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

--require('mobdebug').start()

require 'torch' -- torch

require 'nn'

require 'rnn'

require 'xlua'

dofile ('model/structured_hinge_loss.lua') -- new loss function

d = dofile('common/data.lua')

m = dofile('model/model.lua')

eval = dofile('evaluate.lua')

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

if not opt then

print '==> processing options'

cmd = torch.CmdLine()

cmd:text()

cmd:text('Bi-RNN for Audio Segmentation')

cmd:text()

cmd:text('Options:')

-- general

cmd:option('-seed', 1234, 'the seed to generate numbers')

-- data

cmd:option('-features_path', 'data/word_duration/test/', 'the path to the features file')

cmd:option('-labels_path', 'data/word_duration/test/', 'the path to the labels file')

cmd:option('-input_dim', 13, 'the input size')

-- train

cmd:option('-model', 'results/model.net', 'the path to the model directory')

cmd:option('-type', 'double', 'data type: double | cuda')

-- loss

cmd:option('-eps', 2, 'the tolerance value for the loss function')

cmd:text()

opt = cmd:parse(arg or {})

end

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

torch.manualSeed(opt.seed)

local eps = 2

d:new()

print '==> Loading data set'

x, y, f_n = d:read_data(opt.features_path, opt.labels_path, opt.input_dim, 'test.t7')

print '==> define loss'

criterion = nn.StructuredHingeLoss(eps)

print(criterion)

print '==> loading model'

model = torch.load(opt.model)

print(model)

print '==> predict '

local loss, score = eval:evaluate(model, criterion, x, y, f_n, true)