209 lines
6.6 KiB
Lua
209 lines
6.6 KiB
Lua
require 'pl'
|
|
local __FILE__ = (function() return string.gsub(debug.getinfo(2, 'S').source, "^@", "") end)()
|
|
package.path = path.join(path.dirname(__FILE__), "..", "lib", "?.lua;") .. package.path
|
|
require 'xlua'
|
|
require 'w2nn'
|
|
local iproc = require 'iproc'
|
|
local reconstruct = require 'reconstruct'
|
|
local image_loader = require 'image_loader'
|
|
local gm = require 'graphicsmagick'
|
|
|
|
local cmd = torch.CmdLine()
|
|
cmd:text()
|
|
cmd:text("waifu2x-benchmark")
|
|
cmd:text("Options:")
|
|
|
|
cmd:option("-dir", "./data/test", 'test image directory')
|
|
cmd:option("-model1_dir", "./models/anime_style_art_rgb", 'model1 directory')
|
|
cmd:option("-model2_dir", "", 'model2 directory (optional)')
|
|
cmd:option("-method", "scale", '(scale|noise)')
|
|
cmd:option("-filter", "Catrom", "downscaling filter (Box|Lanczos|Catrom(Bicubic))")
|
|
cmd:option("-color", "y", '(rgb|y)')
|
|
cmd:option("-noise_level", 1, 'model noise level')
|
|
cmd:option("-jpeg_quality", 75, 'jpeg quality')
|
|
cmd:option("-jpeg_times", 1, 'jpeg compression times')
|
|
cmd:option("-jpeg_quality_down", 5, 'value of jpeg quality to decrease each times')
|
|
cmd:option("-range_bug", 0, 'Reproducing the dynamic range bug that is caused by MATLAB\'s rgb2ycbcr(1|0)')
|
|
|
|
local opt = cmd:parse(arg)
|
|
torch.setdefaulttensortype('torch.FloatTensor')
|
|
if cudnn then
|
|
cudnn.fastest = true
|
|
cudnn.benchmark = false
|
|
end
|
|
|
|
local function rgb2y_matlab(x)
|
|
local y = torch.Tensor(1, x:size(2), x:size(3)):zero()
|
|
x = iproc.byte2float(x)
|
|
y:add(x[1] * 65.481)
|
|
y:add(x[2] * 128.553)
|
|
y:add(x[3] * 24.966)
|
|
y:add(16.0)
|
|
return y:byte():float()
|
|
end
|
|
|
|
local function RGBMSE(x1, x2)
|
|
x1 = iproc.float2byte(x1):float()
|
|
x2 = iproc.float2byte(x2):float()
|
|
return (x1 - x2):pow(2):mean()
|
|
end
|
|
local function YMSE(x1, x2)
|
|
if opt.range_bug == 1 then
|
|
local x1_2 = rgb2y_matlab(x1)
|
|
local x2_2 = rgb2y_matlab(x2)
|
|
return (x1_2 - x2_2):pow(2):mean()
|
|
else
|
|
local x1_2 = image.rgb2y(x1):mul(255.0)
|
|
local x2_2 = image.rgb2y(x2):mul(255.0)
|
|
return (x1_2 - x2_2):pow(2):mean()
|
|
end
|
|
end
|
|
local function MSE(x1, x2, color)
|
|
if color == "y" then
|
|
return YMSE(x1, x2)
|
|
else
|
|
return RGBMSE(x1, x2)
|
|
end
|
|
end
|
|
local function PSNR(x1, x2, color)
|
|
local mse = MSE(x1, x2, color)
|
|
return 10 * math.log10((255.0 * 255.0) / mse)
|
|
end
|
|
local function transform_jpeg(x, opt)
|
|
for i = 1, opt.jpeg_times do
|
|
jpeg = gm.Image(x, "RGB", "DHW")
|
|
jpeg:format("jpeg")
|
|
jpeg:samplingFactors({1.0, 1.0, 1.0})
|
|
blob, len = jpeg:toBlob(opt.jpeg_quality - (i - 1) * opt.jpeg_quality_down)
|
|
jpeg:fromBlob(blob, len)
|
|
x = jpeg:toTensor("byte", "RGB", "DHW")
|
|
end
|
|
return iproc.byte2float(x)
|
|
end
|
|
local function baseline_scale(x, filter)
|
|
return iproc.scale(x,
|
|
x:size(3) * 2.0,
|
|
x:size(2) * 2.0,
|
|
filter)
|
|
end
|
|
local function transform_scale(x, opt)
|
|
return iproc.scale(x,
|
|
x:size(3) * 0.5,
|
|
x:size(2) * 0.5,
|
|
opt.filter)
|
|
end
|
|
|
|
local function benchmark(opt, x, input_func, model1, model2)
|
|
local model1_mse = 0
|
|
local model2_mse = 0
|
|
local baseline_mse = 0
|
|
local model1_psnr = 0
|
|
local model2_psnr = 0
|
|
local baseline_psnr = 0
|
|
|
|
for i = 1, #x do
|
|
local ground_truth = x[i]
|
|
local input, model1_output, model2_output, baseline_output
|
|
|
|
input = input_func(ground_truth, opt)
|
|
t = sys.clock()
|
|
if input:size(3) == ground_truth:size(3) then
|
|
model1_output = reconstruct.image(model1, input)
|
|
if model2 then
|
|
model2_output = reconstruct.image(model2, input)
|
|
end
|
|
else
|
|
model1_output = reconstruct.scale(model1, 2.0, input)
|
|
if model2 then
|
|
model2_output = reconstruct.scale(model2, 2.0, input)
|
|
end
|
|
baseline_output = baseline_scale(input, opt.filter)
|
|
end
|
|
model1_mse = model1_mse + MSE(ground_truth, model1_output, opt.color)
|
|
model1_psnr = model1_psnr + PSNR(ground_truth, model1_output, opt.color)
|
|
if model2 then
|
|
model2_mse = model2_mse + MSE(ground_truth, model2_output, opt.color)
|
|
model2_psnr = model2_psnr + PSNR(ground_truth, model2_output, opt.color)
|
|
end
|
|
if baseline_output then
|
|
baseline_mse = baseline_mse + MSE(ground_truth, baseline_output, opt.color)
|
|
baseline_psnr = baseline_psnr + PSNR(ground_truth, baseline_output, opt.color)
|
|
end
|
|
if model2 then
|
|
if baseline_output then
|
|
io.stdout:write(
|
|
string.format("%d/%d; baseline_rmse=%f, model1_rmse=%f, model2_rmse=%f, baseline_psnr=%f, model1_psnr=%f, model2_psnr=%f \r",
|
|
i, #x,
|
|
math.sqrt(baseline_mse / i),
|
|
math.sqrt(model1_mse / i), math.sqrt(model2_mse / i),
|
|
baseline_psnr / i,
|
|
model1_psnr / i, model2_psnr / i
|
|
))
|
|
else
|
|
io.stdout:write(
|
|
string.format("%d/%d; model1_rmse=%f, model2_rmse=%f, model1_psnr=%f, model2_psnr=%f \r",
|
|
i, #x,
|
|
math.sqrt(model1_mse / i), math.sqrt(model2_mse / i),
|
|
model1_psnr / i, model2_psnr / i
|
|
))
|
|
end
|
|
else
|
|
if baseline_output then
|
|
io.stdout:write(
|
|
string.format("%d/%d; baseline_rmse=%f, model1_rmse=%f, baseline_psnr=%f, model1_psnr=%f \r",
|
|
i, #x,
|
|
math.sqrt(baseline_mse / i), math.sqrt(model1_mse / i),
|
|
baseline_psnr / i, model1_psnr / i
|
|
))
|
|
else
|
|
io.stdout:write(
|
|
string.format("%d/%d; model1_rmse=%f, model1_psnr=%f \r",
|
|
i, #x,
|
|
math.sqrt(model1_mse / i), model1_psnr / i
|
|
))
|
|
end
|
|
end
|
|
io.stdout:flush()
|
|
end
|
|
io.stdout:write("\n")
|
|
end
|
|
local function load_data(test_dir)
|
|
local test_x = {}
|
|
local files = dir.getfiles(test_dir, "*.*")
|
|
for i = 1, #files do
|
|
table.insert(test_x, iproc.crop_mod4(image_loader.load_float(files[i])))
|
|
xlua.progress(i, #files)
|
|
end
|
|
return test_x
|
|
end
|
|
function load_model(filename)
|
|
return torch.load(filename, "ascii")
|
|
end
|
|
print(opt)
|
|
if opt.method == "scale" then
|
|
local f1 = path.join(opt.model1_dir, "scale2.0x_model.t7")
|
|
local f2 = path.join(opt.model2_dir, "scale2.0x_model.t7")
|
|
local s1, model1 = pcall(load_model, f1)
|
|
local s2, model2 = pcall(load_model, f2)
|
|
if not s1 then
|
|
error("Load error: " .. f1)
|
|
end
|
|
if not s2 then
|
|
model2 = nil
|
|
end
|
|
local test_x = load_data(opt.dir)
|
|
benchmark(opt, test_x, transform_scale, model1, model2)
|
|
elseif opt.method == "noise" then
|
|
local f1 = path.join(opt.model1_dir, string.format("noise%d_model.t7", opt.noise_level))
|
|
local f2 = path.join(opt.model2_dir, string.format("noise%d_model.t7", opt.noise_level))
|
|
local s1, model1 = pcall(load_model, f1)
|
|
local s2, model2 = pcall(load_model, f2)
|
|
if not s1 then
|
|
error("Load error: " .. f1)
|
|
end
|
|
if not s2 then
|
|
model2 = nil
|
|
end
|
|
local test_x = load_data(opt.dir)
|
|
benchmark(opt, test_x, transform_jpeg, model1, model2)
|
|
end
|