Add support for method=noise_scale to tools/benchmark.lua
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nagadomi
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c16d0a07a2
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83188c5ab7
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@ -17,7 +17,7 @@ cmd:text("Options:")
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cmd:option("-dir", "./data/test", 'test image directory')
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cmd:option("-model1_dir", "./models/anime_style_art_rgb", 'model1 directory')
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cmd:option("-model2_dir", "", 'model2 directory (optional)')
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cmd:option("-method", "scale", '(scale|noise)')
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cmd:option("-method", "scale", '(scale|noise|noise_scale)')
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cmd:option("-filter", "Catrom", "downscaling filter (Box|Lanczos|Catrom(Bicubic))")
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cmd:option("-resize_blur", 1.0, 'blur parameter for resize')
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cmd:option("-color", "y", '(rgb|y)')
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@ -129,6 +129,22 @@ local function transform_scale(x, opt)
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opt.filter, opt.resize_blur)
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end
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local function transform_scale_jpeg(x, opt)
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x = 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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opt.filter, opt.resize_blur)
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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 iproc.byte2float(x)
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end
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local function benchmark(opt, x, input_func, model1, model2)
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local model1_mse = 0
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local model2_mse = 0
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@ -157,15 +173,45 @@ local function benchmark(opt, x, input_func, model1, model2)
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input = input_func(ground_truth, opt)
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t = sys.clock()
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if input:size(3) == ground_truth:size(3) then
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if opt.method == "scale" then
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model1_output = scale_f(model1, 2.0, input, opt.crop_size, opt.batch_size)
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if model2 then
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model2_output = scale_f(model2, 2.0, input, opt.crop_size, opt.batch_size)
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end
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baseline_output = baseline_scale(input, opt.baseline_filter)
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elseif opt.method == "noise" then
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model1_output = image_f(model1, input, opt.crop_size, opt.batch_size)
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if model2 then
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model2_output = image_f(model2, input, opt.crop_size, opt.batch_size)
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end
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else
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model1_output = scale_f(model1, 2.0, input, opt.crop_size, opt.batch_size)
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baseline_output = input
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elseif opt.method == "noise_scale" then
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if model1.noise_scale_model then
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model1_output = scale_f(model1.noise_scale_model, 2.0,
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input, opt.crop_size, opt.batch_size)
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else
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if model1.noise_model then
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model1_output = image_f(model1.noise_model, input, opt.crop_size, opt.batch_size)
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else
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model1_output = input
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end
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model1_output = scale_f(model1.scale_model, 2.0, model1_output,
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opt.crop_size, opt.batch_size)
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end
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if model2 then
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model2_output = scale_f(model2, 2.0, input, opt.crop_size, opt.batch_size)
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if model2.noise_scale_model then
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model2_output = scale_f(model2.noise_scale_model, 2.0,
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input, opt.crop_size, opt.batch_size)
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else
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if model2.noise_model then
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model2_output = image_f(model2.noise_model, input,
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opt.crop_size, opt.batch_size)
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else
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model2_output = input
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end
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model2_output = scale_f(model2.scale_model, 2.0, model2_output,
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opt.crop_size, opt.batch_size)
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end
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end
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baseline_output = baseline_scale(input, opt.baseline_filter)
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end
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@ -271,9 +317,36 @@ end
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function load_model(filename)
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return torch.load(filename, "ascii")
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end
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function load_noise_scale_model(model_dir, noise_level)
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local f = path.join(model_dir, string.format("noise%d_scale2.0x_model.t7", opt.noise_level))
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local s1, noise_scale = pcall(load_model, f)
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local model = {}
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if not s1 then
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f = path.join(model_dir, string.format("noise%d_model.t7", opt.noise_level))
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local noise
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s1, noise = pcall(load_model, f)
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if not s1 then
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model.noise_model = nil
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print(model_dir .. "'s noise model is not found. benchmark will use only scale model.")
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else
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model.noise_model = noise
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end
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f = path.join(model_dir, "scale2.0x_model.t7")
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local scale
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s1, scale = pcall(load_model, f)
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if not s1 then
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return nil
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end
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model.scale_model = scale
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else
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model.noise_scale_model = noise_scale
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end
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return model
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end
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if opt.show_progress then
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print(opt)
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end
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if opt.method == "scale" then
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local f1 = path.join(opt.model1_dir, "scale2.0x_model.t7")
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local f2 = path.join(opt.model2_dir, "scale2.0x_model.t7")
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@ -300,4 +373,12 @@ elseif opt.method == "noise" then
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end
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local test_x = load_data(opt.dir)
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benchmark(opt, test_x, transform_jpeg, model1, model2)
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elseif opt.method == "noise_scale" then
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local model2 = nil
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local model1 = load_noise_scale_model(opt.model1_dir, opt.noise_level)
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if opt.model2_dir:len() > 0 then
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model2 = load_noise_scale_model(opt.model2_dir, opt.noise_level)
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end
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local test_x = load_data(opt.dir)
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benchmark(opt, test_x, transform_scale_jpeg, model1, model2)
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end
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