Add upcunet_v2, fixed the Global AVE problem
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nagadomi
parent
1f18d1919a
commit
0b82668359
175
lib/srcnn.lua
175
lib/srcnn.lua
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@ -794,73 +794,104 @@ function srcnn.upcunet(backend, ch)
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return model
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end
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function srcnn.prog_net(backend, ch)
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function base_upscaler(backend, ch)
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-- cascaded residual spatial channel attention unet
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function srcnn.upcunet_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 con = nn.ConcatTable(2)
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local model = nn.Sequential()
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model:add(nn.SpatialZeroPadding(-11, -11, -11, -11))
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model:add(SpatialConvolution(backend, ch, 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, 64, 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, 64, ch, 4, 4, 2, 2, 3, 3):noBias())
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model:add(w2nn.InplaceClip01())
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block:add(SpatialConvolution(backend, n_input, n_input, 2, 2, 2, 2, 0, 0))-- downsampling
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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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con:add(nn.SpatialZeroPadding(-depad, -depad, -depad, -depad))
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con:add(block)
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model:add(con)
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model:add(nn.CAddTable())
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return model
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end
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function block(backend, input, output)
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local con = nn.ConcatTable()
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local conv = nn.Sequential()
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local dil = nn.Sequential()
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local b = nn.Sequential()
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conv:add(SpatialConvolution(backend, input, output, 3, 3, 1, 1, 0, 0))
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conv:add(nn.SpatialZeroPadding(-5, -5, -5, -5))
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dil:add(SpatialDilatedConvolution(backend, input, output, 3, 3, 1, 1, 0, 0, 2, 2))
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dil:add(nn.LeakyReLU(0.1, true))
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dil:add(SpatialDilatedConvolution(backend, output, output, 3, 3, 1, 1, 0, 0, 4, 4))
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con:add(conv)
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con:add(dil)
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b:add(con)
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b:add(nn.CAddTable())
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b:add(nn.LeakyReLU(0.1, true))
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return b
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end
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function texture_upscaler(backend, ch)
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function unet_conv(n_input, n_middle, n_output, se)
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local model = nn.Sequential()
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model:add(w2nn.EdgeFilter(ch))
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model:add(SpatialConvolution(backend, ch * 8, 32, 1, 1, 1, 1, 0, 0))
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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(block(backend, 32, 128))
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model:add(block(backend, 128, 256))
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model:add(SpatialFullConvolution(backend, 256, ch, 4, 4, 2, 2, 3, 3):noBias())
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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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if se then
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-- Spatial Squeeze and Excitation Networks
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local se_fac = 4
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local con = nn.ConcatTable(2)
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local attention = nn.Sequential()
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attention:add(nn.SpatialAveragePooling(4, 4, 4, 4))
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attention:add(SpatialConvolution(backend, n_output, math.floor(n_output / se_fac), 1, 1, 1, 1, 0, 0))
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attention:add(nn.ReLU(true))
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attention:add(SpatialConvolution(backend, math.floor(n_output / se_fac), n_output, 1, 1, 1, 1, 0, 0))
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attention:add(nn.Sigmoid(true))
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attention:add(nn.SpatialUpSamplingNearest(4, 4))
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con:add(nn.Identity())
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con:add(attention)
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model:add(con)
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model:add(nn.CMulTable())
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end
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return model
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end
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-- Residual U-Net
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function unet(backend, in_ch, out_ch, deconv)
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local block1 = unet_conv(128, 256, 128, true)
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local block2 = nn.Sequential()
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block2:add(unet_conv(64, 64, 128, true))
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block2:add(unet_branch(block1, backend, 128, 128, 4))
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block2:add(unet_conv(128, 64, 64, true))
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local model = nn.Sequential()
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model:add(unet_conv(in_ch, 32, 64, false))
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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(SpatialFullConvolution(backend, 64, out_ch, 4, 4, 2, 2, 3, 3))
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else
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model:add(SpatialConvolution(backend, 64, out_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 = nn.ConcatTable()
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local aux_con = nn.ConcatTable()
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con:add(base_upscaler(backend, ch))
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con:add(texture_upscaler(backend, ch))
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-- 2 cascade
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model:add(unet(backend, ch, ch, true))
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con:add(nn.Sequential():add(unet(backend, ch, ch, false)):add(nn.SpatialZeroPadding(-1, -1, -1, -1))) -- -1 for odd output size
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con:add(nn.SpatialZeroPadding(-20, -20, -20, -20))
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aux:add(nn.Sequential():add(nn.CAddTable()):add(w2nn.InplaceClip01()):add(nn.View(-1):setNumInputDims(3)))
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aux:add(nn.Sequential():add(nn.SelectTable(1)):add(nn.View(-1):setNumInputDims(3)))
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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)
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model:add(w2nn.AuxiliaryLossTable(1))
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model.w2nn_arch_name = "prog_net"
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model.w2nn_offset = 28
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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_v2"
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model.w2nn_offset = 58
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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 = {76,92,108,140,156,172,188,204,220,236,252,268,284,300,316,332,348,364,380,396,412,428,444,460,476,492,508}
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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 = false
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local model = nil
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local arch = {"upconv_7", "upcunet", "upcunet_v2"}
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local backend = "cunn"
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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:training()
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t = sys.clock()
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for i = 1, 10 do
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model:forward(torch.Tensor(1, 3, 172, 172):zero():cuda())
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end
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print(arch[k], sys.clock() - t)
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end
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end
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function srcnn.create(model_name, backend, color)
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model_name = model_name or "vgg_7"
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backend = backend or "cunn"
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@ -881,59 +912,15 @@ function srcnn.create(model_name, backend, color)
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error("unsupported model_name: " .. model_name)
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end
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end
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--[[
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local model = srcnn.fcn_v1("cunn", 3):cuda()
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print(model:forward(torch.Tensor(1, 3, 108, 108):zero():cuda()):size())
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print(model)
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local model = srcnn.unet_refine("cunn", 3):cuda()
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print(model)
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print(model:forward(torch.Tensor(1, 3, 64, 64):zero():cuda()):size())
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local model = srcnn.cupconv_14("cunn", 3):cuda()
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print(model)
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print(model:forward(torch.Tensor(1, 3, 64, 64):zero():cuda()):size())
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os.exit()
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local model = srcnn.cupconv_14("cunn", 3):cuda()
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print(model)
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print(model:forward(torch.Tensor(1, 3, 64, 64):zero():cuda()):size())
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os.exit()
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local model = srcnn.upconv_refine("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, 64, 64):zero():cuda()))
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os.exit()
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local model = srcnn.nw2("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, 64, 64):zero():cuda()))
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os.exit()
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local model = srcnn.prog_net("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, 128, 128):zero():cuda()))
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os.exit()
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local model = srcnn.double_unet("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()))
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os.exit()
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local model = srcnn.cunet_v3("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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os.exit()
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local model = srcnn.cunet_v6("cunn", 3):cuda()
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local model = srcnn.upcunet_v2("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()))
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print(model:forward(torch.Tensor(1, 3, 76, 76):zero():cuda()))
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os.exit()
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--]]
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return srcnn
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