Working Multi-Region Spatial Control in Neural-Style. Also known as "masked style transfer", and "semantic/segmented style transfer".

neural_style_seg.lua

-- Original mask related code from: https://github.com/martinbenson/deep-photo-styletransfer
-- Modified mask code by github.com/ProGamerGov

require 'torch'
require 'nn'
require 'image'
require 'optim'

require 'loadcaffe'


local cmd = torch.CmdLine()

-- Basic options
cmd:option('-style_image', 'examples/inputs/seated-nude.jpg',
           'Style target image')
cmd:option('-style_seg', '',
           'Style segmentation image')
cmd:option('-style_blend_weights', 'nil')
cmd:option('-content_image', 'examples/inputs/tubingen.jpg',
           'Content target image')
cmd:option('-content_seg', '',
           'Style segmentation image')
cmd:option('-image_size', 512, 'Maximum height / width of generated image')
cmd:option('-gpu', '0', 'Zero-indexed ID of the GPU to use; for CPU mode set -gpu = -1')
cmd:option('-multigpu_strategy', '', 'Index of layers to split the network across GPUs')
cmd:option('-color_codes', 'blue,green,black,white,red,yellow,grey,lightblue,purple', 'Colors used in content mask')

-- Optimization options
cmd:option('-content_weight', 5e0)
cmd:option('-style_weight', 1e2)
cmd:option('-tv_weight', 1e-3)
cmd:option('-num_iterations', 1000)
cmd:option('-normalize_gradients', false)
cmd:option('-init', 'random', 'random|image')
cmd:option('-init_image', '')
cmd:option('-optimizer', 'lbfgs', 'lbfgs|adam')
cmd:option('-learning_rate', 1e1)
cmd:option('-lbfgs_num_correction', 0)

-- 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', -1)

cmd:option('-content_layers', 'relu4_2', 'layers for content')
cmd:option('-style_layers', 'relu1_1,relu2_1,relu3_1,relu4_1,relu5_1', 'layers for style')


local function main(params)
  local dtype, multigpu = setup_gpu(params)

  local loadcaffe_backend = params.backend
  if params.backend == 'clnn' then loadcaffe_backend = 'nn' end
  local cnn = loadcaffe.load(params.proto_file, params.model_file, loadcaffe_backend):type(dtype)

  local content_image = image.load(params.content_image, 3)
  content_image = image.scale(content_image, params.image_size, 'bilinear')
  local content_image_caffe = preprocess(content_image):float()

  local style_size = math.ceil(params.style_scale * params.image_size)
  local style_image_list = params.style_image:split(',')
  local style_images_caffe = {}
  for _, img_path in ipairs(style_image_list) do
    local img = image.load(img_path, 3)
    img = image.scale(img, style_size, 'bilinear')
    local img_caffe = preprocess(img):float()
    table.insert(style_images_caffe, img_caffe)
  end

  local init_image = nil
  if params.init_image ~= '' then
    init_image = image.load(params.init_image, 3)
    local H, W = content_image:size(2), content_image:size(3)
    init_image = image.scale(init_image, W, H, 'bilinear')
    init_image = preprocess(init_image):float()
  end

  -- Handle style blending weights for multiple style inputs
  local style_blend_weights = nil
  if params.style_blend_weights == 'nil' then
    -- Style blending not specified, so use equal weighting
    style_blend_weights = {}
    for i = 1, #style_image_list do
      table.insert(style_blend_weights, 1.0)
    end
  else
    style_blend_weights = params.style_blend_weights:split(',')
    assert(#style_blend_weights == #style_image_list,
      '-style_blend_weights and -style_images must have the same number of elements')
  end
  -- Normalize the style blending weights so they sum to 1
  local style_blend_sum = 0
  for i = 1, #style_blend_weights do
    style_blend_weights[i] = tonumber(style_blend_weights[i])
    style_blend_sum = style_blend_sum + style_blend_weights[i]
  end
  for i = 1, #style_blend_weights do
    style_blend_weights[i] = style_blend_weights[i] / style_blend_sum
  end

  local content_layers = params.content_layers:split(",")
  local style_layers = params.style_layers:split(",")


  -- segmentation images
  local style_seg_images_caffe = {}
  local color_content_masks, color_style_masks = {}, {}
  local color_codes = params.color_codes:split(",")

  if params.content_seg and params.style_seg ~= '' then
    local content_seg = image.load(params.content_seg, 3)
    content_seg = image.scale(content_seg, params.image_size, 'bilinear')
    local content_seg_caffe = content_seg:float()
    local style_segs = params.style_seg:split(',')
    assert(#style_segs == #style_image_list,
        '-style_seg and -style_image must have the same number of elements')
    for i, img_path in ipairs(style_segs) do
      local style_seg = image.load(img_path, 3)
      style_seg = image.scale(style_seg, style_size, 'bilinear')
      local style_seg_caffe = style_seg:float()
      table.insert(style_seg_images_caffe, style_seg_caffe)
    end
    --local color_content_masks, color_style_masks = {}, {}
    for j = 1, #color_codes do
      local content_mask_j = ExtractMask(content_seg_caffe, color_codes[j], dtype)
      table.insert(color_content_masks, content_mask_j)
    end
    for i=1, #style_image_list do
      tmp_table = {}
      for j = 1, #color_codes do
        local style_mask_i_j = ExtractMask(style_seg_images_caffe[i], color_codes[j], dtype)
        table.insert(tmp_table, style_mask_i_j)
      end
      table.insert(color_style_masks, tmp_table)
    end
  end
           
  -- 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):type(dtype)
    net:add(tv_mod)
  end
  for i = 1, #cnn 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')
      if params.content_seg and params.style_seg ~= '' then
        if is_pooling then
          local pool_layer
          if params.pooling == 'avg' then
            assert(layer.padW == 0 and layer.padH == 0)
            local kW, kH = layer.kW, layer.kH
            local dW, dH = layer.dW, layer.dH
            local avg_pool_layer = nn.SpatialAveragePooling(kW, kH, dW, dH):type(dtype)
            local msg = 'Replacing max pooling at layer %d with average pooling'
            print(string.format(msg, i))
            pool_layer=avg_pool_layer
          else
            pool_layer=layer
          end
          net:add(pool_layer)
          for k = 1, #color_codes do
            color_content_masks[k] = image.scale(color_content_masks[k]:float(), math.ceil(color_content_masks[k]:size(2)/2), math.ceil(color_content_masks[k]:size(1)/2)):type(dtype)
          end
          for j = 1, #style_image_list do
            for k = 1, #color_codes do
              color_style_masks[j][k]   = image.scale(color_style_masks[j][k]:float(),   math.ceil(color_style_masks[j][k]:size(2)/2),   math.ceil(color_style_masks[j][k]:size(1)/2)):type(dtype)
            end
            color_style_masks[j] = deepcopy(color_style_masks[j])
          end
        elseif is_conv then
          net:add(layer)
          local sap = nn.SpatialAveragePooling(3,3,1,1,1,1):type(dtype)
          for k = 1, #color_codes do
            color_content_masks[k] = sap:forward(color_content_masks[k]:repeatTensor(1,1,1))[1]:clone()
          end
          for j = 1, #style_image_list do
            for k = 1, #color_style_masks do
              color_style_masks[j][k]   = sap:forward(color_style_masks[j][k]:repeatTensor(1,1,1))[1]:clone()
            end
            color_style_masks[j] = deepcopy(color_style_masks[j])
          end
        else
          net:add(layer)
        end
        color_content_masks = deepcopy(color_content_masks)
      elseif is_pooling and params.pooling == 'avg' then
        assert(layer.padW == 0 and layer.padH == 0)
        local kW, kH = layer.kW, layer.kH
        local dW, dH = layer.dW, layer.dH
        local avg_pool_layer = nn.SpatialAveragePooling(kW, kH, dW, dH):type(dtype)
        local msg = 'Replacing max pooling at layer %d with average pooling'
        print(string.format(msg, i))
        net:add(avg_pool_layer)
      else
        net:add(layer)
      end
      
                                 
      if name == content_layers[next_content_idx] then
        print("Setting up content layer", i, ":", layer.name)
        local norm = params.normalize_gradients
        local loss_module = nn.ContentLoss(params.content_weight, norm):type(dtype)
        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 norm = params.normalize_gradients
        local loss_module 
        if params.content_seg ~= '' then
          loss_module = nn.MaskedStyleLoss(params.style_weight, norm, color_style_masks, color_content_masks, color_codes, name):type(dtype)
        else 
          loss_module = nn.StyleLoss(params.style_weight, norm):type(dtype)
        end
        net:add(loss_module)
        table.insert(style_losses, loss_module)
        next_style_idx = next_style_idx + 1
      end
    end
  end
  if multigpu then
    net = setup_multi_gpu(net, params)
  end
  net:type(dtype)

  -- Capture content targets
  for i = 1, #content_losses do
    content_losses[i].mode = 'capture'
  end
  print 'Capturing content targets'
  print(net)
  content_image_caffe = content_image_caffe:type(dtype)
  net:forward(content_image_caffe:type(dtype))

  -- Capture style targets
  for i = 1, #content_losses do
    content_losses[i].mode = 'none'
  end
  for i = 1, #style_images_caffe do
    print(string.format('Capturing style target %d', i))
    for j = 1, #style_losses do
      style_losses[j].mode = 'capture'
      style_losses[j].blend_weight = style_blend_weights[i]
    end
    net:forward(style_images_caffe[i]:type(dtype))
  end

  -- Set all loss modules to loss mode
  for i = 1, #content_losses do
    content_losses[i].mode = 'loss'
  end
  for i = 1, #style_losses do
    style_losses[i].mode = 'loss'
  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()
           
  if params.style_seg ~= '' then
    style_images_caffe=nil		
    style_seg_images_caffe=nil
  end

  -- 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():mul(0.001)
  elseif params.init == 'image' then
    if init_image then
      img = init_image:clone()
    else
      img = content_image_caffe:clone()
    end
  else
    error('Invalid init type')
  end
  img = img:type(dtype)

  -- 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,
      tolX=-1,
      tolFun=-1,
    }
    if params.lbfgs_num_correction > 0 then
      optim_state.nCorrection = params.lbfgs_num_correction
    end
  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())
      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 function 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 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 setup_gpu(params)
  local multigpu = false
  if params.gpu:find(',') then
    multigpu = true
    params.gpu = params.gpu:split(',')
    for i = 1, #params.gpu do
      params.gpu[i] = tonumber(params.gpu[i]) + 1
    end
  else
    params.gpu = tonumber(params.gpu) + 1
  end
  local dtype = 'torch.FloatTensor'
  if multigpu or params.gpu > 0 then
    if params.backend ~= 'clnn' then
      require 'cutorch'
      require 'cunn'
      if multigpu then
        cutorch.setDevice(params.gpu[1])
      else
        cutorch.setDevice(params.gpu)
      end
      dtype = 'torch.CudaTensor'
    else
      require 'clnn'
      require 'cltorch'
      if multigpu then
        cltorch.setDevice(params.gpu[1])
      else
        cltorch.setDevice(params.gpu)
      end
      dtype = torch.Tensor():cl():type()
    end
  else
    params.backend = 'nn'
  end

  if params.backend == 'cudnn' then
    require 'cudnn'
    if params.cudnn_autotune then
      cudnn.benchmark = true
    end
    cudnn.SpatialConvolution.accGradParameters = nn.SpatialConvolutionMM.accGradParameters -- ie: nop
  end
  return dtype, multigpu
end


function setup_multi_gpu(net, params)
  local DEFAULT_STRATEGIES = {
    [2] = {3},
  }
  local gpu_splits = nil
  if params.multigpu_strategy == '' then
    -- Use a default strategy
    gpu_splits = DEFAULT_STRATEGIES[#params.gpu]
    -- Offset the default strategy by one if we are using TV
    if params.tv_weight > 0 then
      for i = 1, #gpu_splits do gpu_splits[i] = gpu_splits[i] + 1 end
    end
  else
    -- Use the user-specified multigpu strategy
    gpu_splits = params.multigpu_strategy:split(',')
    for i = 1, #gpu_splits do
      gpu_splits[i] = tonumber(gpu_splits[i])
    end
  end
  assert(gpu_splits ~= nil, 'Must specify -multigpu_strategy')
  local gpus = params.gpu

  local cur_chunk = nn.Sequential()
  local chunks = {}
  for i = 1, #net do
    cur_chunk:add(net:get(i))
    if i == gpu_splits[1] then
      table.remove(gpu_splits, 1)
      table.insert(chunks, cur_chunk)
      cur_chunk = nn.Sequential()
    end
  end
  table.insert(chunks, cur_chunk)
  assert(#chunks == #gpus)

  local new_net = nn.Sequential()
  for i = 1, #chunks do
    local out_device = nil
    if i == #chunks then
      out_device = gpus[1]
    end
    new_net:add(nn.GPU(chunks[i], gpus[i], out_device))
  end

  return new_net
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, normalize)
  parent.__init(self)
  self.strength = strength
  self.target = torch.Tensor()
  self.normalize = normalize or false
  self.loss = 0
  self.crit = nn.MSECriterion()
  self.mode = 'none'
end

function ContentLoss:updateOutput(input)
  if self.mode == 'loss' then
    self.loss = self.crit:forward(input, self.target) * self.strength
  elseif self.mode == 'capture' then
    self.target:resizeAs(input):copy(input)
  end
  self.output = input
  return self.output
end

function ContentLoss:updateGradInput(input, gradOutput)
  if self.mode == 'loss' then
    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)
  else
    self.gradInput:resizeAs(gradOutput):copy(gradOutput)
  end
  return self.gradInput
end


local Gram, parent = torch.class('nn.GramMatrix', 'nn.Module')

function Gram:__init()
  parent.__init(self)
end

function Gram:updateOutput(input)
  assert(input:dim() == 3)
  local C, H, W = input:size(1), input:size(2), input:size(3)
  local x_flat = input:view(C, H * W)
  self.output:resize(C, C)
  self.output:mm(x_flat, x_flat:t())
  return self.output
end

function Gram:updateGradInput(input, gradOutput)
  assert(input:dim() == 3 and input:size(1))
  local C, H, W = input:size(1), input:size(2), input:size(3)
  local x_flat = input:view(C, H * W)
  self.gradInput:resize(C, H * W):mm(gradOutput, x_flat)
  self.gradInput:addmm(gradOutput:t(), x_flat)
  self.gradInput = self.gradInput:view(C, H, W)
  return self.gradInput
end


-- Define an nn Module to compute style loss in-place
local StyleLoss, parent = torch.class('nn.StyleLoss', 'nn.Module')

function StyleLoss:__init(strength, normalize)
  parent.__init(self)
  self.normalize = normalize or false
  self.strength = strength
  self.target = torch.Tensor()
  self.mode = 'none'
  self.loss = 0

  self.gram = nn.GramMatrix()
  self.blend_weight = nil
  self.G = nil
  self.crit = nn.MSECriterion()
end

function StyleLoss:updateOutput(input)
  self.G = self.gram:forward(input)
  self.G:div(input:nElement())
  if self.mode == 'capture' then
    if self.blend_weight == nil then
      self.target:resizeAs(self.G):copy(self.G)
    elseif self.target:nElement() == 0 then
      self.target:resizeAs(self.G):copy(self.G):mul(self.blend_weight)
    else
      self.target:add(self.blend_weight, self.G)
    end
  elseif self.mode == 'loss' then
    self.loss = self.strength * self.crit:forward(self.G, self.target)
  end
  self.output = input
  return self.output
end

function StyleLoss:updateGradInput(input, gradOutput)
  if self.mode == 'loss' then
    local dG = self.crit:backward(self.G, self.target)
    dG:div(input:nElement())
    self.gradInput = self.gram:backward(input, 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)
  else
    self.gradInput = gradOutput
  end
  return self.gradInput
end

-- Define an nn Module to compute masked style loss in-place
local MaskedStyleLoss, parent = torch.class('nn.MaskedStyleLoss', 'nn.Module')

function MaskedStyleLoss:__init(strength, normalize, color_style_masks, color_content_masks, color_codes)
  parent.__init(self)
  self.normalize = normalize or false
  self.strength = strength
  self.target_grams = {}
  self.masked_grams = {}
  self.masked_features = {}
  self.mode = 'none'

  self.gram = nn.GramMatrix()
  self.blend_weight = nil

  self.crit = nn.MSECriterion()

  self.color_style_masks = deepcopy(color_style_masks)
  self.color_content_masks = deepcopy(color_content_masks)
  self.color_codes = color_codes
  self.capture_count =1
end

function MaskedStyleLoss:updateOutput(input)
  self.loss = 0
  local masks
  if self.mode == 'capture' then
    masks = self.color_style_masks[self.capture_count]
    self.capture_count = self.capture_count +1
  elseif self.mode == 'loss' then
    masks = self.color_content_masks
    self.color_style_masks=nil
  end
  if self.mode ~= 'none' then
    for j = 1, #self.color_codes do
      local l_mask_ori = masks[j]:clone()
      local l_mask = l_mask_ori:repeatTensor(1,1,1):expandAs(input)
      local l_mean = l_mask_ori:mean()
      local masked_features = torch.cmul(l_mask, input)
      local masked_gram = self.gram:forward(masked_features):clone()
      if l_mean > 0 then
        masked_gram:div(input:nElement() * l_mean)
      end
      if self.mode == 'capture' then
        if j>#self.target_grams then
          table.insert(self.target_grams, masked_gram:mul(self.blend_weight))
          table.insert(self.masked_grams, self.target_grams[j]:clone())
          table.insert(self.masked_features, masked_features)
        else
          self.target_grams[j]:add(masked_gram:mul(self.blend_weight))
        end
      elseif self.mode == 'loss' then
        self.masked_grams[j]=masked_gram
        self.masked_features[j]=masked_features
        self.loss = self.loss + self.crit:forward(self.masked_grams[j], self.target_grams[j]) * l_mean * self.strength
      end
    end
  end
  self.output = input
  return self.output
end

function MaskedStyleLoss:updateGradInput(input, gradOutput)
  if self.mode == 'loss' then
    self.gradInput = gradOutput:clone()
    self.gradInput:zero()
    for j = 1, #self.color_codes do
      local dG = self.crit:backward(self.masked_grams[j], self.target_grams[j])
      dG:div(input:nElement())
      local gradient = self.gram:backward(self.masked_features[j], dG)
      if self.normalize then
        gradient:div(torch.norm(gradient, 1) + 1e-8)
      end
      self.gradInput:add(gradient)
    end
    self.gradInput:mul(self.strength)
    self.gradInput:add(gradOutput)
  else
    self.gradInput = gradOutput
  end
  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

function ExtractMask(seg, color, dtype)
  local mask = nil
  if color == 'green' then
  	mask = torch.lt(seg[1], 0.1)
  	mask:cmul(torch.gt(seg[2], 1-0.1))
  	mask:cmul(torch.lt(seg[3], 0.1))
  elseif color == 'black' then
  	mask = torch.lt(seg[1], 0.1)
  	mask:cmul(torch.lt(seg[2], 0.1))
  	mask:cmul(torch.lt(seg[3], 0.1))
  elseif color == 'white' then
  	mask = torch.gt(seg[1], 1-0.1)
  	mask:cmul(torch.gt(seg[2], 1-0.1))
  	mask:cmul(torch.gt(seg[3], 1-0.1))
  elseif color == 'red' then
    mask = torch.gt(seg[1], 1-0.1)
    mask:cmul(torch.lt(seg[2], 0.1))
    mask:cmul(torch.lt(seg[3], 0.1))
  elseif color == 'blue' then
    mask = torch.lt(seg[1], 0.1)
    mask:cmul(torch.lt(seg[2], 0.1))
    mask:cmul(torch.gt(seg[3], 1-0.1))
  elseif color == 'yellow' then
    mask = torch.gt(seg[1], 1-0.1)
    mask:cmul(torch.gt(seg[2], 1-0.1))
    mask:cmul(torch.lt(seg[3], 0.1))
  elseif color == 'grey' then
    mask = torch.cmul(torch.gt(seg[1], 0.5-0.1), torch.lt(seg[1], 0.5+0.1))
    mask:cmul(torch.cmul(torch.gt(seg[2], 0.5-0.1), torch.lt(seg[2], 0.5+0.1)))
    mask:cmul(torch.cmul(torch.gt(seg[3], 0.5-0.1), torch.lt(seg[3], 0.5+0.1)))
  elseif color == 'lightblue' then
    mask = torch.lt(seg[1], 0.1)
    mask:cmul(torch.gt(seg[2], 1-0.1))
    mask:cmul(torch.gt(seg[3], 1-0.1))
  elseif color == 'purple' then
    mask = torch.gt(seg[1], 1-0.1)
    mask:cmul(torch.lt(seg[2], 0.1))
    mask:cmul(torch.gt(seg[3], 1-0.1))
  else
  	print('ExtractMask(): color not recognized, color = ', color)
  end
  return mask:type(dtype)
end

function deepcopy(orig)
    local orig_type = type(orig)
    local copy
    if orig_type == 'table' then
        copy = {}
        for orig_key, orig_value in next, orig, nil do
            copy[deepcopy(orig_key)] = deepcopy(orig_value)
        end
        setmetatable(copy, deepcopy(getmetatable(orig)))
    else -- number, string, boolean, etc
        copy = orig
    end
    return copy
end


local params = cmd:parse(arg)
main(params)

There appears to an issue with neural_style_seg.lua and the fcn32s-heavy-pascal model:

[libprotobuf ERROR google/protobuf/text_format.cc:274] Error parsing text-format caffe.NetParameter: 9:14: Message type "caffe.PythonParameter" has no field named "param_str".
[libprotobuf WARNING google/protobuf/io/coded_stream.cc:537] Reading dangerously large protocol message.  If the message turns out to be larger than 1073741824 bytes, parsing will be halted for security reasons.  To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h.
[libprotobuf WARNING google/protobuf/io/coded_stream.cc:78] The total number of bytes read was 544614787
Successfully loaded models/fcn32s-heavy-pascal.caffemodel
warning: module 'data [type Python]' not found
warning: module 'data_data_0_split [type Split]' not found
warning: module 'upscore [type Deconvolution]' not found
warning: module 'score [type Crop]' not found
conv1_1: 64 3 3 3
conv1_2: 64 64 3 3
conv2_1: 128 64 3 3
conv2_2: 128 128 3 3
conv3_1: 256 128 3 3
conv3_2: 256 256 3 3
conv3_3: 256 256 3 3
conv4_1: 512 256 3 3
conv4_2: 512 512 3 3
conv4_3: 512 512 3 3
conv5_1: 512 512 3 3
conv5_2: 512 512 3 3
conv5_3: 512 512 3 3
fc6: 4096 512 7 7
fc7: 4096 4096 1 1
score_fr: 21 4096 1 1
Setting up style layer          2       :       relu1_1
Setting up style layer          7       :       relu2_1
Setting up style layer          12      :       relu3_1
Setting up style layer          19      :       relu4_1
Setting up content layer        21      :       relu4_2
Setting up style layer          26      :       relu5_1
Capturing content targets
nn.Sequential {
  [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> (7) -> (8) -> (9) -> (10) -> (11) -> (12) -> (13) -> (14) -> (15) -> (16) -> (17) -> (18) -> (19) -> (20) -> (21) -> (22) -> (23) -> (24) -> (25) -> (26) -> (27) -> (28) -> (29) -> (30) -> (31) -> (32) -> output]
  (1): cudnn.SpatialConvolution(3 -> 64, 3x3, 1,1, 100,100)
  (2): cudnn.ReLU
  (3): nn.MaskedStyleLoss
  (4): cudnn.SpatialConvolution(64 -> 64, 3x3, 1,1, 1,1)
  (5): cudnn.ReLU
  (6): cudnn.SpatialMaxPooling(2x2, 2,2)
  (7): cudnn.SpatialConvolution(64 -> 128, 3x3, 1,1, 1,1)
  (8): cudnn.ReLU
  (9): nn.MaskedStyleLoss
  (10): cudnn.SpatialConvolution(128 -> 128, 3x3, 1,1, 1,1)
  (11): cudnn.ReLU
  (12): cudnn.SpatialMaxPooling(2x2, 2,2)
  (13): cudnn.SpatialConvolution(128 -> 256, 3x3, 1,1, 1,1)
  (14): cudnn.ReLU
  (15): nn.MaskedStyleLoss
  (16): cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1)
  (17): cudnn.ReLU
  (18): cudnn.SpatialConvolution(256 -> 256, 3x3, 1,1, 1,1)
  (19): cudnn.ReLU
  (20): cudnn.SpatialMaxPooling(2x2, 2,2)
  (21): cudnn.SpatialConvolution(256 -> 512, 3x3, 1,1, 1,1)
  (22): cudnn.ReLU
  (23): nn.MaskedStyleLoss
  (24): cudnn.SpatialConvolution(512 -> 512, 3x3, 1,1, 1,1)
  (25): cudnn.ReLU
  (26): nn.ContentLoss
  (27): cudnn.SpatialConvolution(512 -> 512, 3x3, 1,1, 1,1)
  (28): cudnn.ReLU
  (29): cudnn.SpatialMaxPooling(2x2, 2,2)
  (30): cudnn.SpatialConvolution(512 -> 512, 3x3, 1,1, 1,1)
  (31): cudnn.ReLU
  (32): nn.MaskedStyleLoss
}
Capturing style target 1
/home/ubuntu/torch/install/bin/luajit: /home/ubuntu/torch/install/share/lua/5.1/nn/Container.lua:67:
In 3 module of nn.Sequential:
/home/ubuntu/torch/install/share/lua/5.1/torch/Tensor.lua:326: incorrect size: only supporting singleton expansion (size=1)
stack traceback:
        [C]: in function 'error'
        /home/ubuntu/torch/install/share/lua/5.1/torch/Tensor.lua:326: in function 'expandAs'
        neural_style_seg.lua:698: in function <neural_style_seg.lua:685>
        [C]: in function 'xpcall'
        /home/ubuntu/torch/install/share/lua/5.1/nn/Container.lua:63: in function 'rethrowErrors'
        /home/ubuntu/torch/install/share/lua/5.1/nn/Sequential.lua:44: in function 'forward'
        neural_style_seg.lua:265: in function 'main'
        neural_style_seg.lua:838: in main chunk
        [C]: in function 'dofile'
        ...untu/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:150: in main chunk
        [C]: at 0x00405d50

WARNING: If you see a stack trace below, it doesn't point to the place where this error occurred. Please use only the one above.
stack traceback:
        [C]: in function 'error'
        /home/ubuntu/torch/install/share/lua/5.1/nn/Container.lua:67: in function 'rethrowErrors'
        /home/ubuntu/torch/install/share/lua/5.1/nn/Sequential.lua:44: in function 'forward'
        neural_style_seg.lua:265: in function 'main'
        neural_style_seg.lua:838: in main chunk
        [C]: in function 'dofile'
        ...untu/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:150: in main chunk
        [C]: at 0x00405d50

Basically the model won't work with the script with the default train_val.prototxt. The model still works with the normal unmodified Neural-Style script.