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neural_gram.lua
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neural_gram.lua
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require 'torch'
require 'nn'
require 'image'
require 'optim'
require 'loadcaffe'
require 'libcuda_utils'
require 'cutorch'
require 'cunn'
local cmd = torch.CmdLine()
-- Basic options
cmd:option('-style_image', 'examples/inputs/seated-nude.jpg', 'Style target image')
cmd:option('-content_image', 'examples/inputs/tubingen.jpg', 'Content target image')
cmd:option('-tmask_image', 'examples/inputs/t_mask.jpg', 'Content tight mask image')
cmd:option('-mask_image', 'examples/inputs/t_mask.jpg', 'Content loose mask image')
cmd:option('-image_size', 700, 'Maximum height / width of generated image')
cmd:option('-gpu', 0, 'Zero-indexed ID of the GPU to use; for CPU mode set -gpu = -1')
-- Optimization options
cmd:option('-content_weight', 5)
cmd:option('-style_weight', 100)
cmd:option('-tv_weight', 1e-3)
cmd:option('-num_iterations', 1000)
cmd:option('-normalize_gradients', false)
cmd:option('-init', 'image', 'random|image')
cmd:option('-optimizer', 'lbfgs', 'lbfgs|adam')
cmd:option('-learning_rate', 1e1)
-- Output options
cmd:option('-print_iter', 50)
cmd:option('-save_iter', 100)
cmd:option('-output_image', 'out.png')
-- Other options
cmd:option('-style_scale', 1.0)
cmd:option('-original_colors', 0)
cmd:option('-pooling', 'max', 'max|avg')
cmd:option('-proto_file', 'models/VGG_ILSVRC_19_layers_deploy.prototxt')
cmd:option('-model_file', 'models/VGG_ILSVRC_19_layers.caffemodel')
cmd:option('-backend', 'nn', 'nn|cudnn|clnn')
cmd:option('-cudnn_autotune', false)
cmd:option('-seed', 316)
cmd:option('-content_layers', 'relu4_1', 'layers for content')
cmd:option('-style_layers', 'relu3_1,relu4_1,relu5_1', 'layers for style')
-- Patchmatch
cmd:option('-patchmatch_size', 3)
local function main(params)
cutorch.setDevice(params.gpu + 1)
cutorch.setHeapTracking(true)
torch.manualSeed(params.seed)
idx = cutorch.getDevice()
print('gpu, idx = ', params.gpu, idx)
local content_image = image.load(params.content_image, 3)
content_image = image.scale(content_image, params.image_size, 'bilinear')
local c, h, w = content_image:size(1), content_image:size(2), content_image:size(3)
local content_image_caffe = preprocess(content_image):float():cuda()
local content_layers = params.content_layers:split(",")
local style_image = image.load(params.style_image, 3)
style_image = image.scale(style_image, w, h, 'bilinear')
local style_image_caffe = preprocess(style_image):float():cuda()
local style_layers = params.style_layers:split(",")
-- Loose mask
local mask_image = image.load(params.mask_image, 3)[1]
mask_image = image.scale(mask_image, params.image_size, 'bilinear'):float()
local mask_image_ori = mask_image:clone()
-- Tight mask
local tmask_image = image.load(params.tmask_image, 3)
tmask_image = image.scale(tmask_image, params.image_size, 'bilinear'):float()
local tmask_image_ori = tmask_image:clone()
local tr = 3;
local tkernel = image.gaussian(2*tr+1, tr, 1, true)
tmask_image = image.convolve(tmask_image, tkernel, 'same')
-- Set up the network, inserting style and content loss modules
local content_losses, style_losses = {}, {}
local next_content_idx, next_style_idx = 1, 1
local net = nn.Sequential()
if params.tv_weight > 0 then
local tv_mod = nn.TVLoss(params.tv_weight):float():cuda()
net:add(tv_mod)
end
-- load VGG-19 network
local cnn = loadcaffe.load(params.proto_file, params.model_file, params.backend):float():cuda()
for i = 1, cnn:size() do
if next_content_idx <= #content_layers or next_style_idx <= #style_layers then
local layer = cnn:get(i)
local name = layer.name
local layer_type = torch.type(layer)
local is_pooling = (layer_type == 'cudnn.SpatialMaxPooling' or layer_type == 'nn.SpatialMaxPooling')
local is_conv = (layer_type == 'nn.SpatialConvolution' or layer_type == 'cudnn.SpatialConvolution')
net:add(layer)
if is_pooling then
mask_image = image.scale(mask_image, math.ceil(mask_image:size(2)/2), math.ceil(mask_image:size(1)/2))
elseif is_conv then
local sap = nn.SpatialAveragePooling(3,3,1,1,1,1):float()
mask_image = sap:forward(mask_image:repeatTensor(1,1,1))[1]:clone()
end
if name == content_layers[next_content_idx] then
print("Setting up content layer", i, ":", layer.name)
local input = net:forward(content_image_caffe):clone()
local norm = params.normalize_gradients
local loss_module = nn.ContentLoss(params.content_weight, input, norm, mask_image):float():cuda()
net:add(loss_module)
table.insert(content_losses, loss_module)
next_content_idx = next_content_idx + 1
end
if name == style_layers[next_style_idx] then
print("Setting up style layer ", i, ":", layer.name)
local gram = GramMatrix():float():cuda()
local input = net:forward(content_image_caffe):clone()
local target = net:forward(style_image_caffe):clone()
local mask = mask_image:clone():repeatTensor(1,1,1):expandAs(target):cuda()
local match, correspondence =
cuda_utils.patchmatch_r(input, target, params.patchmatch_size, 1)
match:cmul(mask)
local target_gram = gram:forward(match):clone()
target_gram:div(mask:sum())
local norm = params.normalize_gradients
local loss_module = nn.StyleLoss(params.style_weight, target_gram, norm, mask_image):float():cuda()
net:add(loss_module)
table.insert(style_losses, loss_module)
next_style_idx = next_style_idx + 1
end
end
end
-- We don't need the base CNN anymore, so clean it up to save memory.
cnn = nil
for i=1,#net.modules do
local module = net.modules[i]
if torch.type(module) == 'nn.SpatialConvolutionMM' then
-- remove these, not used, but uses gpu memory
module.gradWeight = nil
module.gradBias = nil
end
end
collectgarbage()
-- Initialize the image
if params.seed >= 0 then
torch.manualSeed(params.seed)
end
local img = nil
if params.init == 'random' then
img = torch.randn(content_image:size()):float():cuda():mul(0.001)
elseif params.init == 'image' then
img = content_image_caffe:clone():float():cuda()
else
error('Invalid init type')
end
-- Run it through the network once to get the proper size for the gradient
-- All the gradients will come from the extra loss modules, so we just pass
-- zeros into the top of the net on the backward pass.
local y = net:forward(img)
local dy = img.new(#y):zero()
-- Declaring this here lets us access it in maybe_print
local optim_state = nil
if params.optimizer == 'lbfgs' then
optim_state = {
maxIter = params.num_iterations,
verbose=true,
}
elseif params.optimizer == 'adam' then
optim_state = {
learningRate = params.learning_rate,
}
else
error(string.format('Unrecognized optimizer "%s"', params.optimizer))
end
local function maybe_print(t, loss)
local verbose = (params.print_iter > 0 and t % params.print_iter == 0)
if verbose then
print(string.format('Iteration %d / %d', t, params.num_iterations))
for i, loss_module in ipairs(content_losses) do
print(string.format(' Content %d loss: %f', i, loss_module.loss))
end
for i, loss_module in ipairs(style_losses) do
print(string.format(' Style %d loss: %f', i, loss_module.loss))
end
print(string.format(' Total loss: %f', loss))
end
end
local function maybe_save(t)
local should_save = params.save_iter > 0 and t % params.save_iter == 0
should_save = should_save or t == params.num_iterations
if should_save then
-- local disp = deprocess(img:double())
local disp = torch.cmul(img:double(), tmask_image:double())
disp:add(torch.cmul(style_image_caffe:double(), 1.0 - tmask_image:double()))
disp = deprocess(disp)
disp = image.minmax{tensor=disp, min=0, max=1}
local filename = build_filename(params.output_image, t)
if t == params.num_iterations then
filename = params.output_image
end
-- Maybe perform postprocessing for color-independent style transfer
if params.original_colors == 1 then
disp = original_colors(content_image, disp)
end
image.save(filename, disp)
end
end
-- Function to evaluate loss and gradient. We run the net forward and
-- backward to get the gradient, and sum up losses from the loss modules.
-- optim.lbfgs internally handles iteration and calls this fucntion many
-- times, so we manually count the number of iterations to handle printing
-- and saving intermediate results.
local num_calls = 0
local function feval(x)
num_calls = num_calls + 1
net:forward(x)
local grad = net:updateGradInput(x, dy)
local msk = mask_image_ori:clone()
msk = msk:repeatTensor(1,1,1):expandAs(x):cuda()
grad:cmul(msk)
local loss = 0
for _, mod in ipairs(content_losses) do
loss = loss + mod.loss
end
for _, mod in ipairs(style_losses) do
loss = loss + mod.loss
end
maybe_print(num_calls, loss)
maybe_save(num_calls)
collectgarbage()
-- optim.lbfgs expects a vector for gradients
return loss, grad:view(grad:nElement())
end
-- Run optimization.
if params.optimizer == 'lbfgs' then
print('Running optimization with L-BFGS')
local x, losses = optim.lbfgs(feval, img, optim_state)
elseif params.optimizer == 'adam' then
print('Running optimization with ADAM')
for t = 1, params.num_iterations do
local x, losses = optim.adam(feval, img, optim_state)
end
end
end
function build_filename(output_image, iteration)
local ext = paths.extname(output_image)
local basename = paths.basename(output_image, ext)
local directory = paths.dirname(output_image)
return string.format('%s/%s_%d.%s',directory, basename, iteration, ext)
end
-- Preprocess an image before passing it to a Caffe model.
-- We need to rescale from [0, 1] to [0, 255], convert from RGB to BGR,
-- and subtract the mean pixel.
function preprocess(img)
local mean_pixel = torch.DoubleTensor({103.939, 116.779, 123.68})
local perm = torch.LongTensor{3, 2, 1}
img = img:index(1, perm):mul(256.0)
mean_pixel = mean_pixel:view(3, 1, 1):expandAs(img)
img:add(-1, mean_pixel)
return img
end
-- Undo the above preprocessing.
function deprocess(img)
local mean_pixel = torch.DoubleTensor({103.939, 116.779, 123.68})
mean_pixel = mean_pixel:view(3, 1, 1):expandAs(img)
img = img + mean_pixel
local perm = torch.LongTensor{3, 2, 1}
img = img:index(1, perm):div(256.0)
return img
end
-- Combine the Y channel of the generated image and the UV channels of the
-- content image to perform color-independent style transfer.
function original_colors(content, generated)
local generated_y = image.rgb2yuv(generated)[{{1, 1}}]
local content_uv = image.rgb2yuv(content)[{{2, 3}}]
return image.yuv2rgb(torch.cat(generated_y, content_uv, 1))
end
-- Define an nn Module to compute content loss in-place
local ContentLoss, parent = torch.class('nn.ContentLoss', 'nn.Module')
function ContentLoss:__init(strength, target, normalize, mask)
parent.__init(self)
self.strength = strength
self.target = target
self.normalize = normalize or false
self.loss = 0
self.crit = nn.MSECriterion()
self.mask = mask:clone()
end
function ContentLoss:updateOutput(input)
if input:nElement() == self.target:nElement() then
self.loss = self.crit:forward(input, self.target) * self.strength
else
print('WARNING: Skipping content loss')
end
self.output = input
return self.output
end
function ContentLoss:updateGradInput(input, gradOutput)
if input:nElement() == self.target:nElement() then
self.gradInput = self.crit:backward(input, self.target)
end
if self.normalize then
self.gradInput:div(torch.norm(self.gradInput, 1) + 1e-8)
end
self.gradInput:mul(self.strength)
self.gradInput:add(gradOutput)
local msk = self.mask:clone():repeatTensor(1,1,1):expandAs(input):cuda()
self.gradInput:cmul(msk)
return self.gradInput
end
-- Returns a network that computes the CxC Gram matrix from inputs
-- of size C x H x W
function GramMatrix()
local net = nn.Sequential()
net:add(nn.View(-1):setNumInputDims(2))
local concat = nn.ConcatTable()
concat:add(nn.Identity())
concat:add(nn.Identity())
net:add(concat)
net:add(nn.MM(false, true))
return net
end
-- Define an nn Module to compute style loss in-place
local StyleLoss, parent = torch.class('nn.StyleLoss', 'nn.Module')
function StyleLoss:__init(strength, target, normalize, mask)
parent.__init(self)
self.normalize = normalize or false
self.strength = strength
self.target = target
self.loss = 0
self.mask = mask:clone()
self.gram = GramMatrix()
self.G = nil
self.crit = nn.MSECriterion()
end
function StyleLoss:updateOutput(input)
local msk = self.mask:clone():repeatTensor(1,1,1):expandAs(input):cuda()
self.G = self.gram:forward(torch.cmul(input, msk))
-- self.G:div(input:nElement())
self.G:div(msk:sum())
self.loss = self.crit:forward(self.G, self.target)
self.loss = self.loss * self.strength
self.output = input
return self.output
end
function StyleLoss:updateGradInput(input, gradOutput)
local msk = self.mask:clone():repeatTensor(1,1,1):expandAs(input):cuda()
local dG = self.crit:backward(self.G, self.target)
-- dG:div(input:nElement())
dG:div(msk:sum())
self.gradInput = self.gram:backward(torch.cmul(input, msk), dG)
if self.normalize then
self.gradInput:div(torch.norm(self.gradInput, 1) + 1e-8)
end
self.gradInput:mul(self.strength)
self.gradInput:add(gradOutput)
self.gradInput:cmul(msk)
return self.gradInput
end
local TVLoss, parent = torch.class('nn.TVLoss', 'nn.Module')
function TVLoss:__init(strength)
parent.__init(self)
self.strength = strength
self.x_diff = torch.Tensor()
self.y_diff = torch.Tensor()
end
function TVLoss:updateOutput(input)
self.output = input
return self.output
end
-- TV loss backward pass inspired by kaishengtai/neuralart
function TVLoss:updateGradInput(input, gradOutput)
self.gradInput:resizeAs(input):zero()
local C, H, W = input:size(1), input:size(2), input:size(3)
self.x_diff:resize(3, H - 1, W - 1)
self.y_diff:resize(3, H - 1, W - 1)
self.x_diff:copy(input[{{}, {1, -2}, {1, -2}}])
self.x_diff:add(-1, input[{{}, {1, -2}, {2, -1}}])
self.y_diff:copy(input[{{}, {1, -2}, {1, -2}}])
self.y_diff:add(-1, input[{{}, {2, -1}, {1, -2}}])
self.gradInput[{{}, {1, -2}, {1, -2}}]:add(self.x_diff):add(self.y_diff)
self.gradInput[{{}, {1, -2}, {2, -1}}]:add(-1, self.x_diff)
self.gradInput[{{}, {2, -1}, {1, -2}}]:add(-1, self.y_diff)
self.gradInput:mul(self.strength)
self.gradInput:add(gradOutput)
return self.gradInput
end
local params = cmd:parse(arg)
main(params)