def eval_iters(ae_model, dis_model):
eval_data_loader = non_pair_data_loader(
batch_size=1,
id_bos=args.id_bos,
id_eos=args.id_eos,
id_unk=args.id_unk,
max_sequence_length=args.max_sequence_length,
vocab_size=args.vocab_size)
eval_file_list = [
args.data_path + 'sentiment.test.0',
args.data_path + 'sentiment.test.1',
]
eval_label_list = [
[0],
[1],
]
eval_data_loader.create_batches(eval_file_list,
eval_label_list,
if_shuffle=False)
gold_ans = load_human_answer(args.data_path)
assert len(gold_ans) == eval_data_loader.num_batch
add_log("Start eval process.")
ae_model.eval()
dis_model.eval()
for it in range(eval_data_loader.num_batch):
batch_sentences, tensor_labels, \
tensor_src, tensor_src_mask, tensor_tgt, tensor_tgt_y, \
tensor_tgt_mask, tensor_ntokens = eval_data_loader.next_batch()
print("------------%d------------" % it)
print(id2text_sentence(tensor_tgt_y[0], args.id_to_word))
print("origin_labels", tensor_labels)
latent, out = ae_model.forward(tensor_src, tensor_tgt, tensor_src_mask,
tensor_tgt_mask)
generator_text = ae_model.greedy_decode(
latent, max_len=args.max_sequence_length, start_id=args.id_bos)
print(id2text_sentence(generator_text[0], args.id_to_word))
# Define target label
target = get_cuda(torch.tensor([[1.0]], dtype=torch.float))
if tensor_labels[0].item() > 0.5:
target = get_cuda(torch.tensor([[0.0]], dtype=torch.float))
print("target_labels", target)
modify_text = fgim_attack(dis_model, latent, target, ae_model,
args.max_sequence_length, args.id_bos,
id2text_sentence, args.id_to_word,
gold_ans[it])
add_output(modify_text)
return
def train_iters(ae_model, dis_model):
train_data_loader = non_pair_data_loader(
batch_size=args.batch_size,
id_bos=args.id_bos,
id_eos=args.id_eos,
id_unk=args.id_unk,
max_sequence_length=args.max_sequence_length,
vocab_size=args.vocab_size)
train_data_loader.create_batches(args.train_file_list,
args.train_label_list,
if_shuffle=True)
add_log("Start train process.")
ae_model.train()
dis_model.train()
ae_optimizer = NoamOpt(
ae_model.src_embed[0].d_model, 1, 2000,
torch.optim.Adam(ae_model.parameters(),
lr=0,
betas=(0.9, 0.98),
eps=1e-9))
dis_optimizer = torch.optim.Adam(dis_model.parameters(), lr=0.0001)
ae_criterion = get_cuda(
LabelSmoothing(size=args.vocab_size,
padding_idx=args.id_pad,
smoothing=0.1))
dis_criterion = nn.BCELoss(size_average=True)
for epoch in range(200):
print('-' * 94)
epoch_start_time = time.time()
for it in range(train_data_loader.num_batch):
batch_sentences, tensor_labels, \
tensor_src, tensor_src_mask, tensor_tgt, tensor_tgt_y, \
tensor_tgt_mask, tensor_ntokens = train_data_loader.next_batch()
# For debug
# print(batch_sentences[0])
# print(tensor_src[0])
# print(tensor_src_mask[0])
# print("tensor_src_mask", tensor_src_mask.size())
# print(tensor_tgt[0])
# print(tensor_tgt_y[0])
# print(tensor_tgt_mask[0])
# print(batch_ntokens)
# Forward pass
latent, out = ae_model.forward(tensor_src, tensor_tgt,
tensor_src_mask, tensor_tgt_mask)
# print(latent.size()) # (batch_size, max_src_seq, d_model)
# print(out.size()) # (batch_size, max_tgt_seq, vocab_size)
# Loss calculation
loss_rec = ae_criterion(
out.contiguous().view(-1, out.size(-1)),
tensor_tgt_y.contiguous().view(-1)) / tensor_ntokens.data
# loss_all = loss_rec + loss_dis
ae_optimizer.optimizer.zero_grad()
loss_rec.backward()
ae_optimizer.step()
# Classifier
dis_lop = dis_model.forward(to_var(latent.clone()))
loss_dis = dis_criterion(dis_lop, tensor_labels)
dis_optimizer.zero_grad()
loss_dis.backward()
dis_optimizer.step()
if it % 200 == 0:
add_log(
'| epoch {:3d} | {:5d}/{:5d} batches | rec loss {:5.4f} | dis loss {:5.4f} |'
.format(epoch, it, train_data_loader.num_batch, loss_rec,
loss_dis))
print(id2text_sentence(tensor_tgt_y[0], args.id_to_word))
generator_text = ae_model.greedy_decode(
latent,
max_len=args.max_sequence_length,
start_id=args.id_bos)
print(id2text_sentence(generator_text[0], args.id_to_word))
add_log('| end of epoch {:3d} | time: {:5.2f}s |'.format(
epoch, (time.time() - epoch_start_time)))
# Save model
torch.save(ae_model.state_dict(),
args.current_save_path + 'ae_model_params.pkl')
torch.save(dis_model.state_dict(),
args.current_save_path + 'dis_model_params.pkl')
return
# backward + optimization
ae_optimizer.zero_grad()
Loss_rec.backward()
ae_optimizer.step()
print('| end of epoch {:3d} | time: {:5.2f}s |'.format(
epoch, (time.time() - epoch_start_time)))
return
if __name__ == '__main__':
preparation()
train_data_loader = non_pair_data_loader(args.batch_size)
train_data_loader.create_batches(args.train_file_list,
args.train_label_list,
if_shuffle=True)
# create models
ae_model = get_cuda(
EncoderDecoder(
vocab_size=args.vocab_size,
embedding_size=args.embedding_size,
hidden_size=args.hidden_size,
num_layers=args.num_layers_AE,
word_dropout=args.word_dropout,
embedding_dropout=args.embedding_dropout,
sos_idx=args.id_bos,
eos_idx=args.id_eos,
dis_model=get_cuda(Classifier(1, args),args.gpu)
ae_optimizer = NoamOpt(ae_model.src_embed[0].d_model, 1, 2000,
torch.optim.Adam(ae_model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9))
dis_optimizer=torch.optim.Adam(dis_model.parameters(), lr=0.0001)
if args.load_model:
# Load models' params from checkpoint
ae_model.load_state_dict(torch.load(args.current_save_path + '/{}_ae_model_params.pkl'.format(args.load_iter), map_location=device))
dis_model.load_state_dict(torch.load(args.current_save_path + '/{}_dis_model_params.pkl'.format(args.load_iter), map_location=device))
start=args.load_iter+1
else:
start=0
train_data_loader=non_pair_data_loader(
batch_size=args.batch_size, id_bos=args.id_bos,
id_eos=args.id_eos, id_unk=args.id_unk,
max_sequence_length=args.max_sequence_length, vocab_size=args.vocab_size,
gpu=args.gpu
)
train_data_loader.create_batches(args.train_file_list, args.train_label_list, if_shuffle=True)
eval_data_loader=non_pair_data_loader(
# batch_size=args.batch_size, id_bos=args.id_bos,
batch_size=200, id_bos=args.id_bos,
id_eos=args.id_eos, id_unk=args.id_unk,
max_sequence_length=args.max_sequence_length, vocab_size=args.vocab_size,
gpu=args.gpu
)
eval_file_list=[
args.data_path+'sentiment.dev.0',
def generation(ae_model, sm, args):
eval_data_loader = non_pair_data_loader(
batch_size=1,
id_bos=args.id_bos,
id_eos=args.id_eos,
id_unk=args.id_unk,
max_sequence_length=args.max_sequence_length,
vocab_size=args.vocab_size,
gpu=args.gpu)
eval_file_list = [
args.data_path + 'sentiment.test.0',
args.data_path + 'sentiment.test.1'
]
eval_label_list = [[0], [1]]
eval_data_loader.create_batches(eval_file_list,
eval_label_list,
if_shuffle=False)
ae_model.eval()
sent_dic = {0: 'chatbot', 1: 'bible'}
trans_pos = list()
dif_pos = list()
trans_neg = list()
dif_neg = list()
pred = list()
true = list()
for it in range(eval_data_loader.num_batch):
####################
#####load data######
####################
batch_sentences, tensor_labels, \
tensor_src, tensor_src_mask, tensor_tgt, tensor_tgt_y, \
tensor_tgt_mask, tensor_ntokens = eval_data_loader.next_batch()
latent = ae_model.getLatent(tensor_src, tensor_src_mask)
style, similarity = ae_model.getSim(latent)
sign = 2 * (tensor_labels.long()) - 1
t_sign = 2 * (1 - tensor_labels.long()) - 1
#style_prob=ae_model.sigmoid(similarity)
trans_emb = style.clone()[torch.arange(style.size(0)),
(1 - tensor_labels).long().item()]
own_emb = style.clone()[torch.arange(style.size(0)),
tensor_labels.long().item()]
w = args.weight
out_1 = ae_model.greedy_decode(
latent + sign * w * (trans_emb + own_emb),
args.max_sequence_length, args.id_bos)
style_1 = sm.DecodeIds(id2list(out_1[0].tolist()))
add_output(
style_1,
'./generation/{}/sign_{}_add.txt'.format(args.name, args.weight))
out_3 = ae_model.greedy_decode(latent + sign * w * (own_emb),
args.max_sequence_length, args.id_bos)
style_3 = sm.DecodeIds(id2list(out_3[0].tolist()))
add_output(
style_3,
'./generation/{}/sign_{}_own.txt'.format(args.name, args.weight))
sent = sent_dic[tensor_labels.item()]
trans = sent_dic[1 - tensor_labels.item()]
print("------------%d------------" % it)
print('original {}:'.format(sent),
sm.DecodeIds(tensor_tgt_y[0].tolist()))
print('s:{} w:{} {}:'.format(t_sign.item(), args.weight, trans),
style_3)
def sedat_eval(args, ae_model, f, deb):
"""
Input:
Original latent representation z : (n_batch, batch_size, seq_length, latent_size)
Output:
An optimal modified latent representation z'
"""
max_sequence_length = args.max_sequence_length
id_bos = args.id_bos
id_to_word = args.id_to_word
limit_batches = args.limit_batches
eval_data_loader = non_pair_data_loader(
batch_size=args.batch_size,
id_bos=args.id_bos,
id_eos=args.id_eos,
id_unk=args.id_unk,
max_sequence_length=args.max_sequence_length,
vocab_size=args.vocab_size)
file_list = [args.test_data_file]
eval_data_loader.create_batches(args,
file_list,
if_shuffle=False,
n_samples=args.test_n_samples)
if args.references_files:
gold_ans = load_human_answer(args.references_files, args.text_column)
assert len(gold_ans) == eval_data_loader.num_batch
else:
gold_ans = None
add_log(args, "Start eval process.")
ae_model.eval()
f.eval()
deb.eval()
text_z_prime = {}
text_z_prime = {
"source": [],
"origin_labels": [],
"before": [],
"after": [],
"change": [],
"pred_label": []
}
if gold_ans is not None:
text_z_prime["gold_ans"] = []
z_prime = []
n_batches = 0
for it in tqdm.tqdm(list(range(eval_data_loader.num_batch)), desc="SEDAT"):
_, tensor_labels, \
tensor_src, tensor_src_mask, tensor_src_attn_mask, tensor_tgt, tensor_tgt_y, \
tensor_tgt_mask, _ = eval_data_loader.next_batch()
# only on negative example
negative_examples = ~(tensor_labels.squeeze() == args.positive_label)
tensor_labels = tensor_labels[negative_examples].squeeze(
0) # .view(1, -1)
tensor_src = tensor_src[negative_examples].squeeze(0)
tensor_src_mask = tensor_src_mask[negative_examples].squeeze(0)
tensor_src_attn_mask = tensor_src_attn_mask[negative_examples].squeeze(
0)
tensor_tgt_y = tensor_tgt_y[negative_examples].squeeze(0)
tensor_tgt = tensor_tgt[negative_examples].squeeze(0)
tensor_tgt_mask = tensor_tgt_mask[negative_examples].squeeze(0)
if negative_examples.any():
if gold_ans is not None:
text_z_prime["gold_ans"].append(gold_ans[it])
text_z_prime["source"].append(
[id2text_sentence(t, args.id_to_word) for t in tensor_tgt_y])
text_z_prime["origin_labels"].append(tensor_labels.cpu().numpy())
origin_data, _ = ae_model.forward(tensor_src, tensor_tgt,
tensor_src_mask,
tensor_src_attn_mask,
tensor_tgt_mask)
generator_id = ae_model.greedy_decode(origin_data,
max_len=max_sequence_length,
start_id=id_bos)
generator_text = [
id2text_sentence(gid, id_to_word) for gid in generator_id
]
text_z_prime["before"].append(generator_text)
data = deb(origin_data, mask=None)
data = torch.sum(ae_model.sigmoid(data),
dim=1) # (batch_size, d_model)
#logit = ae_model.decode(data.unsqueeze(1), tensor_tgt, tensor_tgt_mask) # (batch_size, max_tgt_seq, d_model)
#output = ae_model.generator(logit) # (batch_size, max_seq, vocab_size)
y_hat = f.forward(data)
y_hat = y_hat.round().int()
z_prime.append(data)
generator_id = ae_model.greedy_decode(data,
max_len=max_sequence_length,
start_id=id_bos)
generator_text = [
id2text_sentence(gid, id_to_word) for gid in generator_id
]
text_z_prime["after"].append(generator_text)
text_z_prime["change"].append([True] * len(y_hat))
text_z_prime["pred_label"].append([y_.item() for y_ in y_hat])
n_batches += 1
if n_batches > limit_batches:
break
write_text_z_in_file(args, text_z_prime)
add_log(args, "")
add_log(args,
"Saving model modify embedding %s ..." % args.current_save_path)
torch.save(z_prime,
os.path.join(args.current_save_path, 'z_prime_sedat.pkl'))
return z_prime, text_z_prime
def sedat_train(args, ae_model, f, deb):
"""
Input:
Original latent representation z : (n_batch, batch_size, seq_length, latent_size)
Output:
An optimal modified latent representation z'
"""
# TODO : fin a metric to control the evelotuion of training, mainly for deb model
lambda_ = args.sedat_threshold
alpha, beta = [float(coef) for coef in args.sedat_alpha_beta.split(",")]
# only on negative example
only_on_negative_example = args.sedat_only_on_negative_example
penalty = args.penalty
type_penalty = args.type_penalty
assert penalty in ["lasso", "ridge"]
assert type_penalty in ["last", "group"]
train_data_loader = non_pair_data_loader(
batch_size=args.batch_size,
id_bos=args.id_bos,
id_eos=args.id_eos,
id_unk=args.id_unk,
max_sequence_length=args.max_sequence_length,
vocab_size=args.vocab_size)
file_list = [args.train_data_file]
if os.path.exists(args.val_data_file):
file_list.append(args.val_data_file)
train_data_loader.create_batches(args,
file_list,
if_shuffle=True,
n_samples=args.train_n_samples)
add_log(args, "Start train process.")
#add_log("Start train process.")
ae_model.train()
f.train()
deb.train()
ae_optimizer = get_optimizer(parameters=ae_model.parameters(),
s=args.ae_optimizer,
noamopt=args.ae_noamopt)
dis_optimizer = get_optimizer(parameters=f.parameters(),
s=args.dis_optimizer)
deb_optimizer = get_optimizer(parameters=deb.parameters(),
s=args.dis_optimizer)
ae_criterion = get_cuda(
LabelSmoothing(size=args.vocab_size,
padding_idx=args.id_pad,
smoothing=0.1), args)
dis_criterion = nn.BCELoss(size_average=True)
deb_criterion = LossSedat(penalty=penalty)
stats = []
for epoch in range(args.max_epochs):
print('-' * 94)
epoch_start_time = time.time()
loss_ae, n_words_ae, xe_loss_ae, n_valid_ae = 0, 0, 0, 0
loss_clf, total_clf, n_valid_clf = 0, 0, 0
for it in range(train_data_loader.num_batch):
_, tensor_labels, \
tensor_src, tensor_src_mask, tensor_src_attn_mask, tensor_tgt, tensor_tgt_y, \
tensor_tgt_mask, _ = train_data_loader.next_batch()
flag = True
# only on negative example
if only_on_negative_example:
negative_examples = ~(tensor_labels.squeeze()
== args.positive_label)
tensor_labels = tensor_labels[negative_examples].squeeze(
0) # .view(1, -1)
tensor_src = tensor_src[negative_examples].squeeze(0)
tensor_src_mask = tensor_src_mask[negative_examples].squeeze(0)
tensor_src_attn_mask = tensor_src_attn_mask[
negative_examples].squeeze(0)
tensor_tgt_y = tensor_tgt_y[negative_examples].squeeze(0)
tensor_tgt = tensor_tgt[negative_examples].squeeze(0)
tensor_tgt_mask = tensor_tgt_mask[negative_examples].squeeze(0)
flag = negative_examples.any()
if flag:
# forward
z, out, z_list = ae_model.forward(tensor_src,
tensor_tgt,
tensor_src_mask,
tensor_src_attn_mask,
tensor_tgt_mask,
return_intermediate=True)
#y_hat = f.forward(to_var(z.clone()))
y_hat = f.forward(z)
loss_dis = dis_criterion(y_hat, tensor_labels)
dis_optimizer.zero_grad()
loss_dis.backward(retain_graph=True)
dis_optimizer.step()
dis_lop = f.forward(z)
t_c = tensor_labels.view(-1).size(0)
n_v = (dis_lop.round().int() == tensor_labels).sum().item()
loss_clf += loss_dis.item()
total_clf += t_c
n_valid_clf += n_v
clf_acc = 100. * n_v / (t_c + eps)
avg_clf_acc = 100. * n_valid_clf / (total_clf + eps)
avg_clf_loss = loss_clf / (it + 1)
mask_deb = y_hat.squeeze(
) >= lambda_ if args.positive_label == 0 else y_hat.squeeze(
) < lambda_
# if f(z) > lambda :
if mask_deb.any():
y_hat_deb = y_hat[mask_deb]
if type_penalty == "last":
z_deb = z[mask_deb].squeeze(
0) if args.batch_size == 1 else z[mask_deb]
elif type_penalty == "group":
# TODO : unit test for bach_size = 1
z_deb = z_list[-1][mask_deb]
z_prime, z_prime_list = deb(z_deb,
mask=None,
return_intermediate=True)
if type_penalty == "last":
z_prime = torch.sum(ae_model.sigmoid(z_prime), dim=1)
loss_deb = alpha * deb_criterion(
z_deb, z_prime,
is_list=False) + beta * y_hat_deb.sum()
elif type_penalty == "group":
z_deb_list = [z_[mask_deb] for z_ in z_list]
#assert len(z_deb_list) == len(z_prime_list)
loss_deb = alpha * deb_criterion(
z_deb_list, z_prime_list,
is_list=True) + beta * y_hat_deb.sum()
deb_optimizer.zero_grad()
loss_deb.backward(retain_graph=True)
deb_optimizer.step()
else:
loss_deb = torch.tensor(float("nan"))
# else :
if (~mask_deb).any():
out_ = out[~mask_deb]
tensor_tgt_y_ = tensor_tgt_y[~mask_deb]
tensor_ntokens = (tensor_tgt_y_ != 0).data.sum().float()
loss_rec = ae_criterion(
out_.contiguous().view(-1, out_.size(-1)),
tensor_tgt_y_.contiguous().view(-1)) / (
tensor_ntokens.data + eps)
else:
loss_rec = torch.tensor(float("nan"))
ae_optimizer.zero_grad()
(loss_dis + loss_deb + loss_rec).backward()
ae_optimizer.step()
if True:
n_v, n_w = get_n_v_w(tensor_tgt_y, out)
else:
n_w = float("nan")
n_v = float("nan")
x_e = loss_rec.item() * n_w
loss_ae += loss_rec.item()
n_words_ae += n_w
xe_loss_ae += x_e
n_valid_ae += n_v
ae_acc = 100. * n_v / (n_w + eps)
avg_ae_acc = 100. * n_valid_ae / (n_words_ae + eps)
avg_ae_loss = loss_ae / (it + 1)
ae_ppl = np.exp(x_e / (n_w + eps))
avg_ae_ppl = np.exp(xe_loss_ae / (n_words_ae + eps))
x_e = loss_rec.item() * n_w
loss_ae += loss_rec.item()
n_words_ae += n_w
xe_loss_ae += x_e
n_valid_ae += n_v
if it % args.log_interval == 0:
add_log(args, "")
add_log(
args, 'epoch {:3d} | {:5d}/{:5d} batches |'.format(
epoch, it, train_data_loader.num_batch))
add_log(
args,
'Train : rec acc {:5.4f} | rec loss {:5.4f} | ppl {:5.4f} | dis acc {:5.4f} | dis loss {:5.4f} |'
.format(ae_acc, loss_rec.item(), ae_ppl, clf_acc,
loss_dis.item()))
add_log(
args,
'Train : avg : rec acc {:5.4f} | rec loss {:5.4f} | ppl {:5.4f} | dis acc {:5.4f} | diss loss {:5.4f} |'
.format(avg_ae_acc, avg_ae_loss, avg_ae_ppl,
avg_clf_acc, avg_clf_loss))
add_log(
args, "input : %s" %
id2text_sentence(tensor_tgt_y[0], args.id_to_word))
generator_text = ae_model.greedy_decode(
z,
max_len=args.max_sequence_length,
start_id=args.id_bos)
# batch_sentences
add_log(
args, "gen : %s" %
id2text_sentence(generator_text[0], args.id_to_word))
if mask_deb.any():
generator_text_prime = ae_model.greedy_decode(
z_prime,
max_len=args.max_sequence_length,
start_id=args.id_bos)
add_log(
args, "deb : %s" % id2text_sentence(
generator_text_prime[0], args.id_to_word))
s = {}
L = train_data_loader.num_batch + eps
s["train_ae_loss"] = loss_ae / L
s["train_ae_acc"] = 100. * n_valid_ae / (n_words_ae + eps)
s["train_ae_ppl"] = np.exp(xe_loss_ae / (n_words_ae + eps))
s["train_clf_loss"] = loss_clf / L
s["train_clf_acc"] = 100. * n_valid_clf / (total_clf + eps)
stats.append(s)
add_log(args, "")
add_log(
args, '| end of epoch {:3d} | time: {:5.2f}s |'.format(
epoch, (time.time() - epoch_start_time)))
add_log(
args,
'| rec acc {:5.4f} | rec loss {:5.4f} | rec ppl {:5.4f} | dis acc {:5.4f} | dis loss {:5.4f} |'
.format(s["train_ae_acc"], s["train_ae_loss"], s["train_ae_ppl"],
s["train_clf_acc"], s["train_clf_loss"]))
# Save model
torch.save(
ae_model.state_dict(),
os.path.join(args.current_save_path, 'ae_model_params_deb.pkl'))
torch.save(
f.state_dict(),
os.path.join(args.current_save_path, 'dis_model_params_deb.pkl'))
torch.save(
deb.state_dict(),
os.path.join(args.current_save_path, 'deb_model_params_deb.pkl'))
add_log(args, "Saving training statistics %s ..." % args.current_save_path)
torch.save(stats,
os.path.join(args.current_save_path, 'stats_train_deb.pkl'))
def fgim_algorithm(args, ae_model, dis_model):
batch_size = 1
test_data_loader = non_pair_data_loader(
batch_size=batch_size,
id_bos=args.id_bos,
id_eos=args.id_eos,
id_unk=args.id_unk,
max_sequence_length=args.max_sequence_length,
vocab_size=args.vocab_size)
file_list = [args.test_data_file]
test_data_loader.create_batches(args,
file_list,
if_shuffle=False,
n_samples=args.test_n_samples)
if args.references_files:
gold_ans = load_human_answer(args.references_files, args.text_column)
assert len(gold_ans) == test_data_loader.num_batch
else:
gold_ans = [[None] * batch_size] * test_data_loader.num_batch
add_log(args, "Start eval process.")
ae_model.eval()
dis_model.eval()
fgim_our = True
if fgim_our:
# for FGIM
z_prime, text_z_prime = fgim(test_data_loader,
args,
ae_model,
dis_model,
gold_ans=gold_ans)
write_text_z_in_file(args, text_z_prime)
add_log(
args,
"Saving model modify embedding %s ..." % args.current_save_path)
torch.save(z_prime,
os.path.join(args.current_save_path, 'z_prime_fgim.pkl'))
else:
for it in range(test_data_loader.num_batch):
batch_sentences, tensor_labels, \
tensor_src, tensor_src_mask, tensor_src_attn_mask, tensor_tgt, tensor_tgt_y, \
tensor_tgt_mask, tensor_ntokens = test_data_loader.next_batch()
print("------------%d------------" % it)
print(id2text_sentence(tensor_tgt_y[0], args.id_to_word))
print("origin_labels", tensor_labels)
latent, out = ae_model.forward(tensor_src, tensor_tgt,
tensor_src_mask,
tensor_src_attn_mask,
tensor_tgt_mask)
generator_text = ae_model.greedy_decode(
latent, max_len=args.max_sequence_length, start_id=args.id_bos)
print(id2text_sentence(generator_text[0], args.id_to_word))
# Define target label
target = get_cuda(torch.tensor([[1.0]], dtype=torch.float), args)
if tensor_labels[0].item() > 0.5:
target = get_cuda(torch.tensor([[0.0]], dtype=torch.float),
args)
add_log(args, "target_labels : %s" % target)
modify_text = fgim_attack(dis_model, latent, target, ae_model,
args.max_sequence_length, args.id_bos,
id2text_sentence, args.id_to_word,
gold_ans[it])
add_output(args, modify_text)
def pretrain(args, ae_model, dis_model):
train_data_loader = non_pair_data_loader(
batch_size=args.batch_size,
id_bos=args.id_bos,
id_eos=args.id_eos,
id_unk=args.id_unk,
max_sequence_length=args.max_sequence_length,
vocab_size=args.vocab_size)
train_data_loader.create_batches(args, [args.train_data_file],
if_shuffle=True,
n_samples=args.train_n_samples)
val_data_loader = non_pair_data_loader(
batch_size=args.batch_size,
id_bos=args.id_bos,
id_eos=args.id_eos,
id_unk=args.id_unk,
max_sequence_length=args.max_sequence_length,
vocab_size=args.vocab_size)
val_data_loader.create_batches(args, [args.val_data_file],
if_shuffle=True,
n_samples=args.valid_n_samples)
ae_model.train()
dis_model.train()
ae_optimizer = get_optimizer(parameters=ae_model.parameters(),
s=args.ae_optimizer,
noamopt=args.ae_noamopt)
dis_optimizer = get_optimizer(parameters=dis_model.parameters(),
s=args.dis_optimizer)
ae_criterion = get_cuda(
LabelSmoothing(size=args.vocab_size,
padding_idx=args.id_pad,
smoothing=0.1), args)
dis_criterion = nn.BCELoss(size_average=True)
possib = [
"%s_%s" % (i, j) for i, j in itertools.product(
["train", "eval"],
["ae_loss", "ae_acc", "ae_ppl", "clf_loss", "clf_acc"])
]
stopping_criterion, best_criterion, decrease_counts, decrease_counts_max = settings(
args, possib)
metric, biggest = stopping_criterion
factor = 1 if biggest else -1
stats = []
add_log(args, "Start train process.")
for epoch in range(args.max_epochs):
print('-' * 94)
add_log(args, "")
s_train = train_step(args, train_data_loader, ae_model, dis_model,
ae_optimizer, dis_optimizer, ae_criterion,
dis_criterion, epoch)
add_log(args, "")
s_eval = eval_step(args, val_data_loader, ae_model, dis_model,
ae_criterion, dis_criterion)
scores = {**s_train, **s_eval}
stats.append(scores)
add_log(args, "")
if factor * scores[metric] > factor * best_criterion:
best_criterion = scores[metric]
add_log(args, "New best validation score: %f" % best_criterion)
decrease_counts = 0
# Save model
add_log(args, "Saving model to %s ..." % args.current_save_path)
torch.save(
ae_model.state_dict(),
os.path.join(args.current_save_path, 'ae_model_params.pkl'))
torch.save(
dis_model.state_dict(),
os.path.join(args.current_save_path, 'dis_model_params.pkl'))
else:
add_log(
args, "Not a better validation score (%i / %i)." %
(decrease_counts, decrease_counts_max))
decrease_counts += 1
if decrease_counts > decrease_counts_max:
add_log(
args,
"Stopping criterion has been below its best value for more "
"than %i epochs. Ending the experiment..." %
decrease_counts_max)
#exit()
break
s_test = None
if os.path.exists(args.test_data_file):
add_log(args, "")
test_data_loader = non_pair_data_loader(
batch_size=args.batch_size,
id_bos=args.id_bos,
id_eos=args.id_eos,
id_unk=args.id_unk,
max_sequence_length=args.max_sequence_length,
vocab_size=args.vocab_size)
test_data_loader.create_batches(args, [args.test_data_file],
if_shuffle=True,
n_samples=args.test_n_samples)
s = eval_step(args, test_data_loader, ae_model, dis_model,
ae_criterion, dis_criterion)
add_log(
args,
'Test | rec acc {:5.4f} | rec loss {:5.4f} | rec ppl {:5.4f} | dis acc {:5.4f} | dis loss {:5.4f} |'
.format(s["eval_ae_acc"], s["eval_ae_loss"], s["eval_ae_ppl"],
s["eval_clf_acc"], s["eval_clf_loss"]))
s_test = s
add_log(args, "")
add_log(args, "Saving training statistics %s ..." % args.current_save_path)
torch.save(stats,
os.path.join(args.current_save_path, 'stats_train_eval.pkl'))
if s_test is not None:
torch.save(s_test, os.path.join(args.current_save_path,
'stat_test.pkl'))
return stats, s_test
