/
demo_cub_move_bbox.lua
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/
demo_cub_move_bbox.lua
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require 'image'
require 'nn'
require 'nngraph'
require 'cunn'
require 'cudnn'
require 'cutorch'
require 'lfs'
require 'stn'
util = paths.dofile('util.lua')
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 = {
dataset = 'cub',
doc_length = 201,
prefix = 'move',
batchSize = 1, -- number of samples to produce
noisetype = 'normal', -- type of noise distribution (uniform / normal).
imsize = 1, -- used to produce larger images. 1 = 64px. 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,
net_gen = '',
net_txt = '',
num_elt = 1,
fineSize = 128,
txtSize = 1024,
txt_file = 'cub_captions_move.txt',
demo = 'shrink' -- trans|stretch|shrink
}
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
assert(opt.net_gen ~= '')
assert(opt.net_txt ~= '')
net_gen = torch.load(opt.net_gen)
net_txt = torch.load(opt.net_txt).protos.enc_doc
net_gen:evaluate()
net_txt:evaluate()
function decode(txt)
local str = ''
for w_ix = 1,txt:size(1) do
local ch_ix = txt[w_ix]
local ch = ivocab[ch_ix]
if (ch ~= nil) then
str = str .. ch
end
end
return str
end
local num_loc
local sx, sx_step
local sy, sy_step
local x, x_step
local y, y_step
if opt.demo == 'trans' then
num_loc = 3
sx = 2
sx_step = 0
sy = 2
sy_step = 0
y = 0
y_step = 0
x = -0.7
x_step = 0.7
elseif opt.demo == 'stretch' then
num_loc = 3
sx = 2
sx_step = -0.5
sy = 2
sy_step = 0
y = 0
y_step = 0.0
x = 0
x_step = 0
elseif opt.demo == 'shrink' then
num_loc = 3
sx = 1.25
sx_step = 0.5
sy = 1.25
sy_step = 0.5
y = 0
y_step = 0.0
x = 0
x_step = 0
else
assert(false)
end
local fea_loc = torch.zeros(num_loc, 1, 2, 3)
local bbox = torch.zeros(4, num_loc)
local sz = torch.zeros(3)
sz[1] = 3
sz[2] = 128
sz[3] = 128
for n = 1,num_loc do
local xpos = x + (n-1)*x_step
local ypos = y + (n-1)*y_step
local tx = xpos
local ty = ypos
local cur_sx = sx + (n-1)*sx_step
local cur_sy = sy + (n-1)*sy_step
-- local box
fea_loc[{n, 1, 1, 1}] = cur_sx
fea_loc[{n, 1, 2, 2}] = cur_sy
fea_loc[{n, 1, 1, 3}] = tx
fea_loc[{n, 1, 2, 3}] = ty
local tmp_loc = util.invert_affine(fea_loc[n]:clone())
bbox[{{},n}]:copy(util.affine_to_bbox(sz, tmp_loc)[{{},1}])
end
fea_loc = fea_loc:cuda()
local html = '<html><body><h1>Generated Images</h1><table border="1" style="width=100%"><tr><td>Caption</td><td>Image</td></tr>'
local count = 1
for query in io.lines(opt.txt_file) do
-- encode text.
query_str = string.lower(query)
print(query_str)
local txt_mat = torch.zeros(1,opt.doc_length,#alphabet)
for i = 1,#query_str do
if i > txt_mat:size(2) then
break
end
local ch = query_str:sub(i,i)
local on_ix = dict[ch]
if on_ix ~= 0 then
txt_mat[{1, i, on_ix}] = 1
end
end
txt_mat = txt_mat:float():cuda()
local fea_txt = net_txt:forward(txt_mat):clone()
fea_txt = fea_txt:reshape(1, 1, opt.txtSize)
fea_txt = torch.repeatTensor(fea_txt, num_loc, 1, 1)
noise = torch.Tensor(1, opt.num_elt, opt.nz)
if opt.noisetype == 'uniform' then
noise:uniform(-1, 1)
elseif opt.noisetype == 'normal' then
noise:normal(0, 1)
end
noise = torch.repeatTensor(noise, num_loc, 1, 1)
noise = noise:cuda()
local images = net_gen:forward({ { noise, fea_txt }, fea_loc }):clone()
images = images:narrow(1,1,3)
images:add(1):mul(0.5)
for n = 1,images:size(1) do
images[n] = util.draw_box(images[n], bbox[{{},n}], 3)
end
lfs.mkdir('results')
local visdir = 'results/cub_' .. opt.demo .. '_bbox'
lfs.mkdir(visdir)
local fname = string.format('%s/cub_%s_%d', visdir, opt.demo, count)
local fname_png = fname .. '.png'
image.save(fname_png, image.toDisplayTensor(images, 4, num_loc))
local vispath = 'cub_' .. opt.demo .. '_bbox'
local fname_rel = string.format('%s/cub_%s_%d', vispath, opt.demo, count)
fname_rel = fname_rel .. '.png'
html = html .. string.format('\n<tr><td>%s</td><td><img src="%s"></td></tr>',
query, fname_rel)
count = count + 1
end
html = html .. '</html>'
fname_html = string.format('results/%s_%s_bbox.html', opt.dataset, opt.demo)
os.execute(string.format('echo "%s" > %s', html, fname_html))