class RelbarGANInstructor(BasicInstructor):
def __init__(self, opt):
super(RelbarGANInstructor, self).__init__(opt)
# generator, discriminator
self.gen = RelbarGAN_G(cfg.mem_slots,
cfg.num_heads,
cfg.head_size,
cfg.gen_embed_dim,
cfg.gen_hidden_dim,
cfg.vocab_size,
cfg.max_seq_len,
cfg.padding_idx,
cfg.temperature,
cfg.eta,
gpu=cfg.CUDA)
self.dis = RelbarGAN_D(cfg.dis_embed_dim,
cfg.max_seq_len,
cfg.num_rep,
cfg.vocab_size,
cfg.padding_idx,
gpu=cfg.CUDA)
self.init_model()
# Optimizer
self.gen_opt = optim.Adam(self.gen.parameters(), lr=cfg.gen_lr)
self.gen_adv_opt = optim.Adam(itertools.chain(
self.gen.parameters(), [self.gen.temperature, self.gen.eta]),
lr=cfg.gen_adv_lr)
self.dis_opt = optim.Adam(self.dis.parameters(), lr=cfg.dis_lr)
# Criterion
self.mle_criterion = nn.NLLLoss()
self.dis_pretrain_criterion = nn.BCEWithLogitsLoss()
# DataLoader
self.gen_data = GenDataIter(
self.gen.sample(cfg.batch_size, cfg.batch_size))
self.dis_data = DisDataIter(self.train_data.random_batch()['target'],
self.gen_data.random_batch()['target'])
# Metrics
bleu_gram = list(range(2, cfg.max_seq_len +
1)) if cfg.max_seq_len < 5 else [2, 3, 4, 5]
self.bleu = BLEU(test_text=tensor_to_tokens(self.gen_data.target,
self.index_word_dict),
real_text=tensor_to_tokens(
self.test_data.target,
self.test_data.index_word_dict),
gram=bleu_gram)
self.self_bleu = BLEU(
test_text=tensor_to_tokens(self.gen_data.target,
self.index_word_dict),
real_text=tensor_to_tokens(self.gen_data.target,
self.index_word_dict),
gram=3)
def _run(self):
# =====PRE-TRAINING (GENERATOR)=====
if not cfg.gen_pretrain:
self.log.info('Starting Generator MLE Training...')
self.pretrain_generator(cfg.MLE_train_epoch)
if cfg.if_save and not cfg.if_test:
torch.save(self.gen.state_dict(), cfg.pretrained_gen_path)
print('Save pretrain_generator: {}'.format(
cfg.pretrained_gen_path))
# =====PRE-TRAINING (DISCRIMINATOR)=====
if not cfg.dis_pretrain:
self.log.info('Starting Discriminator Training...')
self.pretrain_discriminator(cfg.d_step, cfg.d_epoch)
if cfg.if_save and not cfg.if_test:
torch.save(self.dis.state_dict(), cfg.pretrained_dis_path)
print('Save pretrain_generator discriminator: {}'.format(
cfg.pretrained_dis_path))
# # =====ADVERSARIAL TRAINING=====
self.log.info('Starting Adversarial Training...')
self.log.info('Initial generator: %s' %
(self.cal_metrics(fmt_str=True)))
for adv_epoch in range(cfg.ADV_train_epoch):
if adv_epoch % cfg.adv_log_step == 0:
self.log.info('-----\nADV EPOCH %d\n-----' % adv_epoch)
self.sig.update()
if self.sig.adv_sig:
self.adv_train_generator(cfg.ADV_g_step,
adv_epoch) # Generator
self.adv_train_discriminator(cfg.ADV_d_step,
adv_epoch) # Discriminator
if adv_epoch % cfg.adv_log_step == 0:
if cfg.if_save and not cfg.if_test:
self._save('ADV', adv_epoch)
else:
self.log.info(
'>>> Stop by adv_signal! Finishing adversarial training...'
)
break
def _test(self):
print('>>> Begin test...')
self._run()
pass
def pretrain_generator(self, epochs):
"""
Max Likelihood Pre-training for the generator
"""
for epoch in range(epochs):
self.sig.update()
if self.sig.pre_sig:
# =====Train=====
pre_loss = self.train_gen_epoch(self.gen,
self.train_data.loader,
self.mle_criterion,
self.gen_opt)
# =====Test=====
if epoch % cfg.pre_log_step == 0 or epoch == epochs - 1:
self.log.info(
'[MLE-GEN] epoch %d : pre_loss = %.4f, %s' %
(epoch, pre_loss, self.cal_metrics(fmt_str=True)))
if cfg.if_save and not cfg.if_test:
self._save('MLE', epoch)
else:
self.log.info(
'>>> Stop by pre signal, skip to adversarial training...')
break
if cfg.if_save and not cfg.if_test:
self._save('MLE', epoch)
def adv_train_generator(self, g_step, adv_epoch):
rebar_ge = RebarGradientEstimator(
discriminator=self.dis,
batch_size=cfg.batch_size,
real_samples=self.train_data.random_batch()['target'],
gpu=cfg.CUDA,
num_rep=cfg.num_rep)
total_rebar_loss = 0
old_temperature = self.gen.temperature.item()
old_eta = self.gen.eta.item()
for step in range(g_step):
theta, z = self.gen.sample_theta(cfg.batch_size)
estimated_gradient, temperature_grad, eta_grad = rebar_ge.estimate_gradient(
theta, z,
self.gen.temperature.clone().detach().requires_grad_(),
self.gen.eta.clone().detach().requires_grad_())
adv_loss = self.gen.computeRebarLoss(estimated_gradient)
temperature_grad = temperature_grad if cfg.learn_temperature else torch.zeros_like(
temperature_grad)
eta_grad = eta_grad if cfg.learn_eta else torch.zeros_like(
eta_grad)
self.optimize(self.gen_adv_opt,
adv_loss,
callback=functools.partial(
self.gen.set_variance_loss_gradients,
temperature_grad, eta_grad))
total_rebar_loss += adv_loss.item()
# =====Test=====
avg_rebar_loss = total_rebar_loss / g_step if g_step != 0 else 0
if adv_epoch % cfg.adv_log_step == 0:
self.log.info(
'[ADV-GEN] rebar_loss = %.4f, temperature = %.4f, eta = %.4f, %s'
% (avg_rebar_loss, old_temperature, old_eta,
self.cal_metrics(fmt_str=True)))
def adv_train_discriminator(self, d_step, adv_epoch):
total_loss = 0
total_acc = 0
for step in range(d_step):
# TODO(ethanjiang) we may want to train a full epoch instead of a random batch
real_samples = self.train_data.random_batch()['target']
gen_samples = self.gen.sample(cfg.batch_size, cfg.batch_size)
if cfg.CUDA:
real_samples, gen_samples = real_samples.cuda(
), gen_samples.cuda()
real_samples = F.one_hot(real_samples, cfg.vocab_size).float()
gen_samples = F.one_hot(gen_samples, cfg.vocab_size).float()
# =====Train=====
d_out_real = self.dis(real_samples)
d_out_fake = self.dis(gen_samples)
_, d_loss = get_losses(d_out_real, d_out_fake, cfg.loss_type)
self.dis_opt.zero_grad()
d_loss.backward()
torch.nn.utils.clip_grad_norm_(self.dis.parameters(),
cfg.clip_norm)
self.dis_opt.step()
total_loss += d_loss.item()
if cfg.loss_type != 'rsgan':
predictions = torch.cat((d_out_real, d_out_fake))
labels = torch.cat((torch.ones_like(d_out_real),
torch.zeros_like(d_out_fake)))
total_acc += torch.sum(
((predictions > 0).float() == labels)).item()
# =====Test=====
avg_loss = total_loss / d_step if d_step != 0 else 0
if cfg.loss_type != 'rsgan':
avg_acc = total_acc / (d_step * cfg.batch_size *
2) if d_step != 0 else 0
if adv_epoch % cfg.adv_log_step == 0:
self.log.info('[ADV-DIS] d_loss = %.4f, train_acc = %.4f,' %
(avg_loss, avg_acc))
else:
if adv_epoch % cfg.adv_log_step == 0:
self.log.info('[ADV-DIS] d_loss = %.4f,' % (avg_loss))
def pretrain_discriminator(self, d_step, d_epoch, phrase='MLE'):
"""
Training the discriminator on real_data_samples (positive) and generated samples from gen (negative).
Samples are drawn d_step times, and the discriminator is trained for d_epoch d_epoch.
"""
# prepare loader for validate
for step in range(d_step):
# prepare loader for training
pos_samples = self.train_data.target
neg_samples = self.gen.sample(cfg.samples_num, 4 * cfg.batch_size)
self.dis_data.reset(pos_samples, neg_samples)
for epoch in range(d_epoch):
# =====Train=====
d_loss, train_acc = self.train_dis_epoch(
self.dis,
self.dis_data.loader,
self.dis_pretrain_criterion,
self.dis_opt,
one_hot=True,
num_rep=cfg.num_rep)
# =====Test=====
self.log.info(
'[%s-DIS] d_step %d: d_loss = %.4f, train_acc = %.4f,' %
(phrase, step, d_loss, train_acc))
class SeqGANInstructor(BasicInstructor):
def __init__(self, opt):
super(SeqGANInstructor, self).__init__(opt)
# generator, discriminator
self.gen = SeqGAN_G(cfg.gen_embed_dim,
cfg.gen_hidden_dim,
cfg.vocab_size,
cfg.max_seq_len,
cfg.padding_idx,
cfg.temperature,
gpu=cfg.CUDA)
self.dis = SeqGAN_D(cfg.dis_embed_dim,
cfg.vocab_size,
cfg.padding_idx,
gpu=cfg.CUDA)
self.init_model()
# Optimizer
self.gen_opt = optim.Adam(self.gen.parameters(), lr=cfg.gen_lr)
self.gen_adv_opt = optim.Adam(self.gen.parameters(), lr=cfg.gen_lr)
self.dis_opt = optim.Adam(self.dis.parameters(), lr=cfg.dis_lr)
# Criterion
self.mle_criterion = nn.NLLLoss()
self.dis_criterion = nn.BCEWithLogitsLoss()
# DataLoader
self.gen_data = GenDataIter(
self.gen.sample(cfg.batch_size, cfg.batch_size))
self.dis_data = DisDataIter(self.train_data.random_batch()['target'],
self.gen_data.random_batch()['target'])
# Metrics
self.bleu = BLEU(test_text=tensor_to_tokens(self.gen_data.target,
self.index_word_dict),
real_text=tensor_to_tokens(
self.test_data.target,
self.test_data.index_word_dict),
gram=3)
self.self_bleu = BLEU(
test_text=tensor_to_tokens(self.gen_data.target,
self.index_word_dict),
real_text=tensor_to_tokens(self.gen_data.target,
self.index_word_dict),
gram=3)
def _run(self):
# =====PRE-TRAINING=====
# TRAIN GENERATOR
if not cfg.gen_pretrain:
self.log.info('Starting Generator MLE Training...')
self.pretrain_generator(cfg.MLE_train_epoch)
if cfg.if_save and not cfg.if_test:
torch.save(self.gen.state_dict(), cfg.pretrained_gen_path)
print('Save pre-trained generator: {}'.format(
cfg.pretrained_gen_path))
# =====TRAIN DISCRIMINATOR======
if not cfg.dis_pretrain:
self.log.info('Starting Discriminator Training...')
self.train_discriminator(cfg.d_step, cfg.d_epoch)
if cfg.if_save and not cfg.if_test:
torch.save(self.dis.state_dict(), cfg.pretrained_dis_path)
print('Save pretrain_generator discriminator: {}'.format(
cfg.pretrained_dis_path))
# =====ADVERSARIAL TRAINING=====
self.log.info('Starting Adversarial Training...')
self.log.info('Initial generator: %s' %
(self.cal_metrics(fmt_str=True)))
for adv_epoch in range(cfg.ADV_train_epoch):
self.log.info('-----\nADV EPOCH %d\n-----' % adv_epoch)
self.sig.update()
if self.sig.adv_sig:
self.adv_train_generator(cfg.ADV_g_step) # Generator
self.train_discriminator(cfg.ADV_d_step, cfg.ADV_d_epoch,
'ADV') # Discriminator
if adv_epoch % cfg.adv_log_step == 0:
if cfg.if_save and not cfg.if_test:
self._save('ADV', adv_epoch)
else:
self.log.info(
'>>> Stop by adv_signal! Finishing adversarial training...'
)
break
def _test(self):
print('>>> Begin test...')
self._run()
pass
def pretrain_generator(self, epochs):
"""
Max Likelihood Pre-training for the generator
"""
for epoch in range(epochs):
self.sig.update()
if self.sig.pre_sig:
pre_loss = self.train_gen_epoch(self.gen,
self.train_data.loader,
self.mle_criterion,
self.gen_opt)
# =====Test=====
if epoch % cfg.pre_log_step == 0:
self.log.info(
'[MLE-GEN] epoch %d : pre_loss = %.4f, %s' %
(epoch, pre_loss, self.cal_metrics(fmt_str=True)))
if cfg.if_save and not cfg.if_test:
self._save('MLE', epoch)
else:
self.log.info(
'>>> Stop by pre signal, skip to adversarial training...')
break
if cfg.if_save and not cfg.if_test:
self._save('MLE', epoch)
def adv_train_generator(self, g_step):
"""
The gen is trained using policy gradients, using the reward from the discriminator.
Training is done for num_batches batches.
"""
rollout_func = rollout.ROLLOUT(self.gen, cfg.CUDA)
total_g_loss = 0
for step in range(g_step):
inp, target = self.gen_data.prepare(self.gen.sample(
cfg.batch_size, cfg.batch_size),
gpu=cfg.CUDA)
# =====Train=====
rewards = rollout_func.get_reward(target, cfg.rollout_num,
self.dis)
adv_loss = self.gen.batchPGLoss(inp, target, rewards)
self.optimize(self.gen_adv_opt, adv_loss)
total_g_loss += adv_loss.item()
# =====Test=====
self.log.info('[ADV-GEN]: g_loss = %.4f, %s' %
(total_g_loss, self.cal_metrics(fmt_str=True)))
def train_discriminator(self, d_step, d_epoch, phrase='MLE'):
"""
Training the discriminator on real_data_samples (positive) and generated samples from gen (negative).
Samples are drawn d_step times, and the discriminator is trained for d_epoch d_epoch.
"""
# prepare loader for validate
for step in range(d_step):
# prepare loader for training
pos_samples = self.train_data.target
neg_samples = self.gen.sample(cfg.samples_num, 4 * cfg.batch_size)
self.dis_data.reset(pos_samples, neg_samples)
for epoch in range(d_epoch):
# =====Train=====
d_loss, train_acc = self.train_dis_epoch(
self.dis, self.dis_data.loader, self.dis_criterion,
self.dis_opt)
# =====Test=====
self.log.info(
'[%s-DIS] d_step %d: d_loss = %.4f, train_acc = %.4f,' %
(phrase, step, d_loss, train_acc))
if cfg.if_save and not cfg.if_test:
torch.save(self.dis.state_dict(), cfg.pretrained_dis_path)
class LeakGANInstructor(BasicInstructor):
def __init__(self, opt):
super(LeakGANInstructor, self).__init__(opt)
# generator, discriminator
self.gen = LeakGAN_G(cfg.gen_embed_dim, cfg.gen_hidden_dim,
cfg.vocab_size, cfg.max_seq_len, cfg.padding_idx,
cfg.goal_size, cfg.step_size, cfg.CUDA)
self.dis = LeakGAN_D(cfg.dis_embed_dim,
cfg.vocab_size,
cfg.padding_idx,
gpu=cfg.CUDA)
#LSTM
self.corpus = dataa.Corpus('dataset/emnlp_news/')
self.lstm = LSTM.RNNModel('LSTM', len(self.corpus.dictionary), 200,
600, 3, 0.2, False)
if (cfg.CUDA):
self.dis.cuda()
self.gen.cuda()
self.init_model()
# optimizer
mana_params, work_params = self.gen.split_params()
mana_opt = optim.Adam(mana_params, lr=cfg.gen_lr)
work_opt = optim.Adam(work_params, lr=cfg.gen_lr)
self.gen_opt = [mana_opt, work_opt]
self.dis_opt = optim.Adam(self.dis.parameters(), lr=cfg.dis_lr)
# Criterion
self.mle_criterion = nn.NLLLoss()
self.dis_criterion = nn.CrossEntropyLoss()
# DataLoader
self.gen_data = GenDataIter(
self.gen.sample(cfg.batch_size, cfg.batch_size, self.dis))
self.dis_data = DisDataIter(self.gen_data.random_batch()['target'],
self.oracle_data.random_batch()['target'])
# Metrics
self.bleu3 = BLEU(test_text=tensor_to_tokens(self.gen_data.target,
self.index_word_dict),
real_text=tensor_to_tokens(self.test_data.target,
self.index_word_dict),
gram=3)
def _run(self):
for inter_num in range(cfg.inter_epoch):
self.log.info('>>> Interleaved Round %d...' % inter_num)
self.sig.update() # update signal
if self.sig.pre_sig:
# =====DISCRIMINATOR PRE-TRAINING=====
if not cfg.dis_pretrain:
self.log.info('Starting Discriminator Training...')
self.train_discriminator(cfg.d_step, cfg.d_epoch)
if cfg.if_save and not cfg.if_test:
torch.save(self.dis.state_dict(),
cfg.pretrained_dis_path)
print('Save pre-trained discriminator: {}'.format(
cfg.pretrained_dis_path))
# =====GENERATOR MLE TRAINING=====
if not cfg.gen_pretrain:
self.log.info('Starting Generator MLE Training...')
self.pretrain_generator(cfg.MLE_train_epoch)
if cfg.if_save and not cfg.if_test:
torch.save(self.gen.state_dict(),
cfg.pretrained_gen_path)
print('Save pre-trained generator: {}'.format(
cfg.pretrained_gen_path))
else:
self.log.info(
'>>> Stop by pre_signal! Skip to adversarial training...')
break
# =====ADVERSARIAL TRAINING=====
self.log.info('Starting Adversarial Training...')
self.log.info('Initial generator: %s' %
(str(self.cal_metrics(fmt_str=True))))
for adv_epoch in range(cfg.ADV_train_epoch):
self.log.info('-----\nADV EPOCH %d\n-----' % adv_epoch)
self.sig.update()
if self.sig.adv_sig:
self.adv_train_generator(cfg.ADV_g_step) # Generator
self.train_discriminator(cfg.ADV_d_step, cfg.ADV_d_epoch,
'ADV') # Discriminator
if adv_epoch % cfg.adv_log_step == 0:
if cfg.if_save and not cfg.if_test:
self._save('ADV', adv_epoch)
else:
self.log.info(
'>>> Stop by adv_signal! Finishing adversarial training...'
)
break
def string2bins(self, bit_string, n_bins):
n_bits = int(math.log(n_bins, 2))
return [
bit_string[i:i + n_bits] for i in range(0, len(bit_string), n_bits)
]
def LSTM_layer_1(self, intermediate_file, bins_num):
print('>>> Begin test...')
print('Begin with LSTM Layer')
#First layer- LSTM layer
epoch_start_time = time.time()
seed = 1111
data_root = './decode/'
#Reproducibility
torch.manual_seed(seed)
if cfg.CUDA:
torch.cuda.manual_seed(seed)
with open("leakGAN_instructor/real_data/emnlp_news.pt", 'rb') as f:
self.lstm = torch.load(f)
if cfg.CUDA:
self.lstm.cuda()
emnlp_data = 'dataset/emnlp_news/'
corpus = dataa.Corpus(emnlp_data)
ntokens = len(corpus.dictionary)
idx2word_file = data_root + "idx2word_1.txt"
word2idx_file = data_root + "word2idx_1.txt"
with open(idx2word_file, "wb") as fp: #Pickling
pickle.dump(corpus.dictionary.idx2word, fp)
with open(word2idx_file, "wb") as fp: #Pickling
pickle.dump(corpus.dictionary.word2idx, fp)
hidden = self.lstm.init_hidden(1)
input = torch.randint(ntokens, (1, 1), dtype=torch.long)
if cfg.CUDA:
input.data = input.data.cuda()
print("Finished Initializing LSTM Model")
#Step 1: Get secret data
secret_file = open("leakGAN_instructor/real_data/secret_file.txt", 'r')
secret_data = secret_file.read()
#Step 2: Compress string into binary string
bit_string = ''.join(
bin(ord(letter))[2:].zfill(8) for letter in secret_data)
#print(bit_string)
bit_string = '111011100101000111000011110111101111110111000110011010110110'
#In the first step we will use 256 bins (8 bit representation each) to convert so that we can convert 64 bits into 8 word
#Step 3: Divide into bins
secret_text = [
int(i, 2) for i in self.string2bins(bit_string, bins_num)
] #convert to bins
#Step 4: Divide vocabulary into bins, zero words not in the bin
if bins_num >= 2:
tokens = list(range(ntokens)) #indecies of words
random.shuffle(tokens) #randomize
#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(len(bins)):
if (i == leftover):
break
bins[i].append(tokens[i + words_in_bin * bins_num])
print("Len of bins in 1st layer: {}".format(len(bins)))
#save bins into key 1
key1 = data_root + "lstm_key1.txt"
with open(key1, "wb") as fp: #Pickling
pickle.dump(bins, fp)
zero = [list(set(tokens) - set(bin_)) for bin_ in bins]
print('Finished Initializing First LSTM Layer')
print('time: {:5.2f}s'.format(time.time() - epoch_start_time))
print('-' * 89)
intermediate_file = data_root + intermediate_file
with open(intermediate_file, 'w') as outf:
w = 0
i = 1
temperature = 1.5
bin_sequence_length = len(secret_text[:]) # 85
print("bin sequence length", bin_sequence_length) #32
while i <= bin_sequence_length:
epoch_start_time = time.time()
output, hidden = self.lstm(input, hidden)
zero_index = zero[secret_text[:][i - 1]]
zero_index = torch.LongTensor(zero_index)
word_weights = output.squeeze().data.div(
temperature).exp().cpu()
word_weights.index_fill_(0, zero_index, 0)
word_idx = torch.multinomial(word_weights, 1)[0]
input.data.fill_(word_idx)
word = corpus.dictionary.idx2word[word_idx]
i += 1
w += 1
word = word.encode('ascii', 'ignore').decode('ascii')
outf.write(word + ' ')
print("Generated intermediate short steganographic text")
print("Intermediate text saved in following file: {}".format(
intermediate_file))
def LSTM_layer_2(self, secret_file, final_file, bins_num):
print('Final LSTM Layer')
#First layer- LSTM layer
data_root = './decode/'
epoch_start_time = time.time()
seed = 1111
#Reproducibility
torch.manual_seed(seed)
if cfg.CUDA:
torch.cuda.manual_seed(seed)
with open("leakGAN_instructor/real_data/emnlp_news.pt", 'rb') as f:
self.lstm = torch.load(f)
if cfg.CUDA:
self.lstm.cuda()
emnlp_data = 'dataset/emnlp_news/'
corpus = dataa.Corpus(emnlp_data)
#save dictionary
idx2word_file = data_root + "idx2word_2.txt"
word2idx_file = data_root + "word2idx_2.txt"
with open(idx2word_file, "wb") as fp: #Pickling
pickle.dump(corpus.dictionary.idx2word, fp)
with open(word2idx_file, "wb") as fp: #Pickling
pickle.dump(corpus.dictionary.word2idx, fp)
ntokens = len(corpus.dictionary)
hidden = self.lstm.init_hidden(1)
input = torch.randint(ntokens, (1, 1), dtype=torch.long)
if cfg.CUDA:
input.data = input.data.cuda()
print("Finished Initializing LSTM Model")
#Step 1: Get secret data
secret_file = open(data_root + secret_file, 'r')
secret_data = secret_file.read().split()
#Step 2: Compress string into binary string
bit_string = ''
for data in secret_data:
print("Data: {}".format(data))
idWord = corpus.dictionary.word2idx[data]
bit_string += '{0:{fill}13b}'.format(int(idWord), fill='0')
#print(ntokens)
print("Bit String: {}".format(bit_string))
print("Length of Bit String: {}".format(len(bit_string)))
#print(bit_string)
#bit_string = '111011100101000111000011110111101111110111000110011010110110'
#In the first step we will use 256 bins (8 bit representation each) to convert so that we can convert 64 bits into 8 word
#Step 3: Divide into bins
secret_text = [
int(i, 2) for i in self.string2bins(bit_string, bins_num)
] #convert to bins
#Step 4: Divide vocabulary into bins, zero words not in the bin
if bins_num >= 2:
tokens = list(range(ntokens)) #indecies of words
random.shuffle(tokens) #randomize
#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 key 1
key1 = data_root + "lstm_key2.txt"
with open(key1, "wb") as fp: #Pickling
pickle.dump(bins, fp)
zero = [list(set(tokens) - set(bin_)) for bin_ in bins]
print('Finished Initializing Second LSTM Layer')
print('time: {:5.2f}s'.format(time.time() - epoch_start_time))
print('-' * 89)
final_file = data_root + final_file
with open(final_file, 'w') as outf:
w = 0
i = 1
temperature = 1.5
bin_sequence_length = len(secret_text[:]) # 85
print("bin sequence length", bin_sequence_length) #32
while i <= bin_sequence_length:
epoch_start_time = time.time()
output, hidden = self.lstm(input, hidden)
zero_index = zero[secret_text[:][i - 1]]
zero_index = torch.LongTensor(zero_index)
word_weights = output.squeeze().data.div(
temperature).exp().cpu()
word_weights.index_fill_(0, zero_index, 0)
word_idx = torch.multinomial(word_weights, 1)[0]
input.data.fill_(word_idx)
word = corpus.dictionary.idx2word[word_idx]
i += 1
w += 1
word = word.encode('ascii', 'ignore').decode('ascii')
outf.write(word + ' ')
print("Generated final steganographic text")
print("Final text saved in following file: {}".format(
str(data_root + final_file)))
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))
def _test_2_layers(self):
self.LSTM_layer_1("intermediate.txt", 4096)
if cfg.leakGAN:
self.leakGAN_layer("intermediate.txt", "final_leakgan.txt", 4)
else:
self.LSTM_layer_2("intermediate.txt", "final_lstm.txt", 4)
def _test(self):
print('>>> Begin test...')
def pretrain_generator(self, epochs):
"""
Max Likelihood Pretraining for the gen
- gen_opt: [mana_opt, work_opt]
"""
for epoch in range(epochs):
self.sig.update()
if self.sig.pre_sig:
pre_mana_loss = 0
pre_work_loss = 0
# =====Train=====
for i, data in enumerate(self.oracle_data.loader):
inp, target = data['input'], data['target']
if cfg.CUDA:
inp, target = inp.cuda(), target.cuda()
mana_loss, work_loss = self.gen.pretrain_loss(
target, self.dis)
self.optimize_multi(self.gen_opt, [mana_loss, work_loss])
pre_mana_loss += mana_loss.data.item()
pre_work_loss += work_loss.data.item()
pre_mana_loss = pre_mana_loss / len(self.oracle_data.loader)
pre_work_loss = pre_work_loss / len(self.oracle_data.loader)
# =====Test=====
if epoch % cfg.pre_log_step == 0:
self.log.info(
'[MLE-GEN] epoch %d : pre_mana_loss = %.4f, pre_work_loss = %.4f, %s'
% (epoch, pre_mana_loss, pre_work_loss,
self.cal_metrics(fmt_str=True)))
if cfg.if_save and not cfg.if_test:
self._save('MLE', epoch)
else:
self.log.info(
'>>> Stop by pre signal, skip to adversarial training...')
break
def adv_train_generator(self, g_step, current_k=0):
"""
The gen is trained using policy gradients, using the reward from the discriminator.
Training is done for num_batches batches.
"""
rollout_func = rollout.ROLLOUT(self.gen, cfg.CUDA)
adv_mana_loss = 0
adv_work_loss = 0
for step in range(g_step):
with torch.no_grad():
gen_samples = self.gen.sample(
cfg.batch_size, cfg.batch_size, self.dis,
train=True) # !!! train=True, the only place
inp, target = self.gen_data.prepare(gen_samples, gpu=cfg.CUDA)
# =====Train=====
rewards = rollout_func.get_reward_leakgan(
target, cfg.rollout_num, self.dis,
current_k).cpu() # reward with MC search
mana_loss, work_loss = self.gen.adversarial_loss(
target, rewards, self.dis)
# update parameters
self.optimize_multi(self.gen_opt, [mana_loss, work_loss])
adv_mana_loss += mana_loss.data.item()
adv_work_loss += work_loss.data.item()
# =====Test=====
self.log.info(
'[ADV-GEN] adv_mana_loss = %.4f, adv_work_loss = %.4f, %s' %
(adv_mana_loss / g_step, adv_work_loss / g_step,
self.cal_metrics(fmt_str=True)))
def train_discriminator(self, d_step, d_epoch, phrase='MLE'):
"""
Training the discriminator on real_data_samples (positive) and generated samples from gen (negative).
Samples are drawn d_step times, and the discriminator is trained for d_epoch d_epoch.
"""
for step in range(d_step):
# prepare loader for training
pos_samples = self.oracle_data.target
neg_samples = self.gen.sample(cfg.samples_num, cfg.batch_size,
self.dis)
self.dis_data.reset(pos_samples, neg_samples)
for epoch in range(d_epoch):
# =====Train=====
d_loss, train_acc = self.train_dis_epoch(
self.dis, self.dis_data.loader, self.dis_criterion,
self.dis_opt)
# =====Test=====
self.log.info(
'[%s-DIS] d_step %d: d_loss = %.4f, train_acc = %.4f,' %
(phrase, step, d_loss, train_acc))
def cal_metrics(self, fmt_str=False):
self.gen_data.reset(
self.gen.sample(cfg.samples_num, cfg.batch_size, self.dis))
self.bleu3.test_text = tensor_to_tokens(self.gen_data.target,
self.index_word_dict)
bleu3_score = self.bleu3.get_score(ignore=False)
with torch.no_grad():
gen_nll = 0
for data in self.oracle_data.loader:
inp, target = data['input'], data['target']
if cfg.CUDA:
inp, target = inp.cuda(), target.cuda()
loss = self.gen.batchNLLLoss(target, self.dis)
gen_nll += loss.item()
gen_nll /= len(self.oracle_data.loader)
if fmt_str:
return 'BLEU-3 = %.4f, gen_NLL = %.4f,' % (bleu3_score, gen_nll)
return bleu3_score, gen_nll
def _save(self, phrase, epoch):
torch.save(
self.gen.state_dict(),
cfg.save_model_root + 'gen_{}_{:05d}.pt'.format(phrase, epoch))
save_sample_path = cfg.save_samples_root + 'samples_{}_{:05d}.txt'.format(
phrase, epoch)
samples = self.gen.sample(cfg.batch_size, cfg.batch_size, self.dis)
write_tokens(save_sample_path,
tensor_to_tokens(samples, self.index_word_dict))
class LeakGANInstructor(BasicInstructor):
def __init__(self, opt):
super(LeakGANInstructor, self).__init__(opt)
# generator, discriminator
self.gen = LeakGAN_G(cfg.gen_embed_dim, cfg.gen_hidden_dim,
cfg.vocab_size, cfg.max_seq_len, cfg.padding_idx,
cfg.goal_size, cfg.step_size, cfg.CUDA)
self.dis = LeakGAN_D(cfg.dis_embed_dim,
cfg.vocab_size,
cfg.padding_idx,
gpu=cfg.CUDA)
self.init_model()
# optimizer
mana_params, work_params = self.gen.split_params()
mana_opt = optim.Adam(mana_params, lr=cfg.gen_lr)
work_opt = optim.Adam(work_params, lr=cfg.gen_lr)
self.gen_opt = [mana_opt, work_opt]
self.dis_opt = optim.Adam(self.dis.parameters(), lr=cfg.dis_lr)
# Criterion
self.mle_criterion = nn.NLLLoss()
self.dis_criterion = nn.CrossEntropyLoss()
# DataLoader
self.gen_data = GenDataIter(
self.gen.sample(cfg.batch_size, cfg.batch_size, self.dis))
self.dis_data = DisDataIter(self.gen_data.random_batch()['target'],
self.oracle_data.random_batch()['target'])
# Metrics
self.bleu3 = BLEU(test_text=tensor_to_tokens(self.gen_data.target,
self.index_word_dict),
real_text=tensor_to_tokens(self.test_data.target,
self.index_word_dict),
gram=3)
def _run(self):
for inter_num in range(cfg.inter_epoch):
self.log.info('>>> Interleaved Round %d...' % inter_num)
self.sig.update() # update signal
if self.sig.pre_sig:
# =====DISCRIMINATOR PRE-TRAINING=====
if not cfg.dis_pretrain:
self.log.info('Starting Discriminator Training...')
self.train_discriminator(cfg.d_step, cfg.d_epoch)
if cfg.if_save and not cfg.if_test:
torch.save(self.dis.state_dict(),
cfg.pretrained_dis_path)
print('Save pre-trained discriminator: {}'.format(
cfg.pretrained_dis_path))
# =====GENERATOR MLE TRAINING=====
if not cfg.gen_pretrain:
self.log.info('Starting Generator MLE Training...')
self.pretrain_generator(cfg.MLE_train_epoch)
if cfg.if_save and not cfg.if_test:
torch.save(self.gen.state_dict(),
cfg.pretrained_gen_path)
print('Save pre-trained generator: {}'.format(
cfg.pretrained_gen_path))
else:
self.log.info(
'>>> Stop by pre_signal! Skip to adversarial training...')
break
# =====ADVERSARIAL TRAINING=====
self.log.info('Starting Adversarial Training...')
self.log.info('Initial generator: %s' %
(str(self.cal_metrics(fmt_str=True))))
for adv_epoch in range(cfg.ADV_train_epoch):
self.log.info('-----\nADV EPOCH %d\n-----' % adv_epoch)
self.sig.update()
if self.sig.adv_sig:
self.adv_train_generator(cfg.ADV_g_step) # Generator
self.train_discriminator(cfg.ADV_d_step, cfg.ADV_d_epoch,
'ADV') # Discriminator
if adv_epoch % cfg.adv_log_step == 0:
if cfg.if_save and not cfg.if_test:
self._save('ADV', adv_epoch)
else:
self.log.info(
'>>> Stop by adv_signal! Finishing adversarial training...'
)
break
def _test(self):
print('>>> Begin test...')
self._run()
pass
def pretrain_generator(self, epochs):
"""
Max Likelihood Pretraining for the gen
- gen_opt: [mana_opt, work_opt]
"""
for epoch in range(epochs):
self.sig.update()
if self.sig.pre_sig:
pre_mana_loss = 0
pre_work_loss = 0
# =====Train=====
for i, data in enumerate(self.oracle_data.loader):
inp, target = data['input'], data['target']
if cfg.CUDA:
inp, target = inp.cuda(), target.cuda()
mana_loss, work_loss = self.gen.pretrain_loss(
target, self.dis)
self.optimize_multi(self.gen_opt, [mana_loss, work_loss])
pre_mana_loss += mana_loss.data.item()
pre_work_loss += work_loss.data.item()
pre_mana_loss = pre_mana_loss / len(self.oracle_data.loader)
pre_work_loss = pre_work_loss / len(self.oracle_data.loader)
# =====Test=====
if epoch % cfg.pre_log_step == 0:
self.log.info(
'[MLE-GEN] epoch %d : pre_mana_loss = %.4f, pre_work_loss = %.4f, %s'
% (epoch, pre_mana_loss, pre_work_loss,
self.cal_metrics(fmt_str=True)))
if cfg.if_save and not cfg.if_test:
self._save('MLE', epoch)
else:
self.log.info(
'>>> Stop by pre signal, skip to adversarial training...')
break
def adv_train_generator(self, g_step, current_k=0):
"""
The gen is trained using policy gradients, using the reward from the discriminator.
Training is done for num_batches batches.
"""
rollout_func = rollout.ROLLOUT(self.gen, cfg.CUDA)
adv_mana_loss = 0
adv_work_loss = 0
for step in range(g_step):
with torch.no_grad():
gen_samples = self.gen.sample(
cfg.batch_size, cfg.batch_size, self.dis,
train=True) # !!! train=True, the only place
inp, target = self.gen_data.prepare(gen_samples, gpu=cfg.CUDA)
# =====Train=====
rewards = rollout_func.get_reward_leakgan(
target, cfg.rollout_num, self.dis,
current_k).cpu() # reward with MC search
mana_loss, work_loss = self.gen.adversarial_loss(
target, rewards, self.dis)
# update parameters
self.optimize_multi(self.gen_opt, [mana_loss, work_loss])
adv_mana_loss += mana_loss.data.item()
adv_work_loss += work_loss.data.item()
# =====Test=====
self.log.info(
'[ADV-GEN] adv_mana_loss = %.4f, adv_work_loss = %.4f, %s' %
(adv_mana_loss / g_step, adv_work_loss / g_step,
self.cal_metrics(fmt_str=True)))
def train_discriminator(self, d_step, d_epoch, phrase='MLE'):
"""
Training the discriminator on real_data_samples (positive) and generated samples from gen (negative).
Samples are drawn d_step times, and the discriminator is trained for d_epoch d_epoch.
"""
for step in range(d_step):
# prepare loader for training
pos_samples = self.oracle_data.target
neg_samples = self.gen.sample(cfg.samples_num, cfg.batch_size,
self.dis)
self.dis_data.reset(pos_samples, neg_samples)
for epoch in range(d_epoch):
# =====Train=====
d_loss, train_acc = self.train_dis_epoch(
self.dis, self.dis_data.loader, self.dis_criterion,
self.dis_opt)
# =====Test=====
self.log.info(
'[%s-DIS] d_step %d: d_loss = %.4f, train_acc = %.4f,' %
(phrase, step, d_loss, train_acc))
def cal_metrics(self, fmt_str=False):
self.gen_data.reset(
self.gen.sample(cfg.samples_num, cfg.batch_size, self.dis))
self.bleu3.test_text = tensor_to_tokens(self.gen_data.target,
self.index_word_dict)
bleu3_score = self.bleu3.get_score(ignore=False)
with torch.no_grad():
gen_nll = 0
for data in self.oracle_data.loader:
inp, target = data['input'], data['target']
if cfg.CUDA:
inp, target = inp.cuda(), target.cuda()
loss = self.gen.batchNLLLoss(target, self.dis)
gen_nll += loss.item()
gen_nll /= len(self.oracle_data.loader)
if fmt_str:
return 'BLEU-3 = %.4f, gen_NLL = %.4f,' % (bleu3_score, gen_nll)
return bleu3_score, gen_nll
def _save(self, phrase, epoch):
torch.save(
self.gen.state_dict(),
cfg.save_model_root + 'gen_{}_{:05d}.pt'.format(phrase, epoch))
save_sample_path = cfg.save_samples_root + 'samples_{}_{:05d}.txt'.format(
phrase, epoch)
samples = self.gen.sample(cfg.batch_size, cfg.batch_size, self.dis)
write_tokens(save_sample_path,
tensor_to_tokens(samples, self.index_word_dict))
class GumbelGANInstructor(BasicInstructor):
def __init__(self, opt):
super(GumbelGANInstructor, self).__init__(opt)
# generator, discriminator
self.gen = GumbelGAN_G(cfg.gen_embed_dim,
cfg.gen_hidden_dim,
cfg.vocab_size,
cfg.max_seq_len,
cfg.padding_idx,
gpu=cfg.CUDA)
self.dis = GumbelGAN_D2(cfg.dis_embed_dim,
cfg.max_seq_len,
cfg.vocab_size,
cfg.padding_idx,
gpu=cfg.CUDA)
self.init_model()
# Optimizer
self.gen_opt = optim.Adam(self.gen.parameters(), lr=cfg.gen_lr)
self.gen_adv_opt = optim.Adam(self.gen.parameters(), lr=cfg.gen_adv_lr)
self.dis_opt = optim.Adam(self.dis.parameters(), lr=cfg.dis_lr)
# Criterion
self.mle_criterion = nn.NLLLoss()
self.dis_pretrain_criterion = nn.BCEWithLogitsLoss()
# DataLoader
self.gen_data = GenDataIter(
self.gen.sample(cfg.batch_size, cfg.batch_size))
self.dis_data = DisDataIter(self.train_data.random_batch()['target'],
self.gen_data.random_batch()['target'])
# Metrics
bleu_gram = list(range(2, cfg.max_seq_len +
1)) if cfg.max_seq_len < 5 else [2, 3, 4, 5]
self.bleu = BLEU(test_text=tensor_to_tokens(self.gen_data.target,
self.index_word_dict),
real_text=tensor_to_tokens(
self.test_data.target,
self.test_data.index_word_dict),
gram=bleu_gram)
self.self_bleu = BLEU(
test_text=tensor_to_tokens(self.gen_data.target,
self.index_word_dict),
real_text=tensor_to_tokens(self.gen_data.target,
self.index_word_dict),
gram=3)
def _run(self):
# =====PRE-TRAINING (GENERATOR)=====
if not cfg.gen_pretrain:
self.log.info('Starting Generator MLE Training...')
self.pretrain_generator(cfg.MLE_train_epoch)
if cfg.if_save and not cfg.if_test:
torch.save(self.gen.state_dict(), cfg.pretrained_gen_path)
print('Save pretrain_generator: {}'.format(
cfg.pretrained_gen_path))
# =====PRE-TRAINING (DISCRIMINATOR)=====
if not cfg.dis_pretrain:
self.log.info('Starting Discriminator Training...')
self.pretrain_discriminator(cfg.d_step, cfg.d_epoch)
if cfg.if_save and not cfg.if_test:
torch.save(self.dis.state_dict(), cfg.pretrained_dis_path)
print('Save pretrain_generator discriminator: {}'.format(
cfg.pretrained_dis_path))
# =====ADVERSARIAL TRAINING=====
self.log.info('Starting Adversarial Training...')
for adv_epoch in range(cfg.ADV_train_epoch):
if adv_epoch % cfg.adv_log_step == 0:
self.log.info('-----\nADV EPOCH %d\n-----' % adv_epoch)
self.sig.update()
if self.sig.adv_sig:
self.adv_train_generator(cfg.ADV_g_step,
adv_epoch) # Generator
self.adv_train_discriminator(cfg.ADV_d_step,
adv_epoch) # Discriminator
self.update_temperature(
adv_epoch, cfg.ADV_train_epoch) # update temperature
if adv_epoch % cfg.adv_log_step == 0:
if cfg.if_save and not cfg.if_test:
self._save('ADV', adv_epoch)
else:
self.log.info(
'>>> Stop by adv_signal! Finishing adversarial training...'
)
break
def _test(self):
print('>>> Begin test...')
self._run()
pass
def pretrain_generator(self, epochs):
"""
Max Likelihood Pre-training for the generator
"""
for epoch in range(epochs):
self.sig.update()
if self.sig.pre_sig:
# =====Train=====
pre_loss = self.train_gen_epoch(self.gen,
self.train_data.loader,
self.mle_criterion,
self.gen_opt)
# =====Test=====
if epoch % cfg.pre_log_step == 0 or epoch == epochs - 1:
self.log.info('[MLE-GEN] epoch %d : pre_loss = %.4f' %
(epoch, pre_loss))
if cfg.if_save and not cfg.if_test:
self._save('MLE', epoch)
else:
self.log.info(
'>>> Stop by pre signal, skip to adversarial training...')
break
if cfg.if_save and not cfg.if_test:
self._save('MLE', epoch)
def adv_train_generator(self, g_step, adv_epoch):
# true_ge = TrueGradientEstimator() TODO
total_loss = 0
for step in range(g_step):
real_samples = self.train_data.random_batch()['target']
gen_samples = self.gen.sample(cfg.batch_size,
cfg.batch_size,
one_hot=True)
if cfg.CUDA:
real_samples, gen_samples = real_samples.cuda(
), gen_samples.cuda()
real_samples = F.one_hot(real_samples, cfg.vocab_size).float()
# =====Train=====
# vanilla_theta = self.gen.sample_vanilla_theta()
# true_ge = true_ge.estimate_gradient(vanilla_theta...) TODO
d_out_real = self.dis(real_samples)
d_out_fake = self.dis(gen_samples)
g_loss, _ = get_losses(d_out_real, d_out_fake, cfg.loss_type)
theta_gradient = self.optimize(
self.gen_adv_opt,
g_loss,
self.gen,
theta_gradient_fetcher=self.gen.get_theta_gradient)
theta_gradient_log_var = get_gradient_variance(theta_gradient)
total_loss += g_loss.item()
# =====Test=====
avg_loss = total_loss / g_step if g_step != 0 else 0
if adv_epoch % cfg.adv_log_step == 0:
self.log.info(
'[ADV-GEN] g_loss = %.4f, temperature = %.4f, theta_gradient_log_var = %.4f'
% (avg_loss, self.gen.temperature, theta_gradient_log_var))
def adv_train_discriminator(self, d_step, adv_epoch):
total_loss = 0
total_acc = 0
for step in range(d_step):
# TODO(ethanjiang) we may want to train a full epoch instead of a random batch
real_samples = self.train_data.random_batch()['target']
gen_samples = self.gen.sample(cfg.batch_size,
cfg.batch_size,
one_hot=True)
if cfg.CUDA:
real_samples, gen_samples = real_samples.cuda(
), gen_samples.cuda()
real_samples = F.one_hot(real_samples, cfg.vocab_size).float()
# =====Train=====
d_out_real = self.dis(real_samples)
d_out_fake = self.dis(gen_samples)
_, d_loss = get_losses(d_out_real, d_out_fake, cfg.loss_type)
self.optimize(self.dis_opt, d_loss, self.dis)
total_loss += d_loss.item()
predictions = torch.cat((d_out_real, d_out_fake))
labels = torch.cat(
(torch.ones_like(d_out_real), torch.zeros_like(d_out_fake)))
total_acc += torch.sum(
((predictions > 0).float() == labels)).item()
# =====Test=====
avg_loss = total_loss / d_step if d_step != 0 else 0
avg_acc = total_acc / (d_step * cfg.batch_size *
2) if d_step != 0 else 0
if adv_epoch % cfg.adv_log_step == 0:
self.log.info('[ADV-DIS] d_loss = %.4f, train_acc = %.4f,' %
(avg_loss, avg_acc))
def update_temperature(self, i, N):
self.gen.temperature = get_fixed_temperature(cfg.temperature, i, N,
cfg.temp_adpt)
@staticmethod
def optimize(opt,
loss,
model=None,
retain_graph=False,
theta_gradient_fetcher=None):
opt.zero_grad()
loss.backward(retain_graph=retain_graph)
theta_gradient = None if theta_gradient_fetcher is None else theta_gradient_fetcher(
)
if model is not None:
torch.nn.utils.clip_grad_norm_(model.parameters(), cfg.clip_norm)
opt.step()
return theta_gradient
def pretrain_discriminator(self, d_step, d_epoch, phrase='MLE'):
"""
Training the discriminator on real_data_samples (positive) and generated samples from gen (negative).
Samples are drawn d_step times, and the discriminator is trained for d_epoch d_epoch.
"""
# prepare loader for validate
for step in range(d_step):
# prepare loader for training
pos_samples = self.train_data.target
neg_samples = self.gen.sample(cfg.samples_num, 4 * cfg.batch_size)
self.dis_data.reset(pos_samples, neg_samples)
for epoch in range(d_epoch):
# =====Train=====
d_loss, train_acc = self.train_dis_epoch(
self.dis,
self.dis_data.loader,
self.dis_pretrain_criterion,
self.dis_opt,
one_hot=True)
# =====Test=====
self.log.info(
'[%s-DIS] d_step %d: d_loss = %.4f, train_acc = %.4f,' %
(phrase, step, d_loss, train_acc))