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classify_sje_tcnn.lua
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classify_sje_tcnn.lua
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-- Necessary functionalities
require 'nn'
require 'nngraph'
require 'cutorch'
require 'cunn'
require 'cudnn'
local model_utils = require('util.model_utils')
cutorch.setDevice(1)
-- Encode query document using alphabet.
local alphabet = "abcdefghijklmnopqrstuvwxyz0123456789-,;.!?:'\"/\\|_@#$%^&*~`+-=<>()[]{} "
local dict = {}
for i = 1,#alphabet do
dict[alphabet:sub(i,i)] = i
end
-------------------------------------------------
cmd = torch.CmdLine()
cmd:option('-data_dir','data','data directory.')
cmd:option('-image_dir','images','image subdirectory.')
cmd:option('-txt_dir','','text subdirectory.')
cmd:option('-savefile','sje_tcnn','filename to autosave the checkpont to. Will be inside checkpoint_dir/')
cmd:option('-checkpoint_dir', 'cv', 'output directory where checkpoints get written')
cmd:option('-symmetric',1,'symmetric sje')
cmd:option('-learning_rate',0.0001,'learning rate')
cmd:option('-testclasses', 'testclasses.txt', 'validation or test classes to be used in evaluation')
cmd:option('-ids_file', 'trainvalids.txt', 'file specifying which class labels were used for training.')
cmd:option('-model','','model to load. If blank then above options will be used.')
cmd:option('-txt_limit',0,'if 0 then use all available text. Otherwise limit the number of documents per class')
cmd:option('-num_caption',10,'number of captions per image to be used for training')
cmd:option('-ttype','char','word|char')
cmd:option('-gpuid',0,'gpu to use')
opt = cmd:parse(arg)
local model
if opt.model ~= '' then
model = torch.load(opt.model)
else
model = torch.load(string.format('%s/lm_%s_%.5f_%.0f_%.0f_%s.t7', opt.checkpoint_dir, opt.savefile, opt.learning_rate, opt.symmetric, opt.num_caption, opt.ids_file))
end
-----------------------------------------------------------
if opt.gpuid >= 0 then
cutorch.setDevice(opt.gpuid+1)
end
local doc_length = model.opt.doc_length
local protos = model.protos
protos.enc_doc:evaluate()
protos.enc_image:evaluate()
function extract_img(filename)
local data = torch.load(filename)
if data:size():size() == 3 then
local fea = data[{{},{},1}]
fea = fea:float():cuda()
local out = protos.enc_image:forward(fea)
return out:float()
else
local fea = data[{{},{},{},{},1}]
fea = fea:float():cuda()
local out = protos.enc_image:forward(fea)
return out:float()
end
end
function extract_txt(filename)
if opt.ttype == 'word' then
return extract_txt_word(filename)
else -- char
return extract_txt_char(filename)
end
end
function extract_txt_word(filename)
-- average all text features together.
--local txt = torch.load(filename):permute(1,3,2):add(1)
local txt = torch.load(filename):permute(1,3,2)
txt = txt:reshape(txt:size(1)*txt:size(2),txt:size(3)):float():cuda()
if opt.txt_limit > 0 then
local actual_limit = math.min(txt:size(1), opt.txt_limit)
txt_order = torch.randperm(txt:size(1)):sub(1,actual_limit)
local tmp = txt:clone()
for i = 1,actual_limit do
txt[{i,{}}]:copy(tmp[{txt_order[i],{}}])
end
txt = txt:narrow(1,1,actual_limit)
end
if (model.opt.num_repl ~= nil) then
tmp = txt:clone()
txt = torch.ones(txt:size(1),model.opt.num_repl*txt:size(2))
for i = 1,txt:size(1) do
local cur_sen = torch.squeeze(tmp[{i,{}}]):clone()
local cur_len = cur_sen:size(1) - cur_sen:eq(1):sum()
local txt_ix = 1
for j = 1,cur_len do
for k = 1,model.opt.num_repl do
txt[{i,txt_ix}] = cur_sen[j]
txt_ix = txt_ix + 1
end
end
end
end
local txt_mat = torch.zeros(txt:size(1), txt:size(2), vocab_size+1)
for i = 1,txt:size(1) do
for j = 1,txt:size(2) do
local on_ix = txt[{i, j}]
if on_ix == 0 then
break
end
txt_mat[{i, j, on_ix}] = 1
end
end
txt_mat = txt_mat:float():cuda()
local out = protos.enc_doc:forward(txt_mat)
out = torch.mean(out,1):float()
return out
end
function extract_txt_char(filename)
-- average all text features together.
local txt = torch.load(filename):permute(1,3,2)
txt = txt:reshape(txt:size(1)*txt:size(2),txt:size(3)):float():cuda()
if opt.txt_limit > 0 then
local actual_limit = math.min(txt:size(1), opt.txt_limit)
txt_order = torch.randperm(txt:size(1)):sub(1,actual_limit)
local tmp = txt:clone()
for i = 1,actual_limit do
txt[{i,{}}]:copy(tmp[{txt_order[i],{}}])
end
txt = txt:narrow(1,1,actual_limit)
end
local txt_mat = torch.zeros(txt:size(1), txt:size(2), #alphabet)
for i = 1,txt:size(1) do
for j = 1,txt:size(2) do
local on_ix = txt[{i, j}]
if on_ix == 0 then
break
end
txt_mat[{i, j, on_ix}] = 1
end
end
txt_mat = txt_mat:float():cuda()
local out = protos.enc_doc:forward(txt_mat)
return torch.mean(out,1):float()
end
function classify(txt_dir, img_dir, cls_list)
local acc = 0.0
local total = 0.0
local fea_img = {}
local fea_txt = {}
for fname in io.lines(cls_list) do
local imgpath = img_dir .. '/' .. fname .. '.t7'
local txtpath = txt_dir .. '/' .. fname .. '.t7'
fea_img[#fea_img + 1] = extract_img(imgpath)
fea_txt[#fea_txt + 1] = extract_txt(txtpath)
end
for i = 1,#fea_img do
-- loop over individual images.
for k = 1,fea_img[i]:size(1) do
local best_match = 1
local best_score = -math.huge
for j = 1,#fea_txt do
local cur_score = torch.dot(fea_img[i][{k,{}}], fea_txt[j])
if cur_score > best_score then
best_match = j
best_score = cur_score
end
end
if best_match == i then
acc = acc + 1
end
total = total + 1
end
end
return acc / total
end
local txt_dir
if opt.txt_dir == '' then
if opt.ttype == 'char' then
txt_dir = string.format('%s/text_c%d', opt.data_dir, opt.num_caption)
else -- word
txt_dir = string.format('%s/word_c%d', opt.data_dir, opt.num_caption)
end
else
txt_dir = string.format('%s/%s', opt.data_dir, opt.txt_dir)
end
if opt.ttype == 'word' then
vocab_size = 0
for k,v in pairs(model.vocab) do
vocab_size = vocab_size + 1
end
end
local img_dir = string.format('%s/%s', opt.data_dir, opt.image_dir)
local testcls = string.format('%s/%s', opt.data_dir, opt.testclasses)
local test_acc = classify(txt_dir, img_dir, testcls)
print(string.format('Average top-1 val/test accuracy: %6.4f\n', test_acc))