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(nplanes*sz*sz)) | |
model_D:add(nn.ReLU()) | |
model_D:add(nn.Dropout()) | |
model_D:add(nn.Linear(nplanes*sz*sz, 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])) | |
---------------------------------------------------------------------- | |
-- 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(nplanes*sz*sz)) | |
model_D:add(nn.ReLU()) | |
model_D:add(nn.Dropout()) | |
model_D:add(nn.Linear(nplanes*sz*sz, 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])) |
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