Add support for scale4 in benchmark
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@ -18,7 +18,7 @@ cmd:option("-dir", "./data/test", 'test image directory')
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cmd:option("-file", "", 'test image file list')
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cmd:option("-model1_dir", "./models/anime_style_art_rgb", 'model1 directory')
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cmd:option("-model2_dir", "", 'model2 directory (optional)')
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cmd:option("-method", "scale", '(scale|noise|noise_scale|user|diff)')
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cmd:option("-method", "scale", '(scale|noise|noise_scale|user|diff|scale4)')
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cmd:option("-filter", "Catrom", "downscaling filter (Box|Lanczos|Catrom(Bicubic))")
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cmd:option("-resize_blur", 1.0, 'blur parameter for resize')
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cmd:option("-color", "y", '(rgb|y|r|g|b)')
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@ -154,12 +154,24 @@ local function baseline_scale(x, filter)
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x:size(2) * 2.0,
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filter)
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end
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local function baseline_scale4(x, filter)
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return iproc.scale(x,
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x:size(3) * 4.0,
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x:size(2) * 4.0,
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filter)
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end
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local function transform_scale(x, opt)
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return iproc.scale(x,
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x:size(3) * 0.5,
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x:size(2) * 0.5,
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opt.filter, opt.resize_blur)
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end
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local function transform_scale4(x, opt)
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return iproc.scale(x,
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x:size(3) * 0.25,
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x:size(2) * 0.25,
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opt.filter, opt.resize_blur)
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end
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local function transform_scale_jpeg(x, opt)
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x = iproc.scale(x,
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@ -237,6 +249,26 @@ local function benchmark(opt, x, model1, model2)
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model2_time = model2_time + (sys.clock() - t)
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end
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baseline_output = baseline_scale(input, opt.baseline_filter)
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elseif opt.method == "scale4" then
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input = transform_scale4(x[i].y, opt)
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ground_truth = x[i].y
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if opt.force_cudnn and i == 1 then -- run cuDNN benchmark first
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model1_output = scale_f(model1, 2.0, input, opt.crop_size, opt.batch_size)
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if model2 then
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model2_output = scale_f(model2, 2.0, input, opt.crop_size, opt.batch_size)
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end
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end
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t = sys.clock()
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model1_output = scale_f(model1, 2.0, input, opt.crop_size, opt.batch_size)
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model1_output = scale_f(model1, 2.0, model1_output, opt.crop_size, opt.batch_size)
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model1_time = model1_time + (sys.clock() - t)
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if model2 then
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t = sys.clock()
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model2_output = scale_f(model2, 2.0, input, opt.crop_size, opt.batch_size)
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model2_output = scale_f(model2, 2.0, model2_output, opt.crop_size, opt.batch_size)
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model2_time = model2_time + (sys.clock() - t)
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end
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baseline_output = baseline_scale4(input, opt.baseline_filter)
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elseif opt.method == "noise" then
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input = transform_jpeg(x[i].y, opt)
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ground_truth = x[i].y
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@ -604,7 +636,7 @@ if opt.show_progress then
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print(opt)
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end
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if opt.method == "scale" then
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if opt.method == "scale" or opt.method == "scale4" then
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local f1 = path.join(opt.model1_dir, "scale2.0x_model.t7")
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local f2 = path.join(opt.model2_dir, "scale2.0x_model.t7")
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local s1, model1 = pcall(w2nn.load_model, f1, opt.force_cudnn)
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