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change training script

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- add AlexNet's color noise (default: false)
- add `photo` category for noise level setting
This commit is contained in:
nagadomi 2015-07-11 21:57:04 +09:00
parent 99db05a753
commit e3d3a8355c
3 changed files with 140 additions and 62 deletions
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169
lib/pairwise_transform.lua
View file

@ -13,14 +13,29 @@ local function random_half(src, p, min_size)
return src
end
end
local function color_augment(x)
local color_scale = torch.Tensor(3):uniform(0.8, 1.2)
x = x:float():div(255)
local function pcacov(x)
local mean = torch.mean(x, 1)
local xm = x - torch.ger(torch.ones(x:size(1)), mean:squeeze())
local c = torch.mm(xm:t(), xm)
c:div(x:size(1) - 1)
local ce, cv = torch.symeig(c, 'V')
return ce, cv
end
local function color_noise(src)
local p = 0.1
src = src:float():div(255)
local src_t = src:reshape(src:size(1), src:nElement() / src:size(1)):t():contiguous()
local ce, cv = pcacov(src_t)
local color_scale = torch.Tensor(3):uniform(1 / (1 + p), 1 + p)
pca_space = torch.mm(src_t, cv):t():contiguous()
for i = 1, 3 do
x[i]:mul(color_scale[i])
pca_space[i]:mul(color_scale[i])
end
x = torch.mm(pca_space:t(), cv:t()):t():contiguous():resizeAs(src)
x[torch.lt(x, 0.0)] = 0.0
x[torch.gt(x, 1.0)] = 1.0
return x:mul(255):byte()
end
local function flip_augment(x, y)
@ -52,7 +67,7 @@ local function flip_augment(x, y)
end
local INTERPOLATION_PADDING = 16
function pairwise_transform.scale(src, scale, size, offset, options)
options = options or {color_augment = true, random_half = true, rgb = true}
options = options or {color_noise = false, random_half = true, rgb = true}
if options.random_half then
src = random_half(src)
end
@ -74,8 +89,8 @@ function pairwise_transform.scale(src, scale, size, offset, options)
local downscale_filter = filters[torch.random(1, #filters)]
y = flip_augment(y)
if options.color_augment then
y = color_augment(y)
if options.color_noise then
y = color_noise(y)
end
local x = iproc.scale(y, y:size(3) * down_scale, y:size(2) * down_scale, downscale_filter)
x = iproc.scale(x, y:size(3), y:size(2))
@ -94,7 +109,7 @@ function pairwise_transform.scale(src, scale, size, offset, options)
return x, y
end
function pairwise_transform.jpeg_(src, quality, size, offset, options)
options = options or {color_augment = true, random_half = true, rgb = true}
options = options or {color_noise = false, random_half = true, rgb = true}
if options.random_half then
src = random_half(src)
end
@ -103,8 +118,8 @@ function pairwise_transform.jpeg_(src, quality, size, offset, options)
local y = src
local x
if options.color_augment then
y = color_augment(y)
if options.color_noise then
y = color_noise(y)
end
x = y
for i = 1, #quality do
@ -130,34 +145,54 @@ function pairwise_transform.jpeg_(src, quality, size, offset, options)
return x, image.crop(y, offset, offset, size - offset, size - offset)
end
function pairwise_transform.jpeg(src, level, size, offset, options)
if level == 1 then
return pairwise_transform.jpeg_(src, {torch.random(65, 85)},
size, offset,
options)
elseif level == 2 then
local r = torch.uniform()
if r > 0.6 then
return pairwise_transform.jpeg_(src, {torch.random(27, 70)},
function pairwise_transform.jpeg(src, category, level, size, offset, options)
if category == "anime_style_art" then
if level == 1 then
return pairwise_transform.jpeg_(src, {torch.random(65, 85)},
size, offset,
options)
elseif r > 0.3 then
local quality1 = torch.random(37, 70)
local quality2 = quality1 - torch.random(5, 10)
return pairwise_transform.jpeg_(src, {quality1, quality2},
elseif level == 2 then
local r = torch.uniform()
if r > 0.6 then
return pairwise_transform.jpeg_(src, {torch.random(27, 70)},
size, offset,
options)
elseif r > 0.3 then
local quality1 = torch.random(37, 70)
local quality2 = quality1 - torch.random(5, 10)
return pairwise_transform.jpeg_(src, {quality1, quality2},
size, offset,
options)
else
local quality1 = torch.random(52, 70)
return pairwise_transform.jpeg_(src,
{quality1,
quality1 - torch.random(5, 15),
quality1 - torch.random(15, 25)},
size, offset,
options)
end
else
local quality1 = torch.random(52, 70)
return pairwise_transform.jpeg_(src,
{quality1,
quality1 - torch.random(5, 15),
quality1 - torch.random(15, 25)},
error("unknown noise level: " .. level)
end
elseif category == "photo" then
if level == 1 then
if torch.uniform() > 0.75 then
return pairwise_transform.jpeg_(src, {},
size, offset,
options)
else
return pairwise_transform.jpeg_(src, {torch.random(80, 95)},
size, offset,
options)
end
elseif level == 2 then
return pairwise_transform.jpeg_(src, {torch.random(70, 85)},
size, offset,
options)
end
else
error("unknown noise level: " .. level)
error("unknown category: " .. category)
end
end
function pairwise_transform.jpeg_scale_(src, scale, quality, size, offset, options)
@ -180,8 +215,8 @@ function pairwise_transform.jpeg_scale_(src, scale, quality, size, offset, optio
local y = src
local x
if options.color_augment then
y = color_augment(y)
if options.color_noise then
y = color_noise(y)
end
x = y
x = iproc.scale(x, y:size(3) * down_scale, y:size(2) * down_scale, downscale_filter)
@ -212,31 +247,50 @@ function pairwise_transform.jpeg_scale_(src, scale, quality, size, offset, optio
return x, image.crop(y, offset, offset, size - offset, size - offset)
end
function pairwise_transform.jpeg_scale(src, scale, level, size, offset, options)
options = options or {color_augment = true, random_half = true}
if level == 1 then
return pairwise_transform.jpeg_scale_(src, scale, {torch.random(65, 85)},
size, offset, options)
elseif level == 2 then
local r = torch.uniform()
if r > 0.6 then
return pairwise_transform.jpeg_scale_(src, scale, {torch.random(27, 70)},
function pairwise_transform.jpeg_scale(src, scale, category, level, size, offset, options)
options = options or {color_noise = false, random_half = true}
if category == "anime_style_art" then
if level == 1 then
return pairwise_transform.jpeg_scale_(src, scale, {torch.random(65, 85)},
size, offset, options)
elseif r > 0.3 then
local quality1 = torch.random(37, 70)
local quality2 = quality1 - torch.random(5, 10)
return pairwise_transform.jpeg_scale_(src, scale, {quality1, quality2},
elseif level == 2 then
local r = torch.uniform()
if r > 0.6 then
return pairwise_transform.jpeg_scale_(src, scale, {torch.random(27, 70)},
size, offset, options)
else
local quality1 = torch.random(52, 70)
elseif r > 0.3 then
local quality1 = torch.random(37, 70)
local quality2 = quality1 - torch.random(5, 10)
return pairwise_transform.jpeg_scale_(src, scale, {quality1, quality2},
size, offset, options)
else
local quality1 = torch.random(52, 70)
return pairwise_transform.jpeg_scale_(src, scale,
{quality1,
quality1 - torch.random(5, 15),
quality1 - torch.random(15, 25)},
size, offset, options)
end
else
error("unknown noise level: " .. level)
end
elseif category == "photo" then
if level == 1 then
if torch.uniform() > 0.75 then
return pairwise_transform.jpeg_scale_(src, scale, {},
size, offset, options)
else
return pairwise_transform.jpeg_scale_(src, scale, {torch.random(80, 95)},
size, offset, options)
end
elseif level == 2 then
return pairwise_transform.jpeg_scale_(src, scale, {torch.random(70, 85)},
size, offset, options)
else
error("unknown noise level: " .. level)
end
else
error("unknown noise level: " .. level)
error("unknown category: " .. category)
end
end
@ -248,7 +302,7 @@ local function test_jpeg()
image.display({image = x, legend = "x:0"})
for i = 2, 9 do
local y, x = pairwise_transform.jpeg_(pairwise_transform.random_half(src),
{i * 10}, 128, 0, {color_augment = false, random_half = true})
{i * 10}, 128, 0, {color_noise = false, random_half = true})
image.display({image = y, legend = "y:" .. (i * 10), max=1,min=0})
image.display({image = x, legend = "x:" .. (i * 10),max=1,min=0})
--print(x:mean(), y:mean())
@ -256,10 +310,11 @@ local function test_jpeg()
end
local function test_scale()
torch.setdefaulttensortype('torch.FloatTensor')
local loader = require './image_loader'
local src = loader.load_byte("../images/miku_CC_BY-NC.jpg")
local src = loader.load_byte("../images/miku_CC_BY-NC.jpg")
for i = 1, 9 do
local y, x = pairwise_transform.scale(src, 2.0, 128, 7, {color_augment = true, random_half = true, rgb = true})
local y, x = pairwise_transform.scale(src, 2.0, 128, 7, {color_noise = true, random_half = true, rgb = true})
image.display({image = y, legend = "y:" .. (i * 10), min = 0, max = 1})
image.display({image = x, legend = "x:" .. (i * 10), min = 0, max = 1})
print(y:size(), x:size())
@ -267,25 +322,35 @@ local function test_scale()
end
end
local function test_jpeg_scale()
torch.setdefaulttensortype('torch.FloatTensor')
local loader = require './image_loader'
local src = loader.load_byte("../images/miku_CC_BY-NC.jpg")
for i = 1, 9 do
local y, x = pairwise_transform.jpeg_scale(src, 2.0, 1, 128, 7, {color_augment = true, random_half = true})
local y, x = pairwise_transform.jpeg_scale(src, 2.0, 1, 128, 7, {color_noise = true, random_half = true})
image.display({image = y, legend = "y1:" .. (i * 10), min = 0, max = 1})
image.display({image = x, legend = "x1:" .. (i * 10), min = 0, max = 1})
print(y:size(), x:size())
--print(x:mean(), y:mean())
end
for i = 1, 9 do
local y, x = pairwise_transform.jpeg_scale(src, 2.0, 2, 128, 7, {color_augment = true, random_half = true})
local y, x = pairwise_transform.jpeg_scale(src, 2.0, 2, 128, 7, {color_noise = true, random_half = true})
image.display({image = y, legend = "y2:" .. (i * 10), min = 0, max = 1})
image.display({image = x, legend = "x2:" .. (i * 10), min = 0, max = 1})
print(y:size(), x:size())
--print(x:mean(), y:mean())
end
end
local function test_color_noise()
torch.setdefaulttensortype('torch.FloatTensor')
local loader = require './image_loader'
local src = loader.load_byte("../images/miku_CC_BY-NC.jpg")
for i = 1, 10 do
image.display(color_noise(src))
end
end
--test_scale()
--test_jpeg()
--test_jpeg_scale()
--test_color_noise()
return pairwise_transform

17
lib/settings.lua
View file

@ -21,11 +21,13 @@ cmd:option("-data_dir", "./data", 'data directory')
cmd:option("-test", "images/miku_small.png", 'test image file')
cmd:option("-model_dir", "./models", 'model directory')
cmd:option("-method", "scale", '(noise|scale|noise_scale)')
cmd:option("-noise_level", 1, '(0|1|2)')
cmd:option("-noise_level", 1, '(1|2)')
cmd:option("-category", "anime_style_art", '(anime_style_art|photo)')
cmd:option("-color", 'rgb', '(y|rgb)')
cmd:option("-color_noise", 0, 'enable data augmentation using color noise (1|0)')
cmd:option("-scale", 2.0, 'scale')
cmd:option("-learning_rate", 0.00025, 'learning rate for adam')
cmd:option("-random_half", 1, 'enable data augmentation using half resolution image')
cmd:option("-random_half", 1, 'enable data augmentation using half resolution image (0|1)')
cmd:option("-crop_size", 128, 'crop size')
cmd:option("-batch_size", 2, 'mini batch size')
cmd:option("-epoch", 200, 'epoch')
@ -53,18 +55,27 @@ end
if not (settings.scale == math.floor(settings.scale) and settings.scale % 2 == 0) then
error("scale must be mod-2")
end
if not (settings.category == "anime_style_art" or
settings.category == "photo") then
error(string.format("unknown category: %s", settings.category))
end
if settings.random_half == 1 then
settings.random_half = true
else
settings.random_half = false
end
if settings.color_noise == 1 then
settings.color_noise = true
else
settings.color_noise = false
end
torch.setnumthreads(settings.core)
settings.images = string.format("%s/images.t7", settings.data_dir)
settings.image_list = string.format("%s/image_list.txt", settings.data_dir)
settings.validation_ratio = 0.1
settings.validation_crops = 20
settings.validation_crops = 30
local srcnn = require './srcnn'
if (settings.method == "scale" or settings.method == "noise_scale") and settings.scale == 4 then

16
train.lua
View file

@ -65,8 +65,7 @@ local function train()
local x = torch.load(settings.images)
local lrd_count = 0
local train_x, valid_x = split_data(x,
math.floor(settings.validation_ratio * #x),
settings.validation_crops)
math.floor(settings.validation_ratio * #x))
local test = image_loader.load_float(settings.test)
local adam_config = {
learningRate = settings.learning_rate,
@ -80,28 +79,31 @@ local function train()
end
local transformer = function(x, is_validation)
if is_validation == nil then is_validation = false end
local color_noise = (not is_validation) and settings.color_noise
if settings.method == "scale" then
return pairwise_transform.scale(x,
settings.scale,
settings.crop_size, offset,
{ color_augment = not is_validation,
{ color_noise = color_noise,
random_half = settings.random_half,
rgb = (settings.color == "rgb")
})
elseif settings.method == "noise" then
return pairwise_transform.jpeg(x,
settings.category,
settings.noise_level,
settings.crop_size, offset,
{ color_augment = not is_validation,
{ color_noise = color_noise,
random_half = settings.random_half,
rgb = (settings.color == "rgb")
})
elseif settings.method == "noise_scale" then
return pairwise_transform.jpeg_scale(x,
settings.scale,
settings.category,
settings.noise_level,
settings.crop_size, offset,
{ color_augment = not is_validation,
{ color_noise = color_noise,
random_half = settings.random_half,
rgb = (settings.color == "rgb")
})
@ -109,7 +111,7 @@ local function train()
end
local best_score = 100000.0
print("# make validation-set")
local valid_xy = make_validation_set(valid_x, transformer, settings.validation_crop)
local valid_xy = make_validation_set(valid_x, transformer, settings.validation_crops)
valid_x = nil
collectgarbage()
@ -149,7 +151,7 @@ local function train()
lrd_count = lrd_count + 1
if lrd_count > 5 then
lrd_count = 0
adam_config.learningRate = adam_config.learningRate * 0.8
adam_config.learningRate = adam_config.learningRate * 0.9
print("* learning rate decay: " .. adam_config.learningRate)
end
end