Add -force_cudnn option; support for cuDNN in waifu2x.lua/web.lua
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nagadomi
parent
599da6a665
commit
af74a67bd1
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@ -16,6 +16,15 @@ else
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pcall(load_cunn)
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pcall(load_cudnn)
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w2nn = {}
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function w2nn.load_model(model_path, force_cudnn)
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local model = torch.load(model_path, "ascii")
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if force_cudnn then
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model = cudnn.convert(model, cudnn)
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end
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model:cuda():evaluate()
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return model
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end
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require 'LeakyReLU'
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require 'LeakyReLU_deprecated'
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require 'DepthExpand2x'
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39
waifu2x.lua
39
waifu2x.lua
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@ -59,7 +59,7 @@ local function convert_image(opt)
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opt.o = format_output(opt, opt.i)
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if opt.m == "noise" then
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local model_path = path.join(opt.model_dir, ("noise%d_model.t7"):format(opt.noise_level))
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local model = torch.load(model_path, "ascii")
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local model = w2nn.load_model(model_path, opt.force_cudnn)
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if not model then
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error("Load Error: " .. model_path)
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end
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@ -69,7 +69,7 @@ local function convert_image(opt)
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print(opt.o .. ": " .. (sys.clock() - t) .. " sec")
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elseif opt.m == "scale" then
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local model_path = path.join(opt.model_dir, ("scale%.1fx_model.t7"):format(opt.scale))
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local model = torch.load(model_path, "ascii")
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local model = w2nn.load_model(model_path, opt.force_cudnn)
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if not model then
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error("Load Error: " .. model_path)
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end
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@ -82,8 +82,8 @@ local function convert_image(opt)
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local model_path = path.join(opt.model_dir, ("noise%d_scale%.1fx_model.t7"):format(opt.noise_level, opt.scale))
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if path.exists(model_path) then
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local scale_model_path = path.join(opt.model_dir, ("scale%.1fx_model.t7"):format(opt.scale))
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local t, scale_model = pcall(torch.load, scale_model_path, "ascii")
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local model = torch.load(model_path, "ascii")
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local t, scale_model = pcall(load_model, scale_model_path, opt.force_cudnn)
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local model = w2nn.load_model(model_path, opt.force_cudnn)
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if not t then
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scale_model = model
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end
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@ -94,9 +94,9 @@ local function convert_image(opt)
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print(opt.o .. ": " .. (sys.clock() - t) .. " sec")
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else
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local noise_model_path = path.join(opt.model_dir, ("noise%d_model.t7"):format(opt.noise_level))
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local noise_model = torch.load(noise_model_path, "ascii")
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local noise_model = w2nn.load_model(noise_model_path, opt.force_cudnn)
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local scale_model_path = path.join(opt.model_dir, ("scale%.1fx_model.t7"):format(opt.scale))
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local scale_model = torch.load(scale_model_path, "ascii")
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local scale_model = w2nn.load_model(scale_model_path, opt.force_cudnn)
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local t = sys.clock()
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x = alpha_util.make_border(x, alpha, reconstruct.offset_size(scale_model))
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x = image_f(noise_model, x, opt.crop_size, opt.batch_size)
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@ -129,24 +129,24 @@ local function convert_frames(opt)
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end
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if opt.m == "scale" then
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model_path = path.join(opt.model_dir, ("scale%.1fx_model.t7"):format(opt.scale))
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scale_model = torch.load(model_path, "ascii")
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scale_model = w2nn.load_model(model_path, opt.force_cudnn)
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elseif opt.m == "noise" then
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model_path = path.join(opt.model_dir, string.format("noise%d_model.t7", opt.noise_level))
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noise_model[opt.noise_level] = torch.load(model_path, "ascii")
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noise_model[opt.noise_level] = w2nn.load_model(model_path, opt.force_cudnn)
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elseif opt.m == "noise_scale" then
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local model_path = path.join(opt.model_dir, ("noise%d_scale%.1fx_model.t7"):format(opt.noise_level, opt.scale))
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if path.exists(model_path) then
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noise_scale_model[opt.noise_level] = torch.load(model_path, "ascii")
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noise_scale_model[opt.noise_level] = w2nn.load_model(model_path, opt.force_cudnn)
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model_path = path.join(opt.model_dir, ("scale%.1fx_model.t7"):format(opt.scale))
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t, scale_model = pcall(torch.load, model_path, "ascii")
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t, scale_model = pcall(load_model, model_path, opt.force_cudnn)
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if not t then
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scale_model = noise_scale_model[opt.noise_level]
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end
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else
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model_path = path.join(opt.model_dir, ("scale%.1fx_model.t7"):format(opt.scale))
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scale_model = torch.load(model_path, "ascii")
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scale_model = w2nn.load_model(model_path, opt.force_cudnn)
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model_path = path.join(opt.model_dir, string.format("noise%d_model.t7", opt.noise_level))
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noise_model[opt.noise_level] = torch.load(model_path, "ascii")
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noise_model[opt.noise_level] = w2nn.load_model(model_path, opt.force_cudnn)
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end
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end
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local fp = io.open(opt.l)
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@ -214,16 +214,25 @@ local function waifu2x()
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cmd:option("-thread", -1, "number of CPU threads")
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cmd:option("-tta", 0, '8x slower and slightly high quality (0|1)')
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cmd:option("-tta_level", 8, 'TTA level (2|4|8)')
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cmd:option("-force_cudnn", 0, 'use cuDNN backend (0|1)')
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local opt = cmd:parse(arg)
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if opt.thread > 0 then
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torch.setnumthreads(opt.thread)
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end
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if cudnn then
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cudnn.fastest = true
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cudnn.benchmark = false
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if opt.l:len() > 0 then
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cudnn.benchmark = true -- find fastest algo
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else
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cudnn.benchmark = false
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end
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end
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if opt.force_cudnn == 1 then
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opt.force_cudnn = true
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else
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opt.force_cudnn = false
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end
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if string.len(opt.l) == 0 then
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convert_image(opt)
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else
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22
web.lua
22
web.lua
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@ -28,6 +28,8 @@ cmd:option("-gpu", 1, 'Device ID')
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cmd:option("-crop_size", 128, 'patch size per process')
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cmd:option("-batch_size", 1, 'batch size')
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cmd:option("-thread", -1, 'number of CPU threads')
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cmd:option("-force_cudnn", 0, 'use cuDNN backend (0|1)')
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local opt = cmd:parse(arg)
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cutorch.setDevice(opt.gpu)
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torch.setdefaulttensortype('torch.FloatTensor')
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@ -36,18 +38,20 @@ if opt.thread > 0 then
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end
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if cudnn then
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cudnn.fastest = true
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cudnn.benchmark = false
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cudnn.benchmark = true
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end
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opt.force_cudnn = opt.force_cudnn == 1
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local ART_MODEL_DIR = path.join(ROOT, "models", "upconv_7", "art")
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local PHOTO_MODEL_DIR = path.join(ROOT, "models", "photo")
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local art_scale2_model = torch.load(path.join(ART_MODEL_DIR, "scale2.0x_model.t7"), "ascii")
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local art_noise1_model = torch.load(path.join(ART_MODEL_DIR, "noise1_model.t7"), "ascii")
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local art_noise2_model = torch.load(path.join(ART_MODEL_DIR, "noise2_model.t7"), "ascii")
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local art_noise3_model = torch.load(path.join(ART_MODEL_DIR, "noise3_model.t7"), "ascii")
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local photo_scale2_model = torch.load(path.join(PHOTO_MODEL_DIR, "scale2.0x_model.t7"), "ascii")
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local photo_noise1_model = torch.load(path.join(PHOTO_MODEL_DIR, "noise1_model.t7"), "ascii")
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local photo_noise2_model = torch.load(path.join(PHOTO_MODEL_DIR, "noise2_model.t7"), "ascii")
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local photo_noise3_model = torch.load(path.join(PHOTO_MODEL_DIR, "noise3_model.t7"), "ascii")
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local art_scale2_model = w2nn.load_model(path.join(ART_MODEL_DIR, "scale2.0x_model.t7"), opt.force_cudnn)
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local art_noise1_model = w2nn.load_model(path.join(ART_MODEL_DIR, "noise1_model.t7"), opt.force_cudnn)
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local art_noise2_model = w2nn.load_model(path.join(ART_MODEL_DIR, "noise2_model.t7"), opt.force_cudnn)
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local art_noise3_model = w2nn.load_model(path.join(ART_MODEL_DIR, "noise3_model.t7"), opt.force_cudnn)
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local photo_scale2_model = w2nn.load_model(path.join(PHOTO_MODEL_DIR, "scale2.0x_model.t7"), opt.force_cudnn)
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local photo_noise1_model = w2nn.load_model(path.join(PHOTO_MODEL_DIR, "noise1_model.t7"), opt.force_cudnn)
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local photo_noise2_model = w2nn.load_model(path.join(PHOTO_MODEL_DIR, "noise2_model.t7"), opt.force_cudnn)
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local photo_noise3_model = w2nn.load_model(path.join(PHOTO_MODEL_DIR, "noise3_model.t7"), opt.force_cudnn)
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collectgarbage()
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local CLEANUP_MODEL = false -- if you are using the low memory GPU, you could use this flag.
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local CACHE_DIR = path.join(ROOT, "cache")
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local MAX_NOISE_IMAGE = 2560 * 2560
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