change training script
- add AlexNet's color noise (default: false) - add `photo` category for noise level setting
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@ -13,14 +13,29 @@ local function random_half(src, p, min_size)
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return src
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end
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end
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local function color_augment(x)
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local color_scale = torch.Tensor(3):uniform(0.8, 1.2)
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x = x:float():div(255)
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local function pcacov(x)
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local mean = torch.mean(x, 1)
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local xm = x - torch.ger(torch.ones(x:size(1)), mean:squeeze())
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local c = torch.mm(xm:t(), xm)
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c:div(x:size(1) - 1)
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local ce, cv = torch.symeig(c, 'V')
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return ce, cv
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end
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local function color_noise(src)
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local p = 0.1
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src = src:float():div(255)
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local src_t = src:reshape(src:size(1), src:nElement() / src:size(1)):t():contiguous()
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local ce, cv = pcacov(src_t)
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local color_scale = torch.Tensor(3):uniform(1 / (1 + p), 1 + p)
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pca_space = torch.mm(src_t, cv):t():contiguous()
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for i = 1, 3 do
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x[i]:mul(color_scale[i])
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pca_space[i]:mul(color_scale[i])
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end
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x = torch.mm(pca_space:t(), cv:t()):t():contiguous():resizeAs(src)
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x[torch.lt(x, 0.0)] = 0.0
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x[torch.gt(x, 1.0)] = 1.0
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return x:mul(255):byte()
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end
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local function flip_augment(x, y)
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@ -52,7 +67,7 @@ local function flip_augment(x, y)
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end
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local INTERPOLATION_PADDING = 16
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function pairwise_transform.scale(src, scale, size, offset, options)
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options = options or {color_augment = true, random_half = true, rgb = true}
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options = options or {color_noise = false, random_half = true, rgb = true}
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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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@ -74,8 +89,8 @@ function pairwise_transform.scale(src, scale, size, offset, options)
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local downscale_filter = filters[torch.random(1, #filters)]
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y = flip_augment(y)
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if options.color_augment then
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y = color_augment(y)
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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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local x = iproc.scale(y, y:size(3) * down_scale, y:size(2) * down_scale, downscale_filter)
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x = iproc.scale(x, y:size(3), y:size(2))
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@ -94,7 +109,7 @@ function pairwise_transform.scale(src, scale, size, offset, options)
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return x, y
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end
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function pairwise_transform.jpeg_(src, quality, size, offset, options)
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options = options or {color_augment = true, random_half = true, rgb = true}
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options = options or {color_noise = false, random_half = true, rgb = true}
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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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@ -103,8 +118,8 @@ function pairwise_transform.jpeg_(src, quality, size, offset, options)
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local y = src
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local x
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if options.color_augment then
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y = color_augment(y)
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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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x = y
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for i = 1, #quality do
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@ -130,7 +145,8 @@ function pairwise_transform.jpeg_(src, quality, size, offset, options)
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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(src, level, size, offset, options)
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function pairwise_transform.jpeg(src, category, level, size, offset, options)
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if category == "anime_style_art" then
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if level == 1 then
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return pairwise_transform.jpeg_(src, {torch.random(65, 85)},
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size, offset,
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@ -159,6 +175,25 @@ function pairwise_transform.jpeg(src, level, 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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elseif category == "photo" then
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if level == 1 then
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if torch.uniform() > 0.75 then
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return pairwise_transform.jpeg_(src, {},
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size, offset,
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options)
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else
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return pairwise_transform.jpeg_(src, {torch.random(80, 95)},
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size, offset,
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options)
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end
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elseif level == 2 then
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return pairwise_transform.jpeg_(src, {torch.random(70, 85)},
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size, offset,
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options)
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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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function pairwise_transform.jpeg_scale_(src, scale, quality, size, offset, options)
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if options.random_half then
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@ -180,8 +215,8 @@ function pairwise_transform.jpeg_scale_(src, scale, quality, size, offset, optio
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local y = src
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local x
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if options.color_augment then
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y = color_augment(y)
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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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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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@ -212,8 +247,9 @@ function pairwise_transform.jpeg_scale_(src, scale, quality, size, offset, optio
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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, level, size, offset, options)
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options = options or {color_augment = true, random_half = true}
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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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return pairwise_transform.jpeg_scale_(src, scale, {torch.random(65, 85)},
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size, offset, options)
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@ -238,6 +274,24 @@ function pairwise_transform.jpeg_scale(src, scale, level, 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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elseif category == "photo" then
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if level == 1 then
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if torch.uniform() > 0.75 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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@ -248,7 +302,7 @@ local function test_jpeg()
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image.display({image = x, legend = "x:0"})
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for i = 2, 9 do
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local y, x = pairwise_transform.jpeg_(pairwise_transform.random_half(src),
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{i * 10}, 128, 0, {color_augment = false, random_half = true})
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{i * 10}, 128, 0, {color_noise = false, random_half = true})
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image.display({image = y, legend = "y:" .. (i * 10), max=1,min=0})
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image.display({image = x, legend = "x:" .. (i * 10),max=1,min=0})
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--print(x:mean(), y:mean())
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@ -256,10 +310,11 @@ local function test_jpeg()
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end
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local function test_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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for i = 1, 9 do
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local y, x = pairwise_transform.scale(src, 2.0, 128, 7, {color_augment = true, random_half = true, rgb = true})
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local y, x = pairwise_transform.scale(src, 2.0, 128, 7, {color_noise = true, random_half = true, rgb = true})
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image.display({image = y, legend = "y:" .. (i * 10), min = 0, max = 1})
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image.display({image = x, legend = "x:" .. (i * 10), min = 0, max = 1})
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print(y:size(), x:size())
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@ -267,25 +322,35 @@ local function test_scale()
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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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for i = 1, 9 do
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local y, x = pairwise_transform.jpeg_scale(src, 2.0, 1, 128, 7, {color_augment = true, random_half = true})
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local y, x = pairwise_transform.jpeg_scale(src, 2.0, 1, 128, 7, {color_noise = true, random_half = true})
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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, {color_augment = true, random_half = true})
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local y, x = pairwise_transform.jpeg_scale(src, 2.0, 2, 128, 7, {color_noise = true, random_half = true})
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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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local src = loader.load_byte("../images/miku_CC_BY-NC.jpg")
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for i = 1, 10 do
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image.display(color_noise(src))
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end
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end
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--test_scale()
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--test_jpeg()
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--test_jpeg_scale()
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--test_color_noise()
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return pairwise_transform
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@ -21,11 +21,13 @@ cmd:option("-data_dir", "./data", 'data directory')
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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("-noise_level", 1, '(0|1|2)')
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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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cmd:option("-color_noise", 0, 'enable data augmentation using color noise (1|0)')
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cmd:option("-scale", 2.0, 'scale')
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cmd:option("-learning_rate", 0.00025, 'learning rate for adam')
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cmd:option("-random_half", 1, 'enable data augmentation using half resolution image')
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cmd:option("-random_half", 1, 'enable data augmentation using half resolution image (0|1)')
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cmd:option("-crop_size", 128, 'crop size')
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cmd:option("-batch_size", 2, 'mini batch size')
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cmd:option("-epoch", 200, 'epoch')
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@ -53,18 +55,27 @@ end
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if not (settings.scale == math.floor(settings.scale) and settings.scale % 2 == 0) then
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error("scale must be mod-2")
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end
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if not (settings.category == "anime_style_art" or
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settings.category == "photo") then
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error(string.format("unknown category: %s", settings.category))
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end
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if settings.random_half == 1 then
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settings.random_half = true
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else
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settings.random_half = false
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end
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if settings.color_noise == 1 then
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settings.color_noise = true
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else
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settings.color_noise = false
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end
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torch.setnumthreads(settings.core)
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settings.images = string.format("%s/images.t7", settings.data_dir)
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settings.image_list = string.format("%s/image_list.txt", settings.data_dir)
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settings.validation_ratio = 0.1
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settings.validation_crops = 20
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settings.validation_crops = 30
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local srcnn = require './srcnn'
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if (settings.method == "scale" or settings.method == "noise_scale") and settings.scale == 4 then
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16
train.lua
16
train.lua
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@ -65,8 +65,7 @@ local function train()
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local x = torch.load(settings.images)
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local lrd_count = 0
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local train_x, valid_x = split_data(x,
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math.floor(settings.validation_ratio * #x),
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settings.validation_crops)
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math.floor(settings.validation_ratio * #x))
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local test = image_loader.load_float(settings.test)
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local adam_config = {
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learningRate = settings.learning_rate,
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@ -80,28 +79,31 @@ local function train()
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end
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local transformer = function(x, is_validation)
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if is_validation == nil then is_validation = false end
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local color_noise = (not is_validation) and settings.color_noise
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if settings.method == "scale" then
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return pairwise_transform.scale(x,
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settings.scale,
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settings.crop_size, offset,
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{ color_augment = not is_validation,
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{ color_noise = color_noise,
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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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elseif settings.method == "noise" then
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return pairwise_transform.jpeg(x,
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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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{ color_augment = not is_validation,
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{ color_noise = color_noise,
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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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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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{ color_augment = not is_validation,
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{ color_noise = color_noise,
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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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@ -109,7 +111,7 @@ local function train()
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end
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local best_score = 100000.0
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print("# make validation-set")
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local valid_xy = make_validation_set(valid_x, transformer, settings.validation_crop)
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local valid_xy = make_validation_set(valid_x, transformer, settings.validation_crops)
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valid_x = nil
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collectgarbage()
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@ -149,7 +151,7 @@ local function train()
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lrd_count = lrd_count + 1
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if lrd_count > 5 then
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lrd_count = 0
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adam_config.learningRate = adam_config.learningRate * 0.8
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adam_config.learningRate = adam_config.learningRate * 0.9
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print("* learning rate decay: " .. adam_config.learningRate)
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end
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end
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