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models_c2f.lua
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models_c2f.lua
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require 'torch'
require 'nn'
require 'layers.cudnnSpatialConvolutionUpsample'
local models = {}
-- Create coarse to fine G network.
-- Color and grayscale currently get the same network, which is suboptimal.
-- @param dimensions Image dimensions as table of {count channels, height, width}
-- @param cuda Whether to start the network in CUDA/GPU mode (true).
-- @returns Sequential
function models.create_G(dimensions, cuda)
return models.create_G_d(dimensions, cuda)
end
function models.create_G_a(dimensions, cuda)
local nplanes = 64
local model_G = nn.Sequential()
model_G:add(nn.JoinTable(2, 2))
if cuda then
model_G:add(nn.Copy('torch.FloatTensor', 'torch.CudaTensor'))
end
local inner = nn.Sequential()
inner:add(cudnn.SpatialConvolutionUpsample(dimensions[1]+1, 64, 3, 3, 1))
inner:add(nn.PReLU())
inner:add(cudnn.SpatialConvolutionUpsample(64, 128, 7, 7, 1))
inner:add(nn.PReLU())
inner:add(cudnn.SpatialConvolutionUpsample(128, dimensions[1], 5, 5, 1))
inner:add(nn.View(dimensions[1], dimensions[2], dimensions[3]))
model_G:add(inner)
if cuda then
model_G:add(nn.Copy('torch.CudaTensor', 'torch.FloatTensor'))
end
model_G = require('weight-init')(model_G, 'heuristic')
if cuda then
model_G:get(3):cuda()
end
return model_G
end
function models.create_G_b(dimensions, cuda)
local nplanes = 64
local model_G = nn.Sequential()
model_G:add(nn.JoinTable(2, 2))
if cuda then
model_G:add(nn.Copy('torch.FloatTensor', 'torch.CudaTensor'))
end
local inner = nn.Sequential()
inner:add(cudnn.SpatialConvolutionUpsample(dimensions[1]+1, 64, 3, 3, 1))
inner:add(nn.PReLU())
inner:add(cudnn.SpatialConvolutionUpsample(64, 64, 3, 3, 1))
inner:add(nn.PReLU())
inner:add(cudnn.SpatialConvolutionUpsample(64, 256, 5, 5, 1))
inner:add(nn.PReLU())
inner:add(cudnn.SpatialConvolutionUpsample(256, dimensions[1], 7, 7, 1))
inner:add(nn.View(dimensions[1], dimensions[2], dimensions[3]))
model_G:add(inner)
if cuda then
model_G:add(nn.Copy('torch.CudaTensor', 'torch.FloatTensor'))
end
model_G = require('weight-init')(model_G, 'heuristic')
if cuda then
model_G:get(3):cuda()
end
return model_G
end
function models.create_G_c(dimensions, cuda)
local nplanes = 64
local model_G = nn.Sequential()
model_G:add(nn.JoinTable(2, 2))
if cuda then
model_G:add(nn.Copy('torch.FloatTensor', 'torch.CudaTensor'))
end
local inner = nn.Sequential()
inner:add(cudnn.SpatialConvolutionUpsample(dimensions[1]+1, 64, 3, 3, 1))
inner:add(nn.PReLU())
inner:add(cudnn.SpatialConvolutionUpsample(64, 128, 3, 3, 1))
inner:add(nn.PReLU())
inner:add(cudnn.SpatialConvolutionUpsample(128, 256, 5, 5, 1))
inner:add(nn.PReLU())
inner:add(cudnn.SpatialConvolutionUpsample(256, dimensions[1], 7, 7, 1))
inner:add(nn.View(dimensions[1], dimensions[2], dimensions[3]))
model_G:add(inner)
if cuda then
model_G:add(nn.Copy('torch.CudaTensor', 'torch.FloatTensor'))
end
model_G = require('weight-init')(model_G, 'heuristic')
if cuda then
model_G:get(3):cuda()
end
return model_G
end
function models.create_G_d(dimensions, cuda)
local model_G = nn.Sequential()
model_G:add(nn.JoinTable(2, 2))
if cuda then
model_G:add(nn.Copy('torch.FloatTensor', 'torch.CudaTensor'))
end
local inner = nn.Sequential()
inner:add(cudnn.SpatialConvolutionUpsample(dimensions[1]+1, 64, 3, 3, 1))
inner:add(nn.PReLU())
inner:add(cudnn.SpatialConvolutionUpsample(64, 64, 3, 3, 1))
inner:add(nn.PReLU())
inner:add(cudnn.SpatialConvolutionUpsample(64, 128, 5, 5, 1))
inner:add(nn.PReLU())
inner:add(cudnn.SpatialConvolutionUpsample(128, 256, 5, 5, 1))
inner:add(nn.PReLU())
inner:add(cudnn.SpatialConvolutionUpsample(256, dimensions[1], 7, 7, 1))
inner:add(nn.View(dimensions[1], dimensions[2], dimensions[3]))
model_G:add(inner)
if cuda then
model_G:add(nn.Copy('torch.CudaTensor', 'torch.FloatTensor'))
end
model_G = require('weight-init')(model_G, 'heuristic')
if cuda then
model_G:get(3):cuda()
end
return model_G
end
-- Create coarse to fine D network.
-- Color and grayscale currently get the same network, which is suboptimal.
-- @param dimensions Image dimensions as table of {count channels, height, width}
-- @param cuda Whether to start the network in CUDA/GPU mode (true).
-- @returns Sequential
function models.create_D(dimensions, cuda)
return models.create_D_c(dimensions, cuda)
end
function models.create_D_a(dimensions, cuda)
local model_D = nn.Sequential()
model_D:add(nn.CAddTable())
if cuda then
model_D:add(nn.Copy('torch.FloatTensor', 'torch.CudaTensor'))
end
local inner = nn.Sequential()
inner:add(nn.SpatialConvolution(dimensions[1], 64, 3, 3, 1, 1, (3-1)/2))
inner:add(nn.PReLU())
inner:add(nn.SpatialConvolution(64, 64, 3, 3, 1, 1, (3-1)/2))
inner:add(nn.PReLU())
inner:add(nn.SpatialMaxPooling(2, 2))
inner:add(nn.Dropout())
inner:add(nn.View(64 * 0.25 * dimensions[2] * dimensions[3]))
inner:add(nn.Linear(64 * 0.25 * dimensions[2] * dimensions[3], 512))
inner:add(nn.PReLU())
inner:add(nn.Dropout())
inner:add(nn.Linear(512, 1))
inner:add(nn.Sigmoid())
model_D:add(inner)
if cuda then
model_D:add(nn.Copy('torch.CudaTensor', 'torch.FloatTensor'))
end
model_D = require('weight-init')(model_D, 'heuristic')
if cuda then
model_D:get(3):cuda()
end
return model_D
end
function models.create_D_b(dimensions, cuda)
local model_D = nn.Sequential()
model_D:add(nn.CAddTable())
if cuda then
model_D:add(nn.Copy('torch.FloatTensor', 'torch.CudaTensor'))
end
local inner = nn.Sequential()
inner:add(nn.SpatialConvolution(dimensions[1], 64, 3, 3, 1, 1, (3-1)/2))
inner:add(nn.PReLU())
inner:add(nn.SpatialConvolution(64, 64, 3, 3, 1, 1, (3-1)/2))
inner:add(nn.PReLU())
inner:add(nn.SpatialMaxPooling(2, 2))
inner:add(nn.SpatialConvolution(64, 128, 3, 3, 1, 1, (3-1)/2))
inner:add(nn.PReLU())
inner:add(nn.SpatialConvolution(128, 128, 3, 3, 1, 1, (3-1)/2))
inner:add(nn.PReLU())
inner:add(nn.SpatialMaxPooling(2, 2))
inner:add(nn.Dropout())
inner:add(nn.View(128 * 0.25 * 0.25 * dimensions[2] * dimensions[3]))
inner:add(nn.Linear(128 * 0.25 * 0.25 * dimensions[2] * dimensions[3], 512))
inner:add(nn.PReLU())
inner:add(nn.Dropout())
inner:add(nn.Linear(512, 1))
inner:add(nn.Sigmoid())
model_D:add(inner)
if cuda then
model_D:add(nn.Copy('torch.CudaTensor', 'torch.FloatTensor'))
end
model_D = require('weight-init')(model_D, 'heuristic')
if cuda then
model_D:get(3):cuda()
end
return model_D
end
function models.create_D_c(dimensions, cuda)
local model_D = nn.Sequential()
model_D:add(nn.CAddTable())
if cuda then
model_D:add(nn.Copy('torch.FloatTensor', 'torch.CudaTensor'))
end
local inner = nn.Sequential()
inner:add(nn.SpatialConvolution(dimensions[1], 64, 3, 3, 1, 1, (3-1)/2))
inner:add(nn.PReLU())
inner:add(nn.SpatialConvolution(64, 64, 3, 3, 1, 1, (3-1)/2))
inner:add(nn.PReLU())
inner:add(nn.SpatialMaxPooling(2, 2))
inner:add(nn.SpatialConvolution(64, 128, 3, 3, 1, 1, (3-1)/2))
inner:add(nn.PReLU())
inner:add(nn.SpatialConvolution(128, 256, 3, 3, 1, 1, (3-1)/2))
inner:add(nn.PReLU())
inner:add(nn.SpatialMaxPooling(2, 2))
inner:add(nn.Dropout())
inner:add(nn.View(256 * 0.25 * 0.25 * dimensions[2] * dimensions[3]))
inner:add(nn.Linear(256 * 0.25 * 0.25 * dimensions[2] * dimensions[3], 512))
inner:add(nn.PReLU())
inner:add(nn.Dropout())
inner:add(nn.Linear(512, 1))
inner:add(nn.Sigmoid())
model_D:add(inner)
if cuda then
model_D:add(nn.Copy('torch.CudaTensor', 'torch.FloatTensor'))
end
model_D = require('weight-init')(model_D, 'heuristic')
if cuda then
model_D:get(3):cuda()
end
return model_D
end
return models