CIFAR-10 eyescream

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-- CIFAR 8x8 opt.scale = 8 opt.geometry = {3, opt.scale, opt.scale} local input_sz = opt.geometry[1] * opt.geometry[2] * opt.geometry[3] local numhid = 600 model_D = nn.Sequential() model_D:add(nn.Reshape(input_sz)) model_D:add(nn.Linear(input_sz, numhid)) model_D:add(nn.ReLU()) model_D:add(nn.Dropout()) model_D:add(nn.Linear(numhid, numhid)) model_D:add(nn.ReLU()) model_D:add(nn.Dropout()) model_D:add(nn.Linear(numhid,1)) model_D:add(nn.Sigmoid())

local numhid = 1200 model_G = nn.Sequential() model_G:add(nn.Linear(opt.noiseDim, numhid)) model_G:add(nn.ReLU()) model_G:add(nn.Linear(numhid, numhid)) model_G:add(nn.Sigmoid()) model_G:add(nn.Linear(numhid, input_sz)) model_G:add(nn.Reshape(opt.geometry[1], opt.geometry[2], opt.geometry[3]))

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-- CIFAR 8->14 opt.coarseSize = 8 opt.fineSize = 14 opt.geometry = {3, opt.fineSize, opt.fineSize} local input_sz = opt.geometry[1] * opt.geometry[2] * opt.geometry[3] local nplanes = 64 model_D = nn.Sequential() model_D:add(nn.CAddTable()) model_D:add(nn.SpatialConvolution(3, nplanes, 5, 5)) model_D:add(nn.ReLU()) model_D:add(nn.SpatialConvolution(nplanes, nplanes, 5, 5, 2, 2)) local sz =math.floor( ( (opt.fineSize - 5 + 1) - 5) / 2 + 1) model_D:add(nn.Reshape(nplanesszsz)) model_D:add(nn.ReLU()) model_D:add(nn.Dropout()) model_D:add(nn.Linear(nplanesszsz, 1)) model_D:add(nn.Sigmoid())

local nplanes = 64 model_G = nn.Sequential() model_G:add(nn.JoinTable(2, 2)) model_G:add(nn.SpatialConvolutionUpsample(3+1, nplanes, 5, 5, 1)) -- 3 color channels + conditional model_G:add(nn.ReLU()) model_G:add(nn.SpatialConvolutionUpsample(nplanes, nplanes, 5, 5, 1)) model_G:add(nn.ReLU()) model_G:add(nn.SpatialConvolutionUpsample(nplanes, 3, 5, 5, 1)) model_G:add(nn.View(opt.geometry[1], opt.geometry[2], opt.geometry[3]))

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-- CIFAR 14->28 opt.coarseSize = 14 opt.fineSize = 28 opt.geometry = {3, opt.fineSize, opt.fineSize} local input_sz = opt.geometry[1] * opt.geometry[2] * opt.geometry[3] local nplanes = 128 model_D = nn.Sequential() model_D:add(nn.CAddTable()) model_D:add(nn.SpatialConvolution(3, nplanes, 5, 5)) model_D:add(nn.ReLU()) model_D:add(nn.SpatialConvolution(nplanes, nplanes, 5, 5, 2, 2)) local sz =math.floor( ( (opt.fineSize - 5 + 1) - 5) / 2 + 1) model_D:add(nn.Reshape(nplanesszsz)) model_D:add(nn.ReLU()) model_D:add(nn.Dropout()) model_D:add(nn.Linear(nplanesszsz, 1)) model_D:add(nn.Sigmoid())

local nplanes = 128 model_G = nn.Sequential() model_G:add(nn.JoinTable(2, 2)) model_G:add(nn.SpatialConvolutionUpsample(3+1, nplanes, 7, 7, 1)) -- 3 color channels + conditional model_G:add(nn.ReLU()) model_G:add(nn.SpatialConvolutionUpsample(nplanes, nplanes, 7, 7, 1)) model_G:add(nn.ReLU()) model_G:add(nn.SpatialConvolutionUpsample(nplanes, 3, 5, 5, 1)) model_G:add(nn.View(opt.geometry[1], opt.geometry[2], opt.geometry[3]))