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Fix a issue of time comparison in benchmark when using cuDNN

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nagadomi 2016-07-21 18:04:01 +09:00
parent b3c968ef11
commit 5786099bc8
1 changed files with 42 additions and 1 deletions
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43
tools/benchmark.lua
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@ -183,7 +183,7 @@ local function benchmark(opt, x, input_func, model1, model2)
x, block_size, batch_size)
end
end
for i = 1, #x do
local ground_truth = x[i].image
local basename = x[i].basename
@ -191,6 +191,12 @@ local function benchmark(opt, x, input_func, model1, model2)
input = input_func(ground_truth, opt)
if opt.method == "scale" then
if opt.force_cudnn and i == 1 then -- run cuDNN benchmark first
model1_output = scale_f(model1, 2.0, input, opt.crop_size, opt.batch_size)
if model2 then
model2_output = scale_f(model2, 2.0, input, opt.crop_size, opt.batch_size)
end
end
t = sys.clock()
model1_output = scale_f(model1, 2.0, input, opt.crop_size, opt.batch_size)
model1_time = model1_time + (sys.clock() - t)
@ -201,6 +207,12 @@ local function benchmark(opt, x, input_func, model1, model2)
end
baseline_output = baseline_scale(input, opt.baseline_filter)
elseif opt.method == "noise" then
if opt.force_cudnn and i == 1 then -- run cuDNN benchmark first
model1_output = image_f(model1, input, opt.crop_size, opt.batch_size)
if model2 then
model2_output = image_f(model2, input, opt.crop_size, opt.batch_size)
end
end
t = sys.clock()
model1_output = image_f(model1, input, opt.crop_size, opt.batch_size)
model1_time = model1_time + (sys.clock() - t)
@ -211,6 +223,35 @@ local function benchmark(opt, x, input_func, model1, model2)
end
baseline_output = input
elseif opt.method == "noise_scale" then
if opt.force_cudnn and i == 1 then -- run cuDNN benchmark first
if model1.noise_scale_model then
model1_output = scale_f(model1.noise_scale_model, 2.0,
input, opt.crop_size, opt.batch_size)
else
if model1.noise_model then
model1_output = image_f(model1.noise_model, input, opt.crop_size, opt.batch_size)
else
model1_output = input
end
model1_output = scale_f(model1.scale_model, 2.0, model1_output,
opt.crop_size, opt.batch_size)
end
if model2 then
if model2.noise_scale_model then
model2_output = scale_f(model2.noise_scale_model, 2.0,
input, opt.crop_size, opt.batch_size)
else
if model2.noise_model then
model2_output = image_f(model2.noise_model, input,
opt.crop_size, opt.batch_size)
else
model2_output = input
end
model2_output = scale_f(model2.scale_model, 2.0, model2_output,
opt.crop_size, opt.batch_size)
end
end
end
t = sys.clock()
if model1.noise_scale_model then
model1_output = scale_f(model1.noise_scale_model, 2.0,