----------------------------------------------------------------------

-- This script implements a test procedure, to report accuracy

-- on the test data. Nothing fancy here...

--

-- Clement Farabet

----------------------------------------------------------------------

require 'torch' -- torch

require 'xlua' -- xlua provides useful tools, like progress bars

require 'optim' -- an optimization package, for online and batch methods

----------------------------------------------------------------------

print '==> defining test procedure'

-- test function

function test()

-- local vars

local time = sys.clock()

-- averaged param use?

if average then

cachedparams = parameters:clone()

parameters:copy(average)

end

-- set model to evaluate mode (for modules that differ in training and testing, like Dropout)

model:evaluate()

-- test over test data

print('==> testing on test set:')

for t = 1,testData:size() do

-- disp progress

xlua.progress(t, testData:size())

-- get new sample

local input = testData.data[t]

if opt.type == 'double' then input = input:double()

elseif opt.type == 'cuda' then input = input:cuda() end

local target = testData.labels[t]

-- test sample

local pred = model:forward(input)

confusion:add(pred, target)

end

-- timing

time = sys.clock() - time

time = time / testData:size()

print("\n==> time to test 1 sample = " .. (time*1000) .. 'ms')

-- print confusion matrix

print(confusion)

-- update log/plot

testLogger:add{['% mean class accuracy (test set)'] = confusion.totalValid * 100}

if opt.plot then

testLogger:style{['% mean class accuracy (test set)'] = '-'}

testLogger:plot()

end

-- averaged param use?

if average then

-- restore parameters

parameters:copy(cachedparams)

end

-- next iteration:

confusion:zero()

end