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evaluation.lua
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require 'nn'
require 'cunn'
require 'cudnn'
require 'torchx'
ffi = require 'ffi'
require 'image'
t = require 'datasets/transforms'
--matio = require 'matio'
function parse(arg)
local cmd = torch.CmdLine()
cmd = torch.CmdLine()
cmd:text()
cmd:text('Torch-7 DLDL-v2 Evaluation script')
cmd:text('See https://github.com/facebook/fb.resnet.torch/blob/master/TRAINING.md for examples')
cmd:text()
cmd:text('Options:')
------------ General options --------------------
cmd:option('-nGPU', 1, 'Number of GPUs to use by default')
cmd:option('-gen', "./gen", 'Path to save generated files')
cmd:option('-netType', 'resnet', 'Options: resnet | preresnet')
------------- Data options ------------------------
cmd:option('-nThreads', 10, 'number of data loading threads')
cmd:option('-tensorType', 'torch.CudaTensor', 'Options: ld | histc | kde')
cmd:option('-dataset', 'chalearn15', 'number of data loading threads')
cmd:option('-dataAug', 'true', 'Options: true or false')
cmd:option('-lambda', 1, 'the hyper-parameter between kl and l1,Options: 1 or 0.1 or 0.01')
cmd:option('-labelStep', 1, 'the interval of two neighborhood labels')
---------- Model options ----------------------------------
cmd:option('-loss', 'ldkl', 'Options: kl or l1 or sm or sl1')
cmd:option('-CR', 0.5, 'Compression rate: 1|1/2 | 1/4 | 1/8')
cmd:text()
local opt = cmd:parse(arg or {})
return opt
end
opt = parse(arg)
--labelset = torch.range(0,100,opt.labelStep):float()
if opt.dataset == 'scut-fbp' or opt.dataset == 'scut-fbp5500' or
opt.dataset == 'scut-fbp5500_1' or
opt.dataset == 'scut-fbp5500_2' or
opt.dataset == 'scut-fbp5500_3' or
opt.dataset == 'scut-fbp5500_4' or
opt.dataset == 'scut-fbp5500_5' then
labelset = torch.range(1, 5, 0.1):float()
modelname = 'AttNet-SCUTFBP.t7'
elseif opt.dataset == 'cfd' then
labelset = torch.range(1, 7, 0.1):float()
modelname = 'AttNet-CFD.t7'
elseif opt.dataset == 'chalearn15' then
labelset = torch.range(0, 100, 1):float()
modelname = 'AgeNet-ChaLearn15.t7'
elseif opt.dataset == 'chalearn16' then
labelset = torch.range(0, 100, 1):float()
modelname = 'AgeNet-ChaLearn16.t7'
end
if opt.CR == 0.5 then
modelname = 'Thin'..modelname
elseif opt.CR == 0.25 then
modelname = 'Tiny'..modelname
end
local outDim
if opt.loss == 'l1' or opt.loss == 'l2' then
outDim = 1
elseif opt.loss == 'rankbce' or opt.loss == 'rankmse' then
outDim = labelset:size(1) -1
elseif opt.loss == 'sm' or opt.loss == 'ldkl' or 'ldklexpl1' or 'ldklexpsmoothl1'then
outDim = labelset:size(1)
end
print(opt)
-- Model loading
-- rootPath = paths.concat('./Training-Models',opt.dataset, opt.loss..'-'..opt.netType..'-CR'..opt.CR..'-Aug'..opt.dataAug..'-Step'..opt.labelStep)
rootPath = './DLDL-v2Models/OriginModel'
-- modelPath = paths.concat(rootPath, 'model_60.t7')
modelPath = paths.concat(rootPath, modelname)
assert(paths.filep(modelPath), 'File not found: ' .. modelPath)
print('Loading model from file: ' .. modelPath)
net = torch.load(modelPath):type(opt.tensorType)
print(net)
-- Data loading
--trainLoader, valLoader = DataLoader.create(opt)
dataPath = paths.concat(opt.gen, opt.dataset .. '.t7')
data = torch.load(dataPath)
--print(data)
-- forward
timer = torch.Timer()
dataTimer = torch.Timer()
dataTime = dataTimer:time().real
nCrops =1
meanstd = {
mean = { 0.5958, 0.4637, 0.4065 },
std = { 0.2693, 0.2409, 0.2352 },
}
result = {}
if opt.loss == 'ldklexpl1' or 'ldklexpsmoothl1' then
result.score = {torch.FloatTensor(data.val.imagePath:size(1), outDim),
torch.FloatTensor(data.val.imagePath:size(1), outDim),}
result.scoreb = {torch.FloatTensor(data.val.imagePath:size(1), 1),
torch.FloatTensor(data.val.imagePath:size(1), 1),}
else
result.score = {torch.FloatTensor(data.val.imagePath:size(1), outDim),
torch.FloatTensor(data.val.imagePath:size(1), outDim),}
end
result.runTime = {torch.FloatTensor(data.val.imagePath:size(1)):fill(0),
torch.FloatTensor(data.val.imagePath:size(1)):fill(0),}
result.pred = {torch.FloatTensor(data.val.imagePath:size(1)):fill(0),
torch.FloatTensor(data.val.imagePath:size(1)):fill(0),}
trans = {}
trans[1] = t.Compose{
t.Scale(224),
t.CenterCrop(224),
t.ColorNormalize(meanstd),}
trans[2] = t.Compose{
t.Scale(224),
t.CenterCrop(224),
t.HorizontalFlip(1),
t.ColorNormalize(meanstd),}
net:evaluate()
for iter = 1, 2 do
if iter == 1 then
print('original prediction')
elseif iter ==2 then
print('flip prefiction')
end
local te = trans[iter]
for i = 1, data.val.imagePath:size(1) do
imgpath = paths.concat(data.basedir, ffi.string(data.val.imagePath[i]:data()))
img = image.load(imgpath, 'float')
img2 = te(img):cuda()
timer = torch.Timer()
outs = net:forward(img2)
if i%1000 ==0 then
print((' |image: %d, Time %.3f '):
format(i, timer:time().real))
end
timer:reset()
result.runTime[iter][i] = timer:time().real
if opt.loss == 'ldklexpl1' or opt.loss == 'ldklexpsmoothl1'then
local out1, out2 = unpack(outs)
result.score[iter][i], result.scoreb[iter][i] = out1:float(), out2:float()
else
result.score[iter][i] = outs:float()
end
end
end
-- evaluation
class = data.val.imageClass:clone()
sigma = data.val.imageSigma:clone()
inds = torch.LongTensor(torch.find(sigma, 0))
if inds:dim() >=1 then
sigma:indexFill(1, inds:long(), 1e-10)
end
local minl, maxl = labelset:min(), labelset:max()
for iter = 1, 2 do
if opt.loss == 'l1' or opt.loss == 'l2' then
-- regression
f = (maxl-minl)/(1+1)
result.pred[iter] = ((result.score[iter] +1):mul(f) + minl)
elseif opt.loss == 'sm' or opt.loss == 'ldkl' then
-- dex, dldl,
result.pred[iter] = result.score[iter]:float()*(labelset)
elseif opt.loss == 'rankbce' or opt.loss == 'rankmse' then
-- rank
local sumcount = (result.score[iter]:float():ge(0.5):sum(2)):float() + 1
result.pred[iter] = labelset:index(1, sumcount:long():squeeze())
--result.pred[iter] = result.score[iter]:float():ge(.5):sum(2):float()
elseif opt.loss == 'ldklexpl1' or opt.loss == 'ldklexpl2' or 'ldklexpsmoothl1'then
result.pred[iter] = result.scoreb[iter]:float()
end
end
for iter = 1, 3 do
if iter == 3 then
pred = (result.pred[1]:squeeze() + result.pred[2]:squeeze())/2
time = ((result.runTime[1] + result.runTime[2])/2):mean()
else
pred = result.pred[iter]:squeeze()
time = result.runTime[iter]:mean()
end
mae = (pred-class):abs():mean()
cs3 = (pred-class):abs():le(3):sum()/pred:size(1)*100
cs5 = (pred-class):abs():le(5):sum()/pred:size(1)*100
cs8 = (pred-class):abs():le(8):sum()/pred:size(1)*100
error = (1 - (pred - class):cdiv(sigma):pow(2):mul(-0.5):exp()):mean()
if iter ==1 then
print(('-- %s loss, labelStep %.2f'):format(opt.loss, opt.labelStep))
end
if iter == 3 then
print(('fusion | mae %.3f, error %.3f, cs3 %.3f, cs5 %.3f, cs8 %.3f, runtime %.5fs'):format(mae, error, cs3, cs5, cs8, time));
else
print(('%d-th iter | mae %.3f, error %.3f, cs3 %.3f, cs5 %.3f, cs8 %.3f, runtime %.5fs'):format(iter, mae, error, cs3,cs5,cs8, time));
end
end
result.class = class
result.sigma = sigma
torch.save(paths.concat(rootPath,'result.t7'), result)
--[[matio.save(paths.concat(rootPath,'result.mat'), {class= result.class,
sigma = result.sigma,
pred1 = result.pred[1],
pred2 = result.pred[2],
runTime1 = result.runTime[1],
runTime2 = result.runTime[2],
score1 = result.score[1],
score2 = result.score[2],
})]]