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#!/usr/bin/env th
--
-- Outputs the number of parameters in a network for a single image
-- in evaluation mode.
require 'torch'
require 'nn'
require 'dpnn'
torch.setdefaulttensortype('torch.FloatTensor')
local cmd = torch.CmdLine()
cmd:text()
cmd:text('Network Size.')
cmd:text()
cmd:text('Options:')
cmd:option('-model', './models/openface/nn4.small2.v1.t7', 'Path to model.')
cmd:option('-imgDim', 96, 'Image dimension. nn1=224, nn4=96')
cmd:option('-numIter', 500)
cmd:option('-cuda', false)
cmd:text()
local opt = cmd:parse(arg or {})
-- print(opt)
local net = torch.load(opt.model):float()
net:evaluate()
local img = torch.randn(opt.numIter, 1, 3, opt.imgDim, opt.imgDim)
if opt.cuda then
require 'cutorch'
require 'cunn'
net = net:cuda()
img = img:cuda()
end
local times = torch.Tensor(opt.numIter)
for i=1,opt.numIter do
local timer = torch.Timer()
local _ = net:forward(img[i])
times[i] = 1000.0*timer:time().real
end
print(string.format('Single image forward pass: %.2f ms +/- %.2f ms',
torch.mean(times), torch.std(times)))