8dea362bed
- Memory compression by snappy (lua-csnappy) - Use RGB-wise Weighted MSE(R*0.299, G*0.587, B*0.114) instead of MSE - Aggressive cropping for edge region and some change.
223 lines
6.9 KiB
Lua
223 lines
6.9 KiB
Lua
require './lib/portable'
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require './lib/mynn'
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require 'optim'
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require 'xlua'
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require 'pl'
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require 'snappy'
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local settings = require './lib/settings'
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local srcnn = require './lib/srcnn'
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local minibatch_adam = require './lib/minibatch_adam'
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local iproc = require './lib/iproc'
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local reconstruct = require './lib/reconstruct'
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local pairwise_transform = require './lib/pairwise_transform'
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local image_loader = require './lib/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 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)
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n = n or 4
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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 / 8, 1) do
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local xy = transformer(x[i], true, 8)
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for j = 1, #xy do
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local x = xy[j][1]
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local y = xy[j][2]
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table.insert(data, {x = x:reshape(1, x:size(1), x:size(2), x:size(3)),
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y = y:reshape(1, y:size(1), y:size(2), y:size(3))})
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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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return data
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end
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local function validate(model, criterion, data)
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local loss = 0
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for i = 1, #data do
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local z = model:forward(data[i].x:cuda())
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loss = loss + criterion:forward(z, data[i].y:cuda())
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xlua.progress(i, #data)
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if i % 10 == 0 then
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collectgarbage()
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end
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end
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return loss / #data
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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(0.299 * 3) -- R
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weight[2]:fill(0.587 * 3) -- G
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weight[3]:fill(0.114 * 3) -- B
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return mynn.RGBWeightedMSECriterion(weight):cuda()
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else
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return nn.MSECriterion():cuda()
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end
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end
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local function transformer(x, is_validation, n, offset)
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local size = x[1]
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local dec = snappy.decompress(x[2]:string())
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x = torch.ByteTensor(size[1], size[2], size[3])
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x:storage():string(dec)
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n = n or settings.batch_size;
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if is_validation == nil then is_validation = false end
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local color_noise = nil
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local overlay = nil
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local active_cropping_ratio = 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 = 0.0
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active_cropping_tries = 0
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color_noise = false
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overlay = false
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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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color_noise = settings.color_noise
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overlay = settings.overlay
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end
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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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n,
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{ color_noise = color_noise,
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overlay = overlay,
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random_half = settings.random_half,
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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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})
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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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n,
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{ color_noise = color_noise,
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overlay = overlay,
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active_cropping_rate = active_cropping_rate,
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active_cropping_tries = active_cropping_tries,
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random_half = settings.random_half,
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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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local function train()
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local model = srcnn.create(settings.method, settings.backend, settings.color)
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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(x, is_validation, n, offset)
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end
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local criterion = create_criterion(model)
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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, math.floor(settings.validation_ratio * #x))
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local adam_config = {
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learningRate = settings.learning_rate,
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xBatchSize = settings.batch_size,
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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 = 100000.0
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print("# make validation-set")
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local valid_xy = make_validation_set(valid_x, pairwise_func, settings.validation_crops)
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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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for epoch = 1, settings.epoch do
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model:training()
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print("# " .. epoch)
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print(minibatch_adam(model, criterion, train_x, adam_config,
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pairwise_func,
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{ch, settings.crop_size, settings.crop_size},
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{ch, settings.crop_size - offset * 2, settings.crop_size - offset * 2}
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))
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model:evaluate()
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print("# validation")
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local score = validate(model, criterion, valid_xy)
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if score < best_score then
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local test_image = image_loader.load_float(settings.test) -- reload
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lrd_count = 0
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best_score = score
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print("* update best model")
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torch.save(settings.model_file, model)
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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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end
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else
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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 * 0.9
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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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print("current: " .. score .. ", best: " .. best_score)
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collectgarbage()
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end
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end
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torch.manualSeed(settings.seed)
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cutorch.manualSeed(settings.seed)
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print(settings)
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train()
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