remove noise_scale training
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nagadomi
parent
3abc5a03e3
commit
da786e15ba
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@ -276,125 +276,6 @@ function pairwise_transform.jpeg(src, category, level, size, offset, n, options)
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error("unknown category: " .. category)
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end
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end
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function pairwise_transform.jpeg_scale_(src, scale, quality, size, offset, options)
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if options.random_half then
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src = random_half(src)
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end
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src = crop_if_large(src, math.max(size * 4, 512))
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local down_scale = 1.0 / scale
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local filters = {
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"Box", -- 0.012756949974688
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"Blackman", -- 0.013191924552285
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--"Cartom", -- 0.013753536746706
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--"Hanning", -- 0.013761314529647
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--"Hermite", -- 0.013850225205266
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"SincFast", -- 0.014095824314306
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"Jinc", -- 0.014244299255442
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}
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local downscale_filter = filters[torch.random(1, #filters)]
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local yi = torch.random(INTERPOLATION_PADDING, src:size(2) - size - INTERPOLATION_PADDING)
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local xi = torch.random(INTERPOLATION_PADDING, src:size(3) - size - INTERPOLATION_PADDING)
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local y = src
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local x
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if options.color_noise then
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y = color_noise(y)
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end
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if options.overlay then
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y = overlay_augment(y)
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end
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x = y
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x = iproc.scale(x, y:size(3) * down_scale, y:size(2) * down_scale, downscale_filter)
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for i = 1, #quality do
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x = gm.Image(x, "RGB", "DHW")
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x:format("jpeg")
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if options.jpeg_sampling_factors == 444 then
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x:samplingFactors({1.0, 1.0, 1.0})
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else -- 422
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x:samplingFactors({2.0, 1.0, 1.0})
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end
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local blob, len = x:toBlob(quality[i])
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x:fromBlob(blob, len)
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x = x:toTensor("byte", "RGB", "DHW")
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end
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x = iproc.scale(x, y:size(3), y:size(2))
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y = image.crop(y,
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xi, yi,
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xi + size, yi + size)
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x = image.crop(x,
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xi, yi,
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xi + size, yi + size)
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x = x:float():div(255)
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y = y:float():div(255)
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x, y = flip_augment(x, y)
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if options.rgb then
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else
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y = image.rgb2yuv(y)[1]:reshape(1, y:size(2), y:size(3))
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x = image.rgb2yuv(x)[1]:reshape(1, x:size(2), x:size(3))
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end
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return x, image.crop(y, offset, offset, size - offset, size - offset)
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end
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function pairwise_transform.jpeg_scale(src, scale, category, level, size, offset, options)
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options = options or {color_noise = false, random_half = true}
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if category == "anime_style_art" then
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if level == 1 then
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if torch.uniform() > 0.7 then
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return pairwise_transform.jpeg_scale_(src, scale, {},
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size, offset, options)
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else
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return pairwise_transform.jpeg_scale_(src, scale, {torch.random(65, 85)},
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size, offset, options)
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end
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elseif level == 2 then
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if torch.uniform() > 0.7 then
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return pairwise_transform.jpeg_scale_(src, scale, {},
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size, offset, options)
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else
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local r = torch.uniform()
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if r > 0.6 then
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return pairwise_transform.jpeg_scale_(src, scale, {torch.random(27, 70)},
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size, offset, options)
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elseif r > 0.3 then
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local quality1 = torch.random(37, 70)
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local quality2 = quality1 - torch.random(5, 10)
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return pairwise_transform.jpeg_scale_(src, scale, {quality1, quality2},
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size, offset, options)
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else
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local quality1 = torch.random(52, 70)
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local quality2 = quality1 - torch.random(5, 15)
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local quality3 = quality1 - torch.random(15, 25)
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return pairwise_transform.jpeg_scale_(src, scale,
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{quality1, quality2, quality3 },
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size, offset, options)
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end
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end
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else
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error("unknown noise level: " .. level)
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end
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elseif category == "photo" then
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if level == 1 then
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if torch.uniform() > 0.7 then
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return pairwise_transform.jpeg_scale_(src, scale, {},
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size, offset, options)
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else
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return pairwise_transform.jpeg_scale_(src, scale, {torch.random(80, 95)},
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size, offset, options)
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end
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elseif level == 2 then
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return pairwise_transform.jpeg_scale_(src, scale, {torch.random(70, 85)},
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size, offset, options)
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else
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error("unknown noise level: " .. level)
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end
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else
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error("unknown category: " .. category)
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end
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end
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local function test_jpeg()
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local loader = require './image_loader'
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local src = loader.load_byte("../images/miku_CC_BY-NC.jpg")
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@ -428,31 +309,6 @@ local function test_scale()
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--print(x:mean(), y:mean())
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end
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end
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local function test_jpeg_scale()
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torch.setdefaulttensortype('torch.FloatTensor')
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local loader = require './image_loader'
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local src = loader.load_byte("../images/miku_CC_BY-NC.jpg")
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local options = {color_noise = true,
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random_half = true,
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overlay = true,
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active_cropping_ratio = 0.5,
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active_cropping_times = 10
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}
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for i = 1, 9 do
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local y, x = pairwise_transform.jpeg_scale(src, 2.0, 1, 128, 7, options)
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image.display({image = y, legend = "y1:" .. (i * 10), min = 0, max = 1})
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image.display({image = x, legend = "x1:" .. (i * 10), min = 0, max = 1})
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print(y:size(), x:size())
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--print(x:mean(), y:mean())
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end
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for i = 1, 9 do
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local y, x = pairwise_transform.jpeg_scale(src, 2.0, 2, 128, 7, options)
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image.display({image = y, legend = "y2:" .. (i * 10), min = 0, max = 1})
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image.display({image = x, legend = "x2:" .. (i * 10), min = 0, max = 1})
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print(y:size(), x:size())
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--print(x:mean(), y:mean())
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end
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end
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local function test_color_noise()
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torch.setdefaulttensortype('torch.FloatTensor')
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local loader = require './image_loader'
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@ -15,14 +15,14 @@ local settings = {}
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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("waifu2x-training")
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cmd:text("Options:")
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cmd:option("-seed", 11, 'fixed input seed')
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cmd:option("-data_dir", "./data", 'data directory')
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-- cmd:option("-backend", "cunn", '(cunn|cudnn)') -- cudnn is slow than cunn
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-- cmd:option("-backend", "cunn", '(cunn|cudnn)') -- cudnn is slower than cunn
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cmd:option("-test", "images/miku_small.png", 'test image file')
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cmd:option("-model_dir", "./models", 'model directory')
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cmd:option("-method", "scale", '(noise|scale|noise_scale)')
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cmd:option("-method", "scale", '(noise|scale)')
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cmd:option("-noise_level", 1, '(1|2)')
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cmd:option("-category", "anime_style_art", '(anime_style_art|photo)')
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cmd:option("-color", 'rgb', '(y|rgb)')
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18
train.lua
18
train.lua
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@ -126,19 +126,6 @@ local function transformer(x, is_validation, n, offset)
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jpeg_sampling_factors = settings.jpeg_sampling_factors,
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rgb = (settings.color == "rgb")
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})
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elseif settings.method == "noise_scale" then
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return pairwise_transform.jpeg_scale(x,
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settings.scale,
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settings.category,
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settings.noise_level,
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settings.crop_size, offset,
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n,
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{ color_noise = color_noise,
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overlay = overlay,
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jpeg_sampling_factors = settings.jpeg_sampling_factors,
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random_half = settings.random_half,
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rgb = (settings.color == "rgb")
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})
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end
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end
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@ -195,11 +182,6 @@ local function train()
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local log = path.join(settings.model_dir,
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("scale%.1f_best.png"):format(settings.scale))
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save_test_scale(model, test_image, log)
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elseif settings.method == "noise_scale" then
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local log = path.join(settings.model_dir,
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("noise%d_scale%.1f_best.png"):format(settings.noise_level,
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settings.scale))
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save_test_scale(model, test_image, log)
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end
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else
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lrd_count = lrd_count + 1
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