name, backend, color)
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end
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end
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--[[
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local model = srcnn.cunet_v3("cunn", 3):cuda()
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local model = srcnn.upcunet_s("cunn", 3):cuda()
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print(model)
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model:training()
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print(model:forward(torch.Tensor(1, 3, 144, 144):zero():cuda()):size())
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print(model:forward(torch.Tensor(1, 3, 144, 144):zero():cuda()))
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bench()
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os.exit()
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--]]
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