Any details when converting txt to t7 format?

[iinjuly]

Hi,
We want to run your model with other datasets. To do this, according to instructions provided, we need to convert our text into t7 format. We wrote a script to implement the conversion and tested our script on a txt sample in coco dataset, but our script just got different results compared with the txt sample's ccorresponding t7 data you provided.
Here is our conversion script:

`require 'image'
require 'nn'
require 'nngraph'
require 'cunn'
require 'cutorch'
require 'cudnn'
require 'lfs'
torch.setdefaulttensortype('torch.FloatTensor')

local alphabet = "abcdefghijklmnopqrstuvwxyz0123456789-,;.!?:'"/\|_@#$%^&*~`+-=<>()[]{} "
local dict = {}
for i = 1,#alphabet do
dict[alphabet:sub(i,i)] = i
end
ivocab = {}
for k,v in pairs(dict) do
ivocab[v] = k
end

opt = {
filenames = '',
dataset = 'cub',
batchSize = 16, -- number of samples to produce
noisetype = 'normal', -- type of noise distribution (uniform / normal).
imsize = 1, -- used to produce larger images. 1 = 64px.mg_demo.lua 2 = 80px, 3 = 96px, ...
noisemode = 'random', -- random / line / linefull1d / linefull
gpu = 1, -- gpu mode. 0 = CPU, 1 = GPU
display = 0, -- Display image: 0 = false, 1 = true
nz = 100,
doc_length = 201,
queries = 'test-caption.txt',
checkpoint_dir = '',
net_gen = '',
net_txt = '',
}

for k,v in pairs(opt) do opt[k] = tonumber(os.getenv(k)) or os.getenv(k) or opt[k] end
print(opt)
if opt.display == 0 then opt.display = false end

noise = torch.Tensor(opt.batchSize, opt.nz, opt.imsize, opt.imsize)
net_gen = torch.load(opt.checkpoint_dir .. '/' .. opt.net_gen)
net_txt = torch.load(opt.net_txt)
if net_txt.protos ~=nil then net_txt = net_txt.protos.enc_doc end

net_gen:evaluate()
net_txt:evaluate()

-- Extract all text features.
local fea_txt = torch.Tensor(5,1024)
idx=1
-- Decode text for sanity check.
local raw_txt = {}
local raw_img = {}
for query_str in io.lines(opt.queries) do
local txt = torch.zeros(1,opt.doc_length,#alphabet)
for t = 1,opt.doc_length do
local ch = query_str:sub(t,t)
local ix = dict[ch]
if ix ~= 0 and ix ~= nil then
txt[{1,t,ix}] = 1
end
end
raw_txt[#raw_txt+1] = query_str
txt = txt:cuda()
print('idx = ,', idx,'txt size',txt:size())
print("query_str = ",query_str)
tmp = net_txt:forward(txt):float():clone()
fea_txt[idx] = tmp
idx = idx + 1
print('tmp size',tmp:size())
end

torch.save('fea-txt.t7',fea_txt)
`

Why we can't got the exactly same t7 result with the provided one? Did we miss some detail? Any directions or hints would be appreciated.

[xhzhao]

+1, for the txt to .t7 Preprocessing

[yeates]

Can u figure out this now?
It should be a common problem, because everyone will meet the t7 format obstacle when they try to train themselves caption data. Maybe it's just a simple question...Give some hints or tricks Please ORZ

[SreenijaK]

@iinjuly were you able to process the txt file to .t7 format?