461 lines
16 KiB
Lua
461 lines
16 KiB
Lua
require 'pl'
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local __FILE__ = (function() return string.gsub(debug.getinfo(2, 'S').source, "^@", "") end)()
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package.path = path.join(path.dirname(__FILE__), "lib", "?.lua;") .. package.path
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require 'optim'
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require 'xlua'
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require 'w2nn'
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local settings = require 'settings'
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local srcnn = require 'srcnn'
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local minibatch_adam = require 'minibatch_adam'
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local iproc = require 'iproc'
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local reconstruct = require 'reconstruct'
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local compression = require 'compression'
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local pairwise_transform = require 'pairwise_transform'
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local image_loader = require 'image_loader'
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local function save_test_scale(model, rgb, file)
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local up = reconstruct.scale(model, settings.scale, rgb)
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image.save(file, up)
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end
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local function save_test_jpeg(model, rgb, file)
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local im, count = reconstruct.image(model, rgb)
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image.save(file, im)
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end
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local function save_test_user(model, rgb, file)
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if settings.scale == 1 then
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save_test_jpeg(model, rgb, file)
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else
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save_test_scale(model, rgb, file)
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end
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end
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local function split_data(x, test_size)
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local index = torch.randperm(#x)
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local train_size = #x - test_size
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local train_x = {}
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local valid_x = {}
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for i = 1, train_size do
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train_x[i] = x[index[i]]
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end
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for i = 1, test_size do
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valid_x[i] = x[index[train_size + i]]
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end
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return train_x, valid_x
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end
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local function make_validation_set(x, transformer, n, patches)
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n = n or 4
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local validation_patches = math.min(16, patches or 16)
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local data = {}
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for i = 1, #x do
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for k = 1, math.max(n / validation_patches, 1) do
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local xy = transformer(x[i], true, validation_patches)
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for j = 1, #xy do
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table.insert(data, {x = xy[j][1], y = xy[j][2]})
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end
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end
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xlua.progress(i, #x)
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collectgarbage()
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end
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local new_data = {}
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local perm = torch.randperm(#data)
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for i = 1, perm:size(1) do
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new_data[i] = data[perm[i]]
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end
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data = new_data
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return data
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end
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local function validate(model, criterion, eval_metric, data, batch_size)
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local loss = 0
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local mse = 0
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local loss_count = 0
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local inputs_tmp = torch.Tensor(batch_size,
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data[1].x:size(1),
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data[1].x:size(2),
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data[1].x:size(3)):zero()
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local targets_tmp = torch.Tensor(batch_size,
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data[1].y:size(1),
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data[1].y:size(2),
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data[1].y:size(3)):zero()
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local inputs = inputs_tmp:clone():cuda()
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local targets = targets_tmp:clone():cuda()
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for t = 1, #data, batch_size do
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if t + batch_size -1 > #data then
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break
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end
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for i = 1, batch_size do
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inputs_tmp[i]:copy(data[t + i - 1].x)
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targets_tmp[i]:copy(data[t + i - 1].y)
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end
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inputs:copy(inputs_tmp)
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targets:copy(targets_tmp)
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local z = model:forward(inputs)
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loss = loss + criterion:forward(z, targets)
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mse = mse + eval_metric:forward(z, targets)
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loss_count = loss_count + 1
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if loss_count % 10 == 0 then
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xlua.progress(t, #data)
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collectgarbage()
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end
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end
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xlua.progress(#data, #data)
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return {loss = loss / loss_count, MSE = mse / loss_count, PSNR = 10 * math.log10(1 / (mse / loss_count))}
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end
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local function create_criterion(model)
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if reconstruct.is_rgb(model) then
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local offset = reconstruct.offset_size(model)
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local output_w = settings.crop_size - offset * 2
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local weight = torch.Tensor(3, output_w * output_w)
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weight[1]:fill(0.29891 * 3) -- R
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weight[2]:fill(0.58661 * 3) -- G
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weight[3]:fill(0.11448 * 3) -- B
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return w2nn.ClippedWeightedHuberCriterion(weight, 0.1, {0.0, 1.0}):cuda()
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else
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local offset = reconstruct.offset_size(model)
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local output_w = settings.crop_size - offset * 2
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local weight = torch.Tensor(1, output_w * output_w)
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weight[1]:fill(1.0)
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return w2nn.ClippedWeightedHuberCriterion(weight, 0.1, {0.0, 1.0}):cuda()
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end
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end
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local function transformer(model, x, is_validation, n, offset)
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local meta = {data = {}}
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local y = nil
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if type(x) == "table" and type(x[2]) == "table" then
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meta = x[2]
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if x[1].x and x[1].y then
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y = compression.decompress(x[1].y)
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x = compression.decompress(x[1].x)
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else
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x = compression.decompress(x[1])
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end
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else
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x = compression.decompress(x)
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end
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n = n or settings.patches
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if is_validation == nil then is_validation = false end
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local random_color_noise_rate = nil
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local random_overlay_rate = nil
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local active_cropping_rate = nil
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local active_cropping_tries = nil
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if is_validation then
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active_cropping_rate = settings.active_cropping_rate
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active_cropping_tries = settings.active_cropping_tries
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random_color_noise_rate = 0.0
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random_overlay_rate = 0.0
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else
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active_cropping_rate = settings.active_cropping_rate
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active_cropping_tries = settings.active_cropping_tries
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random_color_noise_rate = settings.random_color_noise_rate
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random_overlay_rate = settings.random_overlay_rate
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end
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if settings.method == "scale" then
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local conf = tablex.update({
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downsampling_filters = settings.downsampling_filters,
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random_half_rate = settings.random_half_rate,
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random_color_noise_rate = random_color_noise_rate,
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random_overlay_rate = random_overlay_rate,
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random_unsharp_mask_rate = settings.random_unsharp_mask_rate,
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max_size = settings.max_size,
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active_cropping_rate = active_cropping_rate,
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active_cropping_tries = active_cropping_tries,
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rgb = (settings.color == "rgb"),
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x_upsampling = not reconstruct.has_resize(model),
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resize_blur_min = settings.resize_blur_min,
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resize_blur_max = settings.resize_blur_max}, meta)
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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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n, conf)
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elseif settings.method == "noise" then
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local conf = tablex.update({
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random_half_rate = settings.random_half_rate,
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random_color_noise_rate = random_color_noise_rate,
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random_overlay_rate = random_overlay_rate,
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random_unsharp_mask_rate = settings.random_unsharp_mask_rate,
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max_size = settings.max_size,
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jpeg_chroma_subsampling_rate = settings.jpeg_chroma_subsampling_rate,
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active_cropping_rate = active_cropping_rate,
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active_cropping_tries = active_cropping_tries,
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nr_rate = settings.nr_rate,
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rgb = (settings.color == "rgb")}, meta)
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return pairwise_transform.jpeg(x,
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settings.style,
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settings.noise_level,
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settings.crop_size, offset,
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n, conf)
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elseif settings.method == "noise_scale" then
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local conf = tablex.update({
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downsampling_filters = settings.downsampling_filters,
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random_half_rate = settings.random_half_rate,
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random_color_noise_rate = random_color_noise_rate,
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random_overlay_rate = random_overlay_rate,
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random_unsharp_mask_rate = settings.random_unsharp_mask_rate,
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max_size = settings.max_size,
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jpeg_chroma_subsampling_rate = settings.jpeg_chroma_subsampling_rate,
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nr_rate = settings.nr_rate,
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active_cropping_rate = active_cropping_rate,
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active_cropping_tries = active_cropping_tries,
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rgb = (settings.color == "rgb"),
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x_upsampling = not reconstruct.has_resize(model),
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resize_blur_min = settings.resize_blur_min,
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resize_blur_max = settings.resize_blur_max}, meta)
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return pairwise_transform.jpeg_scale(x,
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settings.scale,
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settings.style,
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settings.noise_level,
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settings.crop_size, offset,
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n, conf)
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elseif settings.method == "user" then
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local conf = tablex.update({
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max_size = settings.max_size,
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active_cropping_rate = active_cropping_rate,
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active_cropping_tries = active_cropping_tries,
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rgb = (settings.color == "rgb")}, meta)
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return pairwise_transform.user(x, y,
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settings.crop_size, offset,
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n, conf)
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end
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end
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local function resampling(x, y, train_x, transformer, input_size, target_size)
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local c = 1
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local shuffle = torch.randperm(#train_x)
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for t = 1, #train_x do
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xlua.progress(t, #train_x)
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local xy = transformer(train_x[shuffle[t]], false, settings.patches)
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for i = 1, #xy do
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x[c]:copy(xy[i][1])
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y[c]:copy(xy[i][2])
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c = c + 1
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if c > x:size(1) then
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break
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end
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end
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if c > x:size(1) then
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break
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end
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if t % 50 == 0 then
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collectgarbage()
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end
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end
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xlua.progress(#train_x, #train_x)
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end
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local function get_oracle_data(x, y, instance_loss, k, samples)
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local index = torch.LongTensor(instance_loss:size(1))
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local dummy = torch.Tensor(instance_loss:size(1))
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torch.topk(dummy, index, instance_loss, k, 1, true)
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print("MSE of all data: " ..instance_loss:mean() .. ", MSE of oracle data: " .. dummy:mean())
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local shuffle = torch.randperm(k)
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local x_s = x:size()
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local y_s = y:size()
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x_s[1] = samples
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y_s[1] = samples
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local oracle_x = torch.Tensor(table.unpack(torch.totable(x_s)))
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local oracle_y = torch.Tensor(table.unpack(torch.totable(y_s)))
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for i = 1, samples do
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oracle_x[i]:copy(x[index[shuffle[i]]])
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oracle_y[i]:copy(y[index[shuffle[i]]])
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end
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return oracle_x, oracle_y
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end
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local function remove_small_image(x)
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local new_x = {}
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for i = 1, #x do
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local xe, meta, x_s
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xe = x[i]
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if type(x) == "table" and type(x[2]) == "table" then
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if xe[1].x and xe[1].y then
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x_s = compression.size(xe[1].y) -- y size
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else
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x_s = compression.size(xe[1])
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end
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else
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x_s = compression.size(xe)
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end
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if x_s[2] / settings.scale > settings.crop_size + 32 and
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x_s[3] / settings.scale > settings.crop_size + 32 then
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table.insert(new_x, x[i])
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end
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if i % 100 == 0 then
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collectgarbage()
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end
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end
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print(string.format("%d small images are removed", #x - #new_x))
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return new_x
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end
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local function plot(train, valid)
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gnuplot.plot({
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{'training', torch.Tensor(train), '-'},
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{'validation', torch.Tensor(valid), '-'}})
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end
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local function train()
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local hist_train = {}
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local hist_valid = {}
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local model
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if settings.resume:len() > 0 then
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model = torch.load(settings.resume, "ascii")
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else
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model = srcnn.create(settings.model, settings.backend, settings.color)
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end
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dir.makepath(settings.model_dir)
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local offset = reconstruct.offset_size(model)
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local pairwise_func = function(x, is_validation, n)
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return transformer(model, x, is_validation, n, offset)
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end
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local criterion = create_criterion(model)
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local eval_metric = w2nn.ClippedMSECriterion(0, 1):cuda()
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local x = remove_small_image(torch.load(settings.images))
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local train_x, valid_x = split_data(x, math.max(math.floor(settings.validation_rate * #x), 1))
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local adam_config = {
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xLearningRate = settings.learning_rate,
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xBatchSize = settings.batch_size,
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xLearningRateDecay = settings.learning_rate_decay
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}
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local ch = nil
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if settings.color == "y" then
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ch = 1
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elseif settings.color == "rgb" then
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ch = 3
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end
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local best_score = 1000.0
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print("# make validation-set")
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local valid_xy = make_validation_set(valid_x, pairwise_func,
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settings.validation_crops,
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settings.patches)
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valid_x = nil
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collectgarbage()
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model:cuda()
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print("load .. " .. #train_x)
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local x = nil
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local y = torch.Tensor(settings.patches * #train_x,
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ch * (settings.crop_size - offset * 2) * (settings.crop_size - offset * 2)):zero()
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if reconstruct.has_resize(model) then
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x = torch.Tensor(settings.patches * #train_x,
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ch, settings.crop_size / settings.scale, settings.crop_size / settings.scale)
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else
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x = torch.Tensor(settings.patches * #train_x,
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ch, settings.crop_size, settings.crop_size)
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end
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local instance_loss = nil
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for epoch = 1, settings.epoch do
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model:training()
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print("# " .. epoch)
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if adam_config.learningRate then
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print("learning rate: " .. adam_config.learningRate)
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end
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print("## resampling")
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if instance_loss then
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-- active learning
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local oracle_k = math.min(x:size(1) * (settings.oracle_rate * (1 / (1 - settings.oracle_drop_rate))), x:size(1))
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local oracle_n = math.min(x:size(1) * settings.oracle_rate, x:size(1))
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if oracle_n > 0 then
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local oracle_x, oracle_y = get_oracle_data(x, y, instance_loss, oracle_k, oracle_n)
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resampling(x:narrow(1, oracle_x:size(1) + 1, x:size(1)-oracle_x:size(1)),
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y:narrow(1, oracle_x:size(1) + 1, x:size(1) - oracle_x:size(1)), train_x, pairwise_func)
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x:narrow(1, 1, oracle_x:size(1)):copy(oracle_x)
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y:narrow(1, 1, oracle_y:size(1)):copy(oracle_y)
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local draw_n = math.floor(math.sqrt(oracle_x:size(1), 0.5))
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if draw_n > 100 then
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draw_n = 100
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end
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image.save(path.join(settings.model_dir, "oracle_x.png"),
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image.toDisplayTensor({
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input = oracle_x:narrow(1, 1, draw_n * draw_n),
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padding = 2,
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nrow = draw_n,
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min = 0,
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max = 1}))
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else
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resampling(x, y, train_x, pairwise_func)
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end
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else
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resampling(x, y, train_x, pairwise_func)
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end
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collectgarbage()
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instance_loss = torch.Tensor(x:size(1)):zero()
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for i = 1, settings.inner_epoch do
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model:training()
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local train_score, il = minibatch_adam(model, criterion, eval_metric, x, y, adam_config)
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instance_loss:copy(il)
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print(train_score)
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model:evaluate()
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print("# validation")
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local score = validate(model, criterion, eval_metric, valid_xy, adam_config.xBatchSize)
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table.insert(hist_train, train_score.loss)
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table.insert(hist_valid, score.loss)
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if settings.plot then
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plot(hist_train, hist_valid)
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end
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if score.MSE < best_score then
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local test_image = image_loader.load_float(settings.test) -- reload
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best_score = score.MSE
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print("* model has updated")
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if settings.save_history then
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torch.save(settings.model_file_best, model:clearState(), "ascii")
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torch.save(string.format(settings.model_file, epoch, i), model:clearState(), "ascii")
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if settings.method == "noise" then
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local log = path.join(settings.model_dir,
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("noise%d_best.%d-%d.png"):format(settings.noise_level,
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epoch, i))
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save_test_jpeg(model, test_image, log)
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elseif settings.method == "scale" then
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local log = path.join(settings.model_dir,
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("scale%.1f_best.%d-%d.png"):format(settings.scale,
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epoch, i))
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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.%d-%d.png"):format(settings.noise_level,
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settings.scale,
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epoch, i))
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save_test_scale(model, test_image, log)
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elseif settings.method == "user" then
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local log = path.join(settings.model_dir,
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("%s_best.%d-%d.png"):format(settings.name,
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epoch, i))
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save_test_user(model, test_image, log)
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end
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else
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torch.save(settings.model_file, model:clearState(), "ascii")
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if settings.method == "noise" then
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local log = path.join(settings.model_dir,
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("noise%d_best.png"):format(settings.noise_level))
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save_test_jpeg(model, test_image, log)
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elseif settings.method == "scale" then
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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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elseif settings.method == "user" then
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local log = path.join(settings.model_dir,
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("%s_best.png"):format(settings.name))
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save_test_user(model, test_image, log)
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end
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end
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end
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print("Batch-wise PSNR: " .. score.PSNR .. ", loss: " .. score.loss .. ", MSE: " .. score.MSE .. ", Minimum MSE: " .. best_score)
|
|
collectgarbage()
|
|
end
|
|
end
|
|
end
|
|
if settings.gpu > 0 then
|
|
cutorch.setDevice(settings.gpu)
|
|
end
|
|
torch.manualSeed(settings.seed)
|
|
cutorch.manualSeed(settings.seed)
|
|
print(settings)
|
|
train()
|