490eb33a6b
Huber loss is less sensitive to outliers(i.e. noise) in data than the squared error loss.
206 lines
6.5 KiB
Lua
206 lines
6.5 KiB
Lua
local __FILE__ = (function() return string.gsub(debug.getinfo(2, 'S').source, "^@", "") end)()
|
|
package.path = path.join(path.dirname(__FILE__), "lib", "?.lua;") .. package.path
|
|
require 'optim'
|
|
require 'xlua'
|
|
require 'pl'
|
|
|
|
require 'w2nn'
|
|
local settings = require 'settings'
|
|
local srcnn = require 'srcnn'
|
|
local minibatch_adam = require 'minibatch_adam'
|
|
local iproc = require 'iproc'
|
|
local reconstruct = require 'reconstruct'
|
|
local compression = require 'compression'
|
|
local pairwise_transform = require 'pairwise_transform'
|
|
local image_loader = require 'image_loader'
|
|
|
|
local function save_test_scale(model, rgb, file)
|
|
local up = reconstruct.scale(model, settings.scale, rgb)
|
|
image.save(file, up)
|
|
end
|
|
local function save_test_jpeg(model, rgb, file)
|
|
local im, count = reconstruct.image(model, rgb)
|
|
image.save(file, im)
|
|
end
|
|
local function split_data(x, test_size)
|
|
local index = torch.randperm(#x)
|
|
local train_size = #x - test_size
|
|
local train_x = {}
|
|
local valid_x = {}
|
|
for i = 1, train_size do
|
|
train_x[i] = x[index[i]]
|
|
end
|
|
for i = 1, test_size do
|
|
valid_x[i] = x[index[train_size + i]]
|
|
end
|
|
return train_x, valid_x
|
|
end
|
|
local function make_validation_set(x, transformer, n, batch_size)
|
|
n = n or 4
|
|
local data = {}
|
|
for i = 1, #x do
|
|
for k = 1, math.max(n / batch_size, 1) do
|
|
local xy = transformer(x[i], true, batch_size)
|
|
local tx = torch.Tensor(batch_size, xy[1][1]:size(1), xy[1][1]:size(2), xy[1][1]:size(3))
|
|
local ty = torch.Tensor(batch_size, xy[1][2]:size(1), xy[1][2]:size(2), xy[1][2]:size(3))
|
|
for j = 1, #xy do
|
|
tx[j]:copy(xy[j][1])
|
|
ty[j]:copy(xy[j][2])
|
|
end
|
|
table.insert(data, {x = tx, y = ty})
|
|
end
|
|
xlua.progress(i, #x)
|
|
collectgarbage()
|
|
end
|
|
return data
|
|
end
|
|
local function validate(model, criterion, data)
|
|
local loss = 0
|
|
for i = 1, #data do
|
|
local z = model:forward(data[i].x:cuda())
|
|
loss = loss + criterion:forward(z, data[i].y:cuda())
|
|
if i % 100 == 0 then
|
|
xlua.progress(i, #data)
|
|
collectgarbage()
|
|
end
|
|
end
|
|
xlua.progress(#data, #data)
|
|
return loss / #data
|
|
end
|
|
|
|
local function create_criterion(model)
|
|
if reconstruct.is_rgb(model) then
|
|
local offset = reconstruct.offset_size(model)
|
|
local output_w = settings.crop_size - offset * 2
|
|
local weight = torch.Tensor(3, output_w * output_w)
|
|
weight[1]:fill(0.29891 * 3) -- R
|
|
weight[2]:fill(0.58661 * 3) -- G
|
|
weight[3]:fill(0.11448 * 3) -- B
|
|
return w2nn.WeightedHuberCriterion(weight, 0.1):cuda()
|
|
else
|
|
return nn.MSECriterion():cuda()
|
|
end
|
|
end
|
|
local function transformer(x, is_validation, n, offset)
|
|
x = compression.decompress(x)
|
|
n = n or settings.batch_size;
|
|
if is_validation == nil then is_validation = false end
|
|
local color_noise = nil
|
|
local overlay = nil
|
|
local active_cropping_ratio = nil
|
|
local active_cropping_tries = nil
|
|
|
|
if is_validation then
|
|
active_cropping_rate = 0.0
|
|
active_cropping_tries = 0
|
|
color_noise = false
|
|
overlay = false
|
|
else
|
|
active_cropping_rate = settings.active_cropping_rate
|
|
active_cropping_tries = settings.active_cropping_tries
|
|
color_noise = settings.color_noise
|
|
overlay = settings.overlay
|
|
end
|
|
|
|
if settings.method == "scale" then
|
|
return pairwise_transform.scale(x,
|
|
settings.scale,
|
|
settings.crop_size, offset,
|
|
n,
|
|
{ color_noise = color_noise,
|
|
overlay = overlay,
|
|
random_half = settings.random_half,
|
|
active_cropping_rate = active_cropping_rate,
|
|
active_cropping_tries = active_cropping_tries,
|
|
rgb = (settings.color == "rgb")
|
|
})
|
|
elseif settings.method == "noise" then
|
|
return pairwise_transform.jpeg(x,
|
|
settings.category,
|
|
settings.noise_level,
|
|
settings.crop_size, offset,
|
|
n,
|
|
{ color_noise = color_noise,
|
|
overlay = overlay,
|
|
active_cropping_rate = active_cropping_rate,
|
|
active_cropping_tries = active_cropping_tries,
|
|
random_half = settings.random_half,
|
|
jpeg_sampling_factors = settings.jpeg_sampling_factors,
|
|
rgb = (settings.color == "rgb")
|
|
})
|
|
end
|
|
end
|
|
|
|
local function train()
|
|
local model = srcnn.create(settings.method, settings.backend, settings.color)
|
|
local offset = reconstruct.offset_size(model)
|
|
local pairwise_func = function(x, is_validation, n)
|
|
return transformer(x, is_validation, n, offset)
|
|
end
|
|
local criterion = create_criterion(model)
|
|
local x = torch.load(settings.images)
|
|
local lrd_count = 0
|
|
local train_x, valid_x = split_data(x, math.floor(settings.validation_ratio * #x))
|
|
local adam_config = {
|
|
learningRate = settings.learning_rate,
|
|
xBatchSize = settings.batch_size,
|
|
}
|
|
local ch = nil
|
|
if settings.color == "y" then
|
|
ch = 1
|
|
elseif settings.color == "rgb" then
|
|
ch = 3
|
|
end
|
|
local best_score = 100000.0
|
|
print("# make validation-set")
|
|
local valid_xy = make_validation_set(valid_x, pairwise_func,
|
|
settings.validation_crops,
|
|
settings.batch_size)
|
|
valid_x = nil
|
|
|
|
collectgarbage()
|
|
model:cuda()
|
|
print("load .. " .. #train_x)
|
|
for epoch = 1, settings.epoch do
|
|
model:training()
|
|
print("# " .. epoch)
|
|
print(minibatch_adam(model, criterion, train_x, adam_config,
|
|
pairwise_func,
|
|
{ch, settings.crop_size, settings.crop_size},
|
|
{ch, settings.crop_size - offset * 2, settings.crop_size - offset * 2}
|
|
))
|
|
model:evaluate()
|
|
print("# validation")
|
|
local score = validate(model, criterion, valid_xy)
|
|
if score < best_score then
|
|
local test_image = image_loader.load_float(settings.test) -- reload
|
|
lrd_count = 0
|
|
best_score = score
|
|
print("* update best model")
|
|
torch.save(settings.model_file, model)
|
|
if settings.method == "noise" then
|
|
local log = path.join(settings.model_dir,
|
|
("noise%d_best.png"):format(settings.noise_level))
|
|
save_test_jpeg(model, test_image, log)
|
|
elseif settings.method == "scale" then
|
|
local log = path.join(settings.model_dir,
|
|
("scale%.1f_best.png"):format(settings.scale))
|
|
save_test_scale(model, test_image, log)
|
|
end
|
|
else
|
|
lrd_count = lrd_count + 1
|
|
if lrd_count > 5 then
|
|
lrd_count = 0
|
|
adam_config.learningRate = adam_config.learningRate * 0.9
|
|
print("* learning rate decay: " .. adam_config.learningRate)
|
|
end
|
|
end
|
|
print("current: " .. score .. ", best: " .. best_score)
|
|
collectgarbage()
|
|
end
|
|
end
|
|
torch.manualSeed(settings.seed)
|
|
cutorch.manualSeed(settings.seed)
|
|
print(settings)
|
|
train()
|