def _save(self, phase, epoch):
"""Save model state dict and generator's samples"""
if phase != 'ADV':
torch.save(self.gen.state_dict(), cfg.save_model_root + 'gen_{}_{:05d}.pt'.format(phase, epoch))
save_sample_path = cfg.save_samples_root + 'samples_{}_{:05d}.txt'.format(phase, epoch)
samples = self.gen.sample(10000, cfg.batch_size)
write_tokens(save_sample_path, tensor_to_tokens(samples, self.idx2word_dict))
def _save(self, phase, epoch):
torch.save(
self.gen.state_dict(),
cfg.save_model_root + 'gen_{}_{:05d}.pt'.format(phase, epoch))
save_sample_path = cfg.save_samples_root + 'samples_{}_{:05d}.txt'.format(
phase, epoch)
samples = self.gen.sample(cfg.batch_size, cfg.batch_size, self.dis)
write_tokens(save_sample_path,
tensor_to_tokens(samples, self.idx2word_dict))
def _save(self, phase, epoch):
"""Save model state dict and generator's samples"""
for i in range(cfg.k_label):
if phase != 'ADV':
torch.save(self.gen_list[i].state_dict(),
cfg.save_model_root + 'gen{}_{}_{:05d}.pt'.format(i, phase, epoch))
save_sample_path = cfg.save_samples_root + 'samples_d{}_{}_{:05d}.txt'.format(i, phase, epoch)
samples = self.gen_list[i].sample(cfg.batch_size, cfg.batch_size)
write_tokens(save_sample_path, tensor_to_tokens(samples, self.idx2word_dict))
def _save(self, phase, epoch, label_i=None):
assert type(label_i) == int
torch.save(
self.gen.state_dict(),
cfg.save_model_root + 'gen_{}_{:05d}.pt'.format(phase, epoch))
save_sample_path = cfg.save_samples_root + 'samples_c{}_{}_{:05d}.txt'.format(
label_i, phase, epoch)
samples = self.gen.sample(cfg.batch_size,
cfg.batch_size,
label_i=label_i)
write_tokens(save_sample_path,
tensor_to_tokens(samples, self.idx2word_dict))
def leakGAN_layer(self, secret_file, final_file, bins_num):
#Second Layer = LeakGAN layer
print('>>> Begin Second Layer...')
data_root = './decode/'
torch.nn.Module.dump_patches = True
epoch_start_time = time.time()
# Set the random seed manually for reproducibility.
seed = 1111
#Step 1: load the most accurate model
with open("leakGAN_instructor/real_data/gen_ADV_00028.pt", 'rb') as f:
self.gen.load_state_dict(torch.load(f))
print("Finish Loading")
self.gen.eval()
#Step 1: Get Intermediate text
secret_file = data_root + secret_file
secret_file = open(secret_file, 'r')
secret_data = secret_file.read().split()
#Step 2: Compress string into binary string
bit_string = ''
#You need LSTM Corpus for that
emnlp_data = 'dataset/emnlp_news/'
corpus = dataa.Corpus(emnlp_data)
for data in secret_data:
print("Data: {}".format(data))
idWord = corpus.dictionary.word2idx[data]
bit_string += '{0:{fill}13b}'.format(int(idWord), fill='0')
secret_text = [
int(i, 2) for i in self.string2bins(bit_string, bins_num)
] #convert to bins
corpus_leak = self.index_word_dict
if bins_num >= 2:
ntokens = len(corpus_leak)
tokens = list(range(ntokens)) # * args.replication_factor
#print(ntokens)
random.shuffle(tokens)
#Words in each bin
words_in_bin = int(ntokens / bins_num)
#leftovers should be also included in the
leftover = int(ntokens % bins_num)
bins = [
tokens[i:i + words_in_bin]
for i in range(0, ntokens - leftover, words_in_bin)
] # words to keep in each bin
for i in range(0, leftover):
bins[i].append(tokens[i + words_in_bin * bins_num])
#save bins into leakGAN key
key2 = data_root + 'leakGAN_key.txt'
with open(key2, "wb") as fp: #Pickling
pickle.dump(bins, fp)
zero = [list(set(tokens) - set(bin_)) for bin_ in bins]
print('Finished Initializing Second LeakGAN Layer')
print('time: {:5.2f}s'.format(time.time() - epoch_start_time))
print('-' * 89)
out_file = data_root + final_file
w = 0
i = 1
bin_sequence_length = len(secret_text[:])
print("bin sequence length", bin_sequence_length)
batch_size = cfg.batch_size
seq_len = cfg.max_seq_len
feature_array = torch.zeros(
(batch_size, seq_len + 1, self.gen.goal_out_size))
goal_array = torch.zeros(
(batch_size, seq_len + 1, self.gen.goal_out_size))
leak_out_array = torch.zeros((batch_size, seq_len + 1, cfg.vocab_size))
samples = torch.zeros(batch_size, seq_len + 1).long()
work_hidden = self.gen.init_hidden(batch_size)
mana_hidden = self.gen.init_hidden(batch_size)
leak_inp = torch.LongTensor([cfg.start_letter] * batch_size)
real_goal = self.gen.goal_init[:batch_size, :]
if cfg.CUDA:
feature_array = feature_array.cuda()
goal_array = goal_array.cuda()
leak_out_array = leak_out_array.cuda()
goal_array[:, 0, :] = real_goal # g0 = goal_init
if_sample = True
no_log = False
index = cfg.start_letter
while i <= seq_len:
dis_inp = torch.zeros(batch_size, bin_sequence_length).long()
if i > 1:
dis_inp[:, :i - 1] = samples[:, :i - 1] # cut sentences
leak_inp = samples[:, i - 2]
if torch.cuda.is_available():
dis_inp = dis_inp.cuda()
leak_inp = leak_inp.cuda()
feature = self.dis.get_feature(dis_inp).unsqueeze(0)
#print(feature)
feature_array[:, i - 1, :] = feature.squeeze(0)
out, cur_goal, work_hidden, mana_hidden = self.gen(index,
leak_inp,
work_hidden,
mana_hidden,
feature,
real_goal,
no_log=no_log,
train=False)
leak_out_array[:, i - 1, :] = out
goal_array[:, i, :] = cur_goal.squeeze(1)
if i > 0 and i % self.gen.step_size == 0:
real_goal = torch.sum(goal_array[:, i - 3:i + 1, :], dim=1)
if i / self.gen.step_size == 1:
real_goal += self.gen.goal_init[:batch_size, :]
# Sample one token
if not no_log:
out = torch.exp(out)
zero_index = zero[secret_text[:][
i -
1]] #indecies that has to be zeroed, as they are not in the current bin
#zero_index.append(0)
zero_index = torch.LongTensor(zero_index)
if cfg.CUDA:
zero_index = zero_index.cuda()
temperature = 1.5
word_weights = out
word_weights = word_weights.index_fill_(
1, zero_index,
0) #make all the indecies zero if they are not in the bin
word_weights = torch.multinomial(word_weights, 1).view(
-1) #choose one word with highest probability for each sample
#print("Out after: {}".format(word_weights))
samples[:, i] = word_weights
leak_inp = word_weights
i += 1
w += 1
leak_out_array = leak_out_array[:, :seq_len, :]
tokens = []
write_tokens(out_file, tensor_to_tokens(samples, self.index_word_dict))
print("Generated final steganographic text")
print("Final steganographic text saved in following file: {}".format(
out_file))
