300 lines
11 KiB
Lua
300 lines
11 KiB
Lua
require 'pl'
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local __FILE__ = (function() return string.gsub(debug.getinfo(2, 'S').source, "^@", "") end)()
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package.path = path.join(path.dirname(__FILE__), "lib", "?.lua;") .. package.path
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require 'sys'
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require 'w2nn'
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local iproc = require 'iproc'
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local reconstruct = require 'reconstruct'
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local image_loader = require 'image_loader'
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local alpha_util = require 'alpha_util'
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torch.setdefaulttensortype('torch.FloatTensor')
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local function format_output(opt, src, no)
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no = no or 1
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local name = path.basename(src)
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local e = path.extension(name)
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local basename = name:sub(0, name:len() - e:len())
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if opt.o == "(auto)" then
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return path.join(path.dirname(src), string.format("%s_%s.png", basename, opt.m))
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else
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local basename_pos = opt.o:find("%%s")
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local no_pos = opt.o:find("%%%d*d")
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if basename_pos ~= nil and no_pos ~= nil then
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if basename_pos < no_pos then
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return string.format(opt.o, basename, no)
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else
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return string.format(opt.o, no, basename)
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end
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elseif basename_pos ~= nil then
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return string.format(opt.o, basename)
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elseif no_pos ~= nil then
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return string.format(opt.o, no)
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else
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return opt.o
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end
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end
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end
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local function convert_image(opt)
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local x, meta = image_loader.load_float(opt.i)
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if not x then
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error(string.format("failed to load image: %s", opt.i))
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end
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local alpha = meta.alpha
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local new_x = nil
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local scale_f, image_f
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if opt.tta == 1 then
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scale_f = function(model, scale, x, block_size, batch_size)
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return reconstruct.scale_tta(model, opt.tta_level,
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scale, x, block_size, batch_size)
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end
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image_f = function(model, x, block_size, batch_size)
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return reconstruct.image_tta(model, opt.tta_level,
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x, block_size, batch_size)
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end
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else
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scale_f = reconstruct.scale
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image_f = reconstruct.image
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end
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opt.o = format_output(opt, opt.i)
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if opt.m == "noise" then
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local model_path = path.join(opt.model_dir, ("noise%d_model.t7"):format(opt.noise_level))
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local model = w2nn.load_model(model_path, opt.force_cudnn, opt.load_mode)
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if not model then
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error("Load Error: " .. model_path)
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end
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local t = sys.clock()
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new_x = image_f(model, x, opt.crop_size, opt.batch_size)
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new_x = alpha_util.composite(new_x, alpha)
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if not opt.q then
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print(opt.o .. ": " .. (sys.clock() - t) .. " sec")
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end
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elseif opt.m == "scale" then
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local model_path = path.join(opt.model_dir, ("scale%.1fx_model.t7"):format(opt.scale))
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local model = w2nn.load_model(model_path, opt.force_cudnn, opt.load_mode)
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if not model then
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error("Load Error: " .. model_path)
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end
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local t = sys.clock()
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x = alpha_util.make_border(x, alpha, reconstruct.offset_size(model))
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new_x = scale_f(model, opt.scale, x, opt.crop_size, opt.batch_size, opt.batch_size)
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new_x = alpha_util.composite(new_x, alpha, model)
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if not opt.q then
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print(opt.o .. ": " .. (sys.clock() - t) .. " sec")
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end
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elseif opt.m == "noise_scale" then
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local model_path = path.join(opt.model_dir, ("noise%d_scale%.1fx_model.t7"):format(opt.noise_level, opt.scale))
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if path.exists(model_path) then
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local scale_model_path = path.join(opt.model_dir, ("scale%.1fx_model.t7"):format(opt.scale))
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local t, scale_model = pcall(w2nn.load_model, scale_model_path, opt.force_cudnn)
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local model = w2nn.load_model(model_path, opt.force_cudnn, opt.load_mode)
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if not t then
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scale_model = model
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end
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local t = sys.clock()
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x = alpha_util.make_border(x, alpha, reconstruct.offset_size(scale_model))
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new_x = scale_f(model, opt.scale, x, opt.crop_size, opt.batch_size)
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new_x = alpha_util.composite(new_x, alpha, scale_model)
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if not opt.q then
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print(opt.o .. ": " .. (sys.clock() - t) .. " sec")
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end
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else
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local noise_model_path = path.join(opt.model_dir, ("noise%d_model.t7"):format(opt.noise_level))
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local noise_model = w2nn.load_model(noise_model_path, opt.force_cudnn, opt.load_mode)
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local scale_model_path = path.join(opt.model_dir, ("scale%.1fx_model.t7"):format(opt.scale))
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local scale_model = w2nn.load_model(scale_model_path, opt.force_cudnn, opt.load_mode)
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local t = sys.clock()
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x = alpha_util.make_border(x, alpha, reconstruct.offset_size(scale_model))
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x = image_f(noise_model, x, opt.crop_size, opt.batch_size)
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new_x = scale_f(scale_model, opt.scale, x, opt.crop_size, opt.batch_size)
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new_x = alpha_util.composite(new_x, alpha, scale_model)
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if not opt.q then
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print(opt.o .. ": " .. (sys.clock() - t) .. " sec")
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end
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end
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elseif opt.m == "user" then
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local model_path = opt.model_path
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local model = w2nn.load_model(model_path, opt.force_cudnn, opt.load_mode)
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if not model then
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error("Load Error: " .. model_path)
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end
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local t = sys.clock()
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x = alpha_util.make_border(x, alpha, reconstruct.offset_size(model))
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if opt.scale == 1 then
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new_x = image_f(model, x, opt.crop_size, opt.batch_size)
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else
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new_x = scale_f(model, opt.scale, x, opt.crop_size, opt.batch_size)
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end
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new_x = alpha_util.composite(new_x, alpha) -- TODO: should it use model?
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if not opt.q then
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print(opt.o .. ": " .. (sys.clock() - t) .. " sec")
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end
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else
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error("undefined method:" .. opt.method)
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end
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image_loader.save_png(opt.o, new_x, tablex.update({depth = opt.depth, inplace = true}, meta))
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end
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local function convert_frames(opt)
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local model_path, scale_model, t
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local noise_scale_model = {}
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local noise_model = {}
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local user_model = nil
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local scale_f, image_f
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if opt.tta == 1 then
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scale_f = function(model, scale, x, block_size, batch_size)
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return reconstruct.scale_tta(model, opt.tta_level,
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scale, x, block_size, batch_size)
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end
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image_f = function(model, x, block_size, batch_size)
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return reconstruct.image_tta(model, opt.tta_level,
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x, block_size, batch_size)
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end
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else
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scale_f = reconstruct.scale
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image_f = reconstruct.image
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end
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if opt.m == "scale" then
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model_path = path.join(opt.model_dir, ("scale%.1fx_model.t7"):format(opt.scale))
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scale_model = w2nn.load_model(model_path, opt.force_cudnn, opt.load_mode)
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elseif opt.m == "noise" then
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model_path = path.join(opt.model_dir, string.format("noise%d_model.t7", opt.noise_level))
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noise_model[opt.noise_level] = w2nn.load_model(model_path, opt.force_cudnn, opt.load_mode)
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elseif opt.m == "noise_scale" then
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local model_path = path.join(opt.model_dir, ("noise%d_scale%.1fx_model.t7"):format(opt.noise_level, opt.scale))
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if path.exists(model_path) then
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noise_scale_model[opt.noise_level] = w2nn.load_model(model_path, opt.force_cudnn, opt.load_mode)
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model_path = path.join(opt.model_dir, ("scale%.1fx_model.t7"):format(opt.scale))
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t, scale_model = pcall(w2nn.load_model, model_path, opt.force_cudnn, opt.load_mode)
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if not t then
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scale_model = noise_scale_model[opt.noise_level]
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end
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else
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model_path = path.join(opt.model_dir, ("scale%.1fx_model.t7"):format(opt.scale))
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scale_model = w2nn.load_model(model_path, opt.force_cudnn, opt.load_mode)
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model_path = path.join(opt.model_dir, string.format("noise%d_model.t7", opt.noise_level))
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noise_model[opt.noise_level] = w2nn.load_model(model_path, opt.force_cudnn, opt.load_mode)
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end
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elseif opt.m == "user" then
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user_model = w2nn.load_model(opt.model_path, opt.force_cudnn, opt.load_mode)
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end
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local fp = io.open(opt.l)
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if not fp then
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error("Open Error: " .. opt.l)
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end
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local count = 0
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local lines = {}
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for line in fp:lines() do
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table.insert(lines, line)
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end
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fp:close()
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for i = 1, #lines do
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local output = format_output(opt, lines[i], i)
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if opt.resume == 0 or path.exists(output) == false then
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local x, meta = image_loader.load_float(lines[i])
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if not x then
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io.stderr:write(string.format("failed to load image: %s\n", lines[i]))
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else
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local alpha = meta.alpha
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local new_x = nil
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if opt.m == "noise" then
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new_x = image_f(noise_model[opt.noise_level], x, opt.crop_size, opt.batch_size)
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new_x = alpha_util.composite(new_x, alpha)
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elseif opt.m == "scale" then
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x = alpha_util.make_border(x, alpha, reconstruct.offset_size(scale_model))
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new_x = scale_f(scale_model, opt.scale, x, opt.crop_size, opt.batch_size)
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new_x = alpha_util.composite(new_x, alpha, scale_model)
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elseif opt.m == "noise_scale" then
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x = alpha_util.make_border(x, alpha, reconstruct.offset_size(scale_model))
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if noise_scale_model[opt.noise_level] then
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new_x = scale_f(noise_scale_model[opt.noise_level], opt.scale, x, opt.crop_size, opt.batch_size)
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else
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x = image_f(noise_model[opt.noise_level], x, opt.crop_size, opt.batch_size)
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new_x = scale_f(scale_model, opt.scale, x, opt.crop_size, opt.batch_size)
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end
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new_x = alpha_util.composite(new_x, alpha, scale_model)
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elseif opt.m == "user" then
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x = alpha_util.make_border(x, alpha, reconstruct.offset_size(user_model))
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if opt.scale == 1 then
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new_x = image_f(user_model, x, opt.crop_size, opt.batch_size)
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else
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new_x = scale_f(user_model, opt.scale, x, opt.crop_size, opt.batch_size)
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end
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new_x = alpha_util.composite(new_x, alpha)
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else
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error("undefined method:" .. opt.method)
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end
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image_loader.save_png(output, new_x,
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tablex.update({depth = opt.depth, inplace = true}, meta))
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end
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if not opt.q then
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xlua.progress(i, #lines)
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end
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if i % 10 == 0 then
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collectgarbage()
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end
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else
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if not opt.q then
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xlua.progress(i, #lines)
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end
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end
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end
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end
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local function waifu2x()
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local cmd = torch.CmdLine()
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cmd:text()
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cmd:text("waifu2x")
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cmd:text("Options:")
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cmd:option("-i", "images/miku_small.png", 'path to input image')
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cmd:option("-l", "", 'path to image-list.txt')
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cmd:option("-scale", 2, 'scale factor')
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cmd:option("-o", "(auto)", 'path to output file')
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cmd:option("-depth", 8, 'bit-depth of the output image (8|16)')
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cmd:option("-model_dir", "./models/cunet/art", 'path to model directory')
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cmd:option("-name", "user", 'model name for user method')
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cmd:option("-m", "noise_scale", 'method (noise|scale|noise_scale|user)')
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cmd:option("-method", "", 'same as -m')
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cmd:option("-noise_level", 1, '(0|1|2|3)')
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cmd:option("-crop_size", 256, 'patch size per process')
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cmd:option("-batch_size", 1, 'batch_size')
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cmd:option("-resume", 0, "skip existing files (0|1)")
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cmd:option("-thread", -1, "number of CPU threads")
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cmd:option("-tta", 0, 'use TTA mode. It is slow but slightly high quality (0|1)')
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cmd:option("-tta_level", 8, 'TTA level (2|4|8). A higher value makes better quality output but slow')
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cmd:option("-force_cudnn", 0, 'use cuDNN backend (0|1)')
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cmd:option("-q", 0, 'quiet (0|1)')
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cmd:option("-gpu", 1, 'Device ID')
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cmd:option("-load_mode", "ascii", "ascii/binary")
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local opt = cmd:parse(arg)
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if opt.method:len() > 0 then
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opt.m = opt.method
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end
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if opt.thread > 0 then
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torch.setnumthreads(opt.thread)
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end
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cutorch.setDevice(opt.gpu)
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if cudnn then
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cudnn.fastest = true
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if opt.l:len() > 0 then
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cudnn.benchmark = true -- find fastest algo
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else
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cudnn.benchmark = false
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end
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end
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opt.force_cudnn = opt.force_cudnn == 1
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opt.q = opt.q == 1
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opt.model_path = path.join(opt.model_dir, string.format("%s_model.t7", opt.name))
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if string.len(opt.l) == 0 then
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convert_image(opt)
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else
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convert_frames(opt)
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end
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end
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waifu2x()
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