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@@ -105,6 +105,10 @@ local function PSNR(x1, x2, color)
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local mse = math.max(MSE(x1, x2, color), 1)
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return 10 * math.log10((255.0 * 255.0) / mse)
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end
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+local function MSE2PSNR(mse)
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+ return 10 * math.log10((255.0 * 255.0) / mse)
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+end
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+
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local function transform_jpeg(x, opt)
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for i = 1, opt.jpeg_times do
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jpeg = gm.Image(x, "RGB", "DHW")
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@@ -146,12 +150,15 @@ local function transform_scale_jpeg(x, opt)
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end
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local function benchmark(opt, x, input_func, model1, model2)
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+ local mse
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local model1_mse = 0
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local model2_mse = 0
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local baseline_mse = 0
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local model1_psnr = 0
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local model2_psnr = 0
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local baseline_psnr = 0
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+ local model1_time = 0
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+ local model2_time = 0
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local scale_f = reconstruct.scale
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local image_f = reconstruct.image
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if opt.tta then
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@@ -168,24 +175,31 @@ local function benchmark(opt, x, input_func, model1, model2)
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for i = 1, #x do
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local ground_truth = x[i].image
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local basename = x[i].basename
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-
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local input, model1_output, model2_output, baseline_output
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input = input_func(ground_truth, opt)
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- t = sys.clock()
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if opt.method == "scale" then
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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_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_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 == "noise" then
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+ t = sys.clock()
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model1_output = image_f(model1, input, 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 = image_f(model2, input, 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 = input
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elseif opt.method == "noise_scale" then
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+ t = sys.clock()
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if model1.noise_scale_model then
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model1_output = scale_f(model1.noise_scale_model, 2.0,
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input, opt.crop_size, opt.batch_size)
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@@ -198,7 +212,10 @@ local function benchmark(opt, x, input_func, model1, model2)
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model1_output = scale_f(model1.scale_model, 2.0, model1_output,
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opt.crop_size, opt.batch_size)
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end
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+ model1_time = model1_time + (sys.clock() - t)
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+
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if model2 then
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+ t = sys.clock()
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if model2.noise_scale_model then
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model2_output = scale_f(model2.noise_scale_model, 2.0,
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input, opt.crop_size, opt.batch_size)
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@@ -212,18 +229,22 @@ local function benchmark(opt, x, input_func, model1, model2)
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model2_output = scale_f(model2.scale_model, 2.0, model2_output,
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opt.crop_size, opt.batch_size)
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end
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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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end
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- model1_mse = model1_mse + MSE(ground_truth, model1_output, opt.color)
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- model1_psnr = model1_psnr + PSNR(ground_truth, model1_output, opt.color)
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+ mse = MSE(ground_truth, model1_output, opt.color)
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+ model1_mse = model1_mse + mse
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+ model1_psnr = model1_psnr + MSE2PSNR(mse)
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if model2 then
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- model2_mse = model2_mse + MSE(ground_truth, model2_output, opt.color)
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- model2_psnr = model2_psnr + PSNR(ground_truth, model2_output, opt.color)
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+ mse = MSE(ground_truth, model2_output, opt.color)
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+ model2_mse = model2_mse + mse
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+ model2_psnr = model2_psnr + MSE2PSNR(mse)
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end
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if baseline_output then
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- baseline_mse = baseline_mse + MSE(ground_truth, baseline_output, opt.color)
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- baseline_psnr = baseline_psnr + PSNR(ground_truth, baseline_output, opt.color)
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+ mse = MSE(ground_truth, baseline_output, opt.color)
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+ baseline_mse = baseline_mse + mse
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+ baseline_psnr = baseline_psnr + MSE2PSNR(mse)
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end
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if opt.save_image then
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if opt.save_baseline_image and baseline_output then
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@@ -243,8 +264,10 @@ local function benchmark(opt, x, input_func, model1, model2)
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if model2 then
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if baseline_output then
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io.stdout:write(
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- string.format("%d/%d; baseline_rmse=%f, model1_rmse=%f, model2_rmse=%f, baseline_psnr=%f, model1_psnr=%f, model2_psnr=%f \r",
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+ string.format("%d/%d; model1_time=%.2f, model2_time=%.2f, baseline_rmse=%f, model1_rmse=%f, model2_rmse=%f, baseline_psnr=%f, model1_psnr=%f, model2_psnr=%f \r",
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i, #x,
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+ model1_time,
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+ model2_time,
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math.sqrt(baseline_mse / i),
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math.sqrt(model1_mse / i), math.sqrt(model2_mse / i),
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baseline_psnr / i,
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@@ -252,8 +275,10 @@ local function benchmark(opt, x, input_func, model1, model2)
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))
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else
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io.stdout:write(
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- string.format("%d/%d; model1_rmse=%f, model2_rmse=%f, model1_psnr=%f, model2_psnr=%f \r",
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+ string.format("%d/%d; model1_time=%.2f, model2_time=%.2f, model1_rmse=%f, model2_rmse=%f, model1_psnr=%f, model2_psnr=%f \r",
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i, #x,
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+ model1_time,
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+ model2_time,
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math.sqrt(model1_mse / i), math.sqrt(model2_mse / i),
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model1_psnr / i, model2_psnr / i
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))
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@@ -261,15 +286,17 @@ local function benchmark(opt, x, input_func, model1, model2)
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else
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if baseline_output then
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io.stdout:write(
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- string.format("%d/%d; baseline_rmse=%f, model1_rmse=%f, baseline_psnr=%f, model1_psnr=%f \r",
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+ string.format("%d/%d; model1_time=%.2f, baseline_rmse=%f, model1_rmse=%f, baseline_psnr=%f, model1_psnr=%f \r",
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i, #x,
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+ model1_time,
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math.sqrt(baseline_mse / i), math.sqrt(model1_mse / i),
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baseline_psnr / i, model1_psnr / i
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))
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else
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io.stdout:write(
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- string.format("%d/%d; model1_rmse=%f, model1_psnr=%f \r",
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+ string.format("%d/%d; model1_time=%.2f, model1_rmse=%f, model1_psnr=%f \r",
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i, #x,
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+ model1_time,
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math.sqrt(model1_mse / i), model1_psnr / i
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))
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end
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@@ -285,12 +312,12 @@ local function benchmark(opt, x, input_func, model1, model2)
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math.sqrt(baseline_mse / #x), baseline_psnr / #x))
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end
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if model1_psnr > 0 then
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- fp:write(string.format("model1 : RMSE = %.3f, PSNR = %.3f\n",
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- math.sqrt(model1_mse / #x), model1_psnr / #x))
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+ fp:write(string.format("model1 : RMSE = %.3f, PSNR = %.3f, evaluation time = %.3f\n",
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+ math.sqrt(model1_mse / #x), model1_psnr / #x, model1_time))
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end
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if model2_psnr > 0 then
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- fp:write(string.format("model2 : RMSE = %.3f, PSNR = %.3f\n",
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- math.sqrt(model2_mse / #x), model2_psnr / #x))
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+ fp:write(string.format("model2 : RMSE = %.3f, PSNR = %.3f, evaluation time = %.3f\n",
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+ math.sqrt(model2_mse / #x), model2_psnr / #x, model2_time))
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end
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fp:close()
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end
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nagadomi