waifu2x/lib/reconstruct.lua

require 'image'
local iproc = require 'iproc'
local srcnn = require 'srcnn'

local function reconstruct_nn(model, x, inner_scale, offset, block_size, batch_size)
   batch_size = batch_size or 1
   if x:dim() == 2 then
      x = x:reshape(1, x:size(1), x:size(2))
   end
   local ch = x:size(1)
   local new_x = torch.Tensor(x:size(1), x:size(2) * inner_scale, x:size(3) * inner_scale):zero()
   local input_block_size = block_size / inner_scale
   local output_block_size = block_size
   local output_size = output_block_size - offset * 2
   local output_size_in_input = input_block_size - math.ceil(offset / inner_scale) * 2
   local input_indexes = {}
   local output_indexes = {}
   for i = 1, x:size(2), output_size_in_input do
      for j = 1, x:size(3), output_size_in_input do
	 if i + input_block_size - 1 <= x:size(2) and j + input_block_size - 1 <= x:size(3) then
	    local index = {{},
			   {i, i + input_block_size - 1},
			   {j, j + input_block_size - 1}}
	    local ii = (i - 1) * inner_scale + 1
	    local jj = (j - 1) * inner_scale + 1
	    local output_index = {{}, { ii , ii + output_size - 1 },
	       { jj, jj + output_size - 1}}
	    table.insert(input_indexes, index)
	    table.insert(output_indexes, output_index)
	 end
      end
   end
   local input = torch.Tensor(batch_size, ch, input_block_size, input_block_size)
   local input_cuda = torch.CudaTensor(batch_size, ch, input_block_size, input_block_size)
   for i = 1, #input_indexes, batch_size do
      local c = 0
      local output
      for j = 0, batch_size - 1 do
	 if i + j > #input_indexes then
	    break
	 end
	 input[j+1]:copy(x[input_indexes[i + j]])
	 c = c + 1
      end
      input_cuda:copy(input)
      if c == batch_size then
	 output = model:forward(input_cuda)
      else
	 output = model:forward(input_cuda:narrow(1, 1, c))
      end
      --output = output:view(batch_size, ch, output_size, output_size)
      for j = 0, c - 1 do
	 new_x[output_indexes[i + j]]:copy(output[j+1])
      end
   end
   return new_x
end
local reconstruct = {}
function reconstruct.is_rgb(model)
   if srcnn.channels(model) == 3 then
      -- 3ch RGB
      return true
   else
      -- 1ch Y
      return false
   end
end
function reconstruct.offset_size(model)
   return srcnn.offset_size(model)
end
function reconstruct.has_resize(model)
   return srcnn.scale_factor(model) > 1
end
function reconstruct.inner_scale(model)
   return srcnn.scale_factor(model)
end
local function padding_params(x, model, block_size)
   local p = {}
   local offset = reconstruct.offset_size(model)
   p.x_w = x:size(3)
   p.x_h = x:size(2)
   p.inner_scale = reconstruct.inner_scale(model)
   local input_offset = math.ceil(offset / p.inner_scale)
   local input_block_size = block_size / p.inner_scale
   local process_size = input_block_size - input_offset * 2
   local h_blocks = math.floor(p.x_h / process_size) +
      ((p.x_h % process_size == 0 and 0) or 1)
   local w_blocks = math.floor(p.x_w / process_size) +
      ((p.x_w % process_size == 0 and 0) or 1)
   local h = (h_blocks * process_size) + input_offset * 2
   local w = (w_blocks * process_size) + input_offset * 2
   p.pad_h1 = input_offset
   p.pad_w1 = input_offset
   p.pad_h2 = (h - input_offset) - p.x_h
   p.pad_w2 = (w - input_offset) - p.x_w
   return p
end
function reconstruct.image_y(model, x, offset, block_size, batch_size)
   block_size = block_size or 128
   local p = padding_params(x, model, block_size)
   x = image.rgb2yuv(iproc.padding(x, p.pad_w1, p.pad_w2, p.pad_h1, p.pad_h2))
   local y = reconstruct_nn(model, x[1], p.inner_scale, offset, block_size, batch_size)
   x = iproc.crop(x, p.pad_w1, p.pad_w2, p.pad_w1 + p.x_w, p.pad_w2 + p.x_h)
   y = iproc.crop(y, 0, 0, p.x_w, p.x_h)
   y[torch.lt(y, 0)] = 0
   y[torch.gt(y, 1)] = 1
   x[1]:copy(y)
   local output = image.yuv2rgb(x)
   output[torch.lt(output, 0)] = 0
   output[torch.gt(output, 1)] = 1
   x = nil
   y = nil
   collectgarbage()
   
   return output
end
function reconstruct.scale_y(model, scale, x, offset, block_size, batch_size, upsampling_filter)
   upsampling_filter = upsampling_filter or "Box"
   block_size = block_size or 128
   local x_lanczos
   if reconstruct.has_resize(model) then
      x_lanczos = iproc.scale(x, x:size(3) * scale, x:size(2) * scale, "Lanczos")
   else
      x_lanczos = iproc.scale(x, x:size(3) * scale, x:size(2) * scale, "Lanczos")
      x = iproc.scale(x, x:size(3) * scale, x:size(2) * scale, upsampling_filter)
   end
   local p = padding_params(x, model, block_size)
   if p.x_w * p.x_h > 2048*2048 then
      collectgarbage()
   end
   x = image.rgb2yuv(iproc.padding(x, p.pad_w1, p.pad_w2, p.pad_h1, p.pad_h2))
   x_lanczos = image.rgb2yuv(x_lanczos)
   local y = reconstruct_nn(model, x[1], p.inner_scale, offset, block_size, batch_size)
   y = iproc.crop(y, 0, 0, p.x_w * p.inner_scale, p.x_h * p.inner_scale)
   y[torch.lt(y, 0)] = 0
   y[torch.gt(y, 1)] = 1
   x_lanczos[1]:copy(y)
   local output = image.yuv2rgb(x_lanczos)
   output[torch.lt(output, 0)] = 0
   output[torch.gt(output, 1)] = 1
   x = nil
   x_lanczos = nil
   y = nil
   collectgarbage()
   
   return output
end
function reconstruct.image_rgb(model, x, offset, block_size, batch_size)
   block_size = block_size or 128
   local p = padding_params(x, model, block_size)
   x = iproc.padding(x, p.pad_w1, p.pad_w2, p.pad_h1, p.pad_h2)
   if p.x_w * p.x_h > 2048*2048 then
      collectgarbage()
   end
   local y = reconstruct_nn(model, x, p.inner_scale, offset, block_size, batch_size)
   local output = iproc.crop(y, 0, 0, p.x_w, p.x_h)
   output[torch.lt(output, 0)] = 0
   output[torch.gt(output, 1)] = 1
   x = nil
   y = nil
   collectgarbage()

   return output
end
function reconstruct.scale_rgb(model, scale, x, offset, block_size, batch_size, upsampling_filter)
   upsampling_filter = upsampling_filter or "Box"
   block_size = block_size or 128
   if not reconstruct.has_resize(model) then
      x = iproc.scale(x, x:size(3) * scale, x:size(2) * scale, upsampling_filter)
   end
   local p = padding_params(x, model, block_size)
   x = iproc.padding(x, p.pad_w1, p.pad_w2, p.pad_h1, p.pad_h2)
   if p.x_w * p.x_h > 2048*2048 then
      collectgarbage()
   end
   local y
   y = reconstruct_nn(model, x, p.inner_scale, offset, block_size, batch_size)
   local output = iproc.crop(y, 0, 0, p.x_w * p.inner_scale, p.x_h * p.inner_scale)
   output[torch.lt(output, 0)] = 0
   output[torch.gt(output, 1)] = 1
   x = nil
   y = nil
   collectgarbage()

   return output
end
function reconstruct.image(model, x, block_size)
   local i2rgb = false
   if x:size(1) == 1 then
      local new_x = torch.Tensor(3, x:size(2), x:size(3))
      new_x[1]:copy(x)
      new_x[2]:copy(x)
      new_x[3]:copy(x)
      x = new_x
      i2rgb = true
   end
   if reconstruct.is_rgb(model) then
      x = reconstruct.image_rgb(model, x,
				reconstruct.offset_size(model), block_size)
   else
      x = reconstruct.image_y(model, x,
			      reconstruct.offset_size(model), block_size)
   end
   if i2rgb then
      x = image.rgb2y(x)
   end
   return x
end
function reconstruct.scale(model, scale, x, block_size, upsampling_filter)
   local i2rgb = false
   if x:size(1) == 1 then
      local new_x = torch.Tensor(3, x:size(2), x:size(3))
      new_x[1]:copy(x)
      new_x[2]:copy(x)
      new_x[3]:copy(x)
      x = new_x
      i2rgb = true
   end
   if reconstruct.is_rgb(model) then
      x = reconstruct.scale_rgb(model, scale, x,
				reconstruct.offset_size(model),
				block_size,
				upsampling_filter)
   else
      x = reconstruct.scale_y(model, scale, x,
			      reconstruct.offset_size(model),
			      block_size,
			      upsampling_filter)
   end
   if i2rgb then
      x = image.rgb2y(x)
   end
   return x
end
local function tr_f(a)
   return a:transpose(2, 3):contiguous() 
end
local function itr_f(a)
   return a:transpose(2, 3):contiguous()
end
local augmented_patterns = {
   {
      forward = function (a) return a end,
      backward = function (a) return a end
   },
   {
      forward = function (a) return image.hflip(a) end,
      backward = function (a) return image.hflip(a) end
   },
   {
      forward = function (a) return image.vflip(a) end,
      backward = function (a) return image.vflip(a) end
   },
   {
      forward = function (a) return image.hflip(image.vflip(a)) end,
      backward = function (a) return image.vflip(image.hflip(a)) end
   },
   {
      forward = function (a) return tr_f(a) end,
      backward = function (a) return itr_f(a) end
   },
   {
      forward = function (a) return image.hflip(tr_f(a)) end,
      backward = function (a) return itr_f(image.hflip(a)) end
   },
   {
      forward = function (a) return image.vflip(tr_f(a)) end,
      backward = function (a) return itr_f(image.vflip(a)) end
   },
   {
      forward = function (a) return image.hflip(image.vflip(tr_f(a))) end,
      backward = function (a) return itr_f(image.vflip(image.hflip(a))) end
   }
}
local function get_augmented_patterns(n)
   if n == 2 then
      return {augmented_patterns[1], augmented_patterns[5]}
   elseif n == 4 then
      return {augmented_patterns[1], augmented_patterns[5],
	      augmented_patterns[2], augmented_patterns[7]}
   elseif n == 8 then
      return augmented_patterns
   else
      error("unsupported TTA level: " .. n)
   end
end
local function tta(f, n, model, x, block_size)
   local average = nil
   local offset = reconstruct.offset_size(model)
   local augments = get_augmented_patterns(n)
   for i = 1, #augments do 
      local out = augments[i].backward(f(model, augments[i].forward(x), offset, block_size))
      if not average then
	 average = out
      else
	 average:add(out)
      end
   end
   return average:div(#augments)
end
function reconstruct.image_tta(model, n, x, block_size)
   if reconstruct.is_rgb(model) then
      return tta(reconstruct.image_rgb, n, model, x, block_size)
   else
      return tta(reconstruct.image_y, n, model, x, block_size)
   end
end
function reconstruct.scale_tta(model, n, scale, x, block_size, upsampling_filter)
   if reconstruct.is_rgb(model) then
      local f = function (model, x, offset, block_size)
	 return reconstruct.scale_rgb(model, scale, x, offset, block_size, upsampling_filter)
      end
      return tta(f, n, model, x, block_size)
   else
      local f = function (model, x, offset, block_size)
	 return reconstruct.scale_y(model, scale, x, offset, block_size, upsampling_filter)
      end
      return tta(f, n, model, x, block_size)
   end
end

return reconstruct