144 lines
4.5 KiB
Lua
144 lines
4.5 KiB
Lua
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 'xlua'
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require 'pl'
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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 gm = require 'graphicsmagick'
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local cmd = torch.CmdLine()
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cmd:text()
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cmd:text("waifu2x-benchmark")
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cmd:text("Options:")
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cmd:option("-seed", 11, 'fixed input seed')
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cmd:option("-dir", "./data/test", 'test image directory')
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cmd:option("-model1_dir", "./models/anime_style_art", 'model1 directory')
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cmd:option("-model2_dir", "./models/anime_style_art_rgb", 'model2 directory')
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cmd:option("-method", "scale", '(scale|noise)')
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cmd:option("-noise_level", 1, '(1|2)')
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cmd:option("-color_weight", "y", '(y|rgb)')
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cmd:option("-jpeg_quality", 75, 'jpeg quality')
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cmd:option("-jpeg_times", 1, 'jpeg compression times')
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cmd:option("-jpeg_quality_down", 5, 'value of jpeg quality to decrease each times')
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local opt = cmd:parse(arg)
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torch.setdefaulttensortype('torch.FloatTensor')
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local function MSE(x1, x2)
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return (x1 - x2):pow(2):mean()
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end
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local function YMSE(x1, x2)
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local x1_2 = x1:clone()
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local x2_2 = x2:clone()
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x1_2[1]:mul(0.299 * 3)
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x1_2[2]:mul(0.587 * 3)
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x1_2[3]:mul(0.114 * 3)
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x2_2[1]:mul(0.299 * 3)
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x2_2[2]:mul(0.587 * 3)
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x2_2[3]:mul(0.114 * 3)
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return (x1_2 - x2_2):pow(2):mean()
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end
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local function PSNR(x1, x2)
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local mse = MSE(x1, x2)
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return 20 * (math.log(1.0 / math.sqrt(mse)) / math.log(10))
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end
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local function YPSNR(x1, x2)
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local mse = YMSE(x1, x2)
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return 20 * (math.log((0.587 * 3) / math.sqrt(mse)) / math.log(10))
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end
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local function transform_jpeg(x)
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for i = 1, opt.jpeg_times do
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jpeg = gm.Image(x, "RGB", "DHW")
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jpeg:format("jpeg")
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jpeg:samplingFactors({1.0, 1.0, 1.0})
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blob, len = jpeg:toBlob(opt.jpeg_quality - (i - 1) * opt.jpeg_quality_down)
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jpeg:fromBlob(blob, len)
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x = jpeg:toTensor("byte", "RGB", "DHW")
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end
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return x
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end
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local function transform_scale(x)
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return iproc.scale(x,
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x:size(3) * 0.5,
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x:size(2) * 0.5,
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"Box")
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end
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local function benchmark(color_weight, x, input_func, v1_noise, v2_noise)
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local v1_mse = 0
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local v2_mse = 0
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local v1_psnr = 0
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local v2_psnr = 0
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for i = 1, #x do
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local ground_truth = x[i]
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local input, v1_output, v2_output
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input = input_func(ground_truth)
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input = input:float():div(255)
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ground_truth = ground_truth:float():div(255)
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t = sys.clock()
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if input:size(3) == ground_truth:size(3) then
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v1_output = reconstruct.image(v1_noise, input)
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v2_output = reconstruct.image(v2_noise, input)
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else
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v1_output = reconstruct.scale(v1_noise, 2.0, input)
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v2_output = reconstruct.scale(v2_noise, 2.0, input)
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end
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if color_weight == "y" then
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v1_mse = v1_mse + YMSE(ground_truth, v1_output)
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v1_psnr = v1_psnr + YPSNR(ground_truth, v1_output)
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v2_mse = v2_mse + YMSE(ground_truth, v2_output)
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v2_psnr = v2_psnr + YPSNR(ground_truth, v2_output)
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elseif color_weight == "rgb" then
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v1_mse = v1_mse + MSE(ground_truth, v1_output)
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v1_psnr = v1_psnr + PSNR(ground_truth, v1_output)
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v2_mse = v2_mse + MSE(ground_truth, v2_output)
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v2_psnr = v2_psnr + PSNR(ground_truth, v2_output)
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end
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io.stdout:write(
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string.format("%d/%d; v1_mse=%f, v2_mse=%f, v1_psnr=%f, v2_psnr=%f \r",
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i, #x,
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v1_mse / i, v2_mse / i,
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v1_psnr / i, v2_psnr / i
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)
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)
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io.stdout:flush()
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end
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io.stdout:write("\n")
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end
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local function load_data(test_dir)
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local test_x = {}
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local files = dir.getfiles(test_dir, "*.*")
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for i = 1, #files do
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table.insert(test_x, iproc.crop_mod4(image_loader.load_byte(files[i])))
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xlua.progress(i, #files)
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end
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return test_x
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end
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print(opt)
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torch.manualSeed(opt.seed)
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cutorch.manualSeed(opt.seed)
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if opt.method == "scale" then
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local v1 = torch.load(path.join(opt.model1_dir, "scale2.0x_model.t7"), "ascii")
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local v2 = torch.load(path.join(opt.model2_dir, "scale2.0x_model.t7"), "ascii")
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local test_x = load_data(opt.dir)
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benchmark(opt.color_weight, test_x, transform_scale, v1, v2)
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elseif opt.method == "noise" then
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local v1 = torch.load(path.join(opt.model1_dir, string.format("noise%d_model.t7", opt.noise_level)), "ascii")
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local v2 = torch.load(path.join(opt.model2_dir, string.format("noise%d_model.t7", opt.noise_level)), "ascii")
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local test_x = load_data(opt.dir)
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benchmark(opt.color_weight, test_x, transform_jpeg, v1, v2)
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end
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