192 lines
8.8 KiB
Lua
192 lines
8.8 KiB
Lua
require 'xlua'
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require 'pl'
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require 'trepl'
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require 'cutorch'
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-- global settings
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if package.preload.settings then
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return package.preload.settings
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end
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-- default tensor type
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torch.setdefaulttensortype('torch.FloatTensor')
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local settings = {}
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local cmd = torch.CmdLine()
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cmd:text()
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cmd:text("waifu2x-training")
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cmd:text("Options:")
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cmd:option("-seed", 11, 'RNG seed (note: it only able to reproduce the training results with `-thread 1`)')
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cmd:option("-data_dir", "./data", 'path to data directory')
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cmd:option("-backend", "cunn", '(cunn|cudnn)')
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cmd:option("-test", "images/miku_small.png", 'path to test image')
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cmd:option("-model_dir", "./models", 'model directory')
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cmd:option("-method", "scale", 'method to training (noise|scale|noise_scale|user)')
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cmd:option("-model", "vgg_7", 'model architecture (vgg_7|vgg_12|upconv_7|upconv_8_4x|dilated_7)')
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cmd:option("-noise_level", 1, '(0|1|2|3)')
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cmd:option("-style", "art", '(art|photo)')
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cmd:option("-color", 'rgb', '(y|rgb)')
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cmd:option("-random_color_noise_rate", 0.0, 'data augmentation using color noise (0.0-1.0)')
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cmd:option("-random_overlay_rate", 0.0, 'data augmentation using flipped image overlay (0.0-1.0)')
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cmd:option("-random_half_rate", 0.0, 'data augmentation using half resolution image (0.0-1.0)')
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cmd:option("-random_unsharp_mask_rate", 0.0, 'data augmentation using unsharp mask (0.0-1.0)')
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cmd:option("-random_blur_rate", 0.0, 'data augmentation using gaussian blur (0.0-1.0)')
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cmd:option("-random_blur_size", "3,5", 'filter size for random gaussian blur (comma separated)')
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cmd:option("-random_blur_sigma_min", 0.5, 'min sigma for random gaussian blur')
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cmd:option("-random_blur_sigma_max", 1.0, 'max sigma for random gaussian blur')
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cmd:option("-random_pairwise_scale_rate", 0.0, 'data augmentation using pairwise resize for user method')
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cmd:option("-random_pairwise_scale_min", 0.85, 'min scale factor for random pairwise scale')
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cmd:option("-random_pairwise_scale_max", 1.176, 'max scale factor for random pairwise scale')
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cmd:option("-random_pairwise_rotate_rate", 0.0, 'data augmentation using pairwise resize for user method')
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cmd:option("-random_pairwise_rotate_min", -6, 'min rotate angle for random pairwise rotate')
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cmd:option("-random_pairwise_rotate_max", 6, 'max rotate angle for random pairwise rotate')
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cmd:option("-random_pairwise_negate_rate", 0.0, 'data augmentation using nagate image for user method')
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cmd:option("-random_pairwise_negate_x_rate", 0.0, 'data augmentation using nagate image only x side for user method')
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cmd:option("-pairwise_y_binary", 0, 'binarize y after data augmentation(0|1)')
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cmd:option("-pairwise_flip", 1, 'use flip(0|1)')
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cmd:option("-scale", 2.0, 'scale factor (2)')
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cmd:option("-learning_rate", 0.00025, 'learning rate for adam')
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cmd:option("-crop_size", 48, 'crop size')
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cmd:option("-max_size", 256, 'if image is larger than N, image will be crop randomly')
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cmd:option("-batch_size", 16, 'mini batch size')
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cmd:option("-patches", 64, 'number of patch samples')
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cmd:option("-inner_epoch", 4, 'number of inner epochs')
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cmd:option("-epoch", 50, 'number of epochs to run')
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cmd:option("-thread", -1, 'number of CPU threads')
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cmd:option("-jpeg_chroma_subsampling_rate", 0.5, 'the rate of using YUV 4:2:0 in denoising training (0.0-1.0)')
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cmd:option("-validation_rate", 0.05, 'validation-set rate (number_of_training_images * validation_rate > 1)')
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cmd:option("-validation_crops", 200, 'number of cropping region per image in validation')
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cmd:option("-active_cropping_rate", 0.5, 'active cropping rate')
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cmd:option("-active_cropping_tries", 10, 'active cropping tries')
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cmd:option("-nr_rate", 0.65, 'trade-off between reducing noise and erasing details (0.0-1.0)')
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cmd:option("-save_history", 0, 'save all model (0|1)')
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cmd:option("-plot", 0, 'plot loss chart(0|1)')
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cmd:option("-downsampling_filters", "Box,Lanczos,Sinc", '(comma separated)downsampling filters for 2x scale training. (Point,Box,Triangle,Hermite,Hanning,Hamming,Blackman,Gaussian,Quadratic,Cubic,Catrom,Mitchell,Lanczos,Bessel,Sinc)')
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cmd:option("-max_training_image_size", -1, 'if training image is larger than N, image will be crop randomly when data converting')
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cmd:option("-use_transparent_png", 0, 'use transparent png (0|1)')
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cmd:option("-resize_blur_min", 0.95, 'min blur parameter for ResizeImage')
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cmd:option("-resize_blur_max", 1.05, 'max blur parameter for ResizeImage')
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cmd:option("-oracle_rate", 0.1, '')
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cmd:option("-oracle_drop_rate", 0.5, '')
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cmd:option("-learning_rate_decay", 3.0e-7, 'learning rate decay (learning_rate * 1/(1+num_of_data*patches*epoch))')
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cmd:option("-resume", "", 'resume model file')
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cmd:option("-name", "user", 'model name for user method')
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cmd:option("-gpu", "", 'GPU Device ID or ID lists (comma seprated)')
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cmd:option("-loss", "huber", 'loss function (huber|l1|mse|bce)')
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cmd:option("-update_criterion", "mse", 'mse|loss')
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cmd:option("-padding", 0, 'replication padding size')
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cmd:option("-padding_y_zero", 0, 'zero padding y for segmentation (0|1)')
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cmd:option("-grayscale", 0, 'grayscale x&y (0|1)')
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cmd:option("-validation_filename_split", 0, 'make validation-set based on filename(basename)')
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cmd:option("-invert_x", 0, 'invert x image in convert_lua')
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local function to_bool(settings, name)
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if settings[name] == 1 then
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settings[name] = true
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else
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settings[name] = false
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end
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end
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local opt = cmd:parse(arg)
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for k, v in pairs(opt) do
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settings[k] = v
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end
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to_bool(settings, "plot")
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to_bool(settings, "save_history")
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to_bool(settings, "use_transparent_png")
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to_bool(settings, "pairwise_y_binary")
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to_bool(settings, "pairwise_flip")
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to_bool(settings, "padding_y_zero")
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to_bool(settings, "grayscale")
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to_bool(settings, "validation_filename_split")
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to_bool(settings, "invert_x")
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if settings.plot then
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require 'gnuplot'
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end
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if settings.save_history then
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if settings.method == "noise" then
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settings.model_file = string.format("%s/noise%d_model.%%d-%%d.t7",
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settings.model_dir, settings.noise_level)
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settings.model_file_best = string.format("%s/noise%d_model.t7",
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settings.model_dir, settings.noise_level)
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elseif settings.method == "scale" then
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settings.model_file = string.format("%s/scale%.1fx_model.%%d-%%d.t7",
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settings.model_dir, settings.scale)
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settings.model_file_best = string.format("%s/scale%.1fx_model.t7",
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settings.model_dir, settings.scale)
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elseif settings.method == "noise_scale" then
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settings.model_file = string.format("%s/noise%d_scale%.1fx_model.%%d-%%d.t7",
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settings.model_dir,
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settings.noise_level,
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settings.scale)
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settings.model_file_best = string.format("%s/noise%d_scale%.1fx_model.t7",
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settings.model_dir,
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settings.noise_level,
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settings.scale)
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elseif settings.method == "user" then
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settings.model_file = string.format("%s/%s_model.%%d-%%d.t7",
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settings.model_dir,
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settings.name)
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settings.model_file_best = string.format("%s/%s_model.t7",
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settings.model_dir,
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settings.name)
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else
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error("unknown method: " .. settings.method)
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end
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else
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if settings.method == "noise" then
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settings.model_file = string.format("%s/noise%d_model.t7",
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settings.model_dir, settings.noise_level)
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elseif settings.method == "scale" then
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settings.model_file = string.format("%s/scale%.1fx_model.t7",
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settings.model_dir, settings.scale)
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elseif settings.method == "noise_scale" then
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settings.model_file = string.format("%s/noise%d_scale%.1fx_model.t7",
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settings.model_dir, settings.noise_level, settings.scale)
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elseif settings.method == "user" then
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settings.model_file = string.format("%s/%s_model.t7",
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settings.model_dir, settings.name)
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else
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error("unknown method: " .. settings.method)
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end
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end
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if not (settings.color == "rgb" or settings.color == "y") then
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error("color must be y or rgb")
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end
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if not ( settings.scale == 1 or (settings.scale == math.floor(settings.scale) and settings.scale % 2 == 0)) then
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error("scale must be 1 or mod-2")
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end
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if not (settings.style == "art" or
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settings.style == "photo") then
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error(string.format("unknown style: %s", settings.style))
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end
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if settings.thread > 0 then
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torch.setnumthreads(tonumber(settings.thread))
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end
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if settings.downsampling_filters and settings.downsampling_filters:len() > 0 then
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settings.downsampling_filters = settings.downsampling_filters:split(",")
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else
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settings.downsampling_filters = {"Box", "Lanczos", "Catrom"}
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end
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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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if settings.gpu:len() > 0 then
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local gpus = {}
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local gpu_string = utils.split(settings.gpu, ",")
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for i = 1, #gpu_string do
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table.insert(gpus, tonumber(gpu_string[i]))
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end
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settings.gpu = gpus
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else
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settings.gpu = {1}
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end
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cutorch.setDevice(settings.gpu[1])
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return settings
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