def run():
utils.set_logger(config.log_path)
train_dataset = MTDataset(config.train_data_path)
dev_dataset = MTDataset(config.dev_data_path)
test_dataset = MTDataset(config.test_data_path)
logging.info("-------- Dataset Build! --------")
train_dataloader = DataLoader(train_dataset,
shuffle=True,
batch_size=config.batch_size,
collate_fn=train_dataset.collate_fn)
dev_dataloader = DataLoader(dev_dataset,
shuffle=False,
batch_size=config.batch_size,
collate_fn=dev_dataset.collate_fn)
test_dataloader = DataLoader(test_dataset,
shuffle=False,
batch_size=config.batch_size,
collate_fn=test_dataset.collate_fn)
logging.info("-------- Get Dataloader! --------")
# 初始化模型
model = make_model(config.src_vocab_size, config.tgt_vocab_size,
config.n_layers, config.d_model, config.d_ff,
config.n_heads, config.dropout)
model_par = torch.nn.DataParallel(model)
# 训练
if config.use_smoothing:
criterion = LabelSmoothing(size=config.tgt_vocab_size,
padding_idx=config.padding_idx,
smoothing=0.1)
criterion.cuda()
else:
criterion = torch.nn.CrossEntropyLoss(ignore_index=0, reduction='sum')
if config.use_noamopt:
optimizer = get_std_opt(model)
else:
optimizer = torch.optim.AdamW(model.parameters(), lr=config.lr)
train(train_dataloader, dev_dataloader, model, model_par, criterion,
optimizer)
test(test_dataloader, model, criterion)
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
def train_iters(ae_model, dis_model):
if args.use_albert:
tokenizer = BertTokenizer.from_pretrained("clue/albert_chinese_tiny",
do_lower_case=True)
elif args.use_tiny_bert:
tokenizer = AutoTokenizer.from_pretrained(
"google/bert_uncased_L-2_H-256_A-4", do_lower_case=True)
elif args.use_distil_bert:
tokenizer = DistilBertTokenizer.from_pretrained(
'distilbert-base-uncased', do_lower_case=True)
# tokenizer = BertTokenizer.from_pretrained(args.PRETRAINED_MODEL_NAME, do_lower_case=True)
tokenizer.add_tokens('[EOS]')
bos_id = tokenizer.convert_tokens_to_ids(['[CLS]'])[0]
ae_model.bert_encoder.resize_token_embeddings(len(tokenizer))
#print("[CLS] ID: ", bos_id)
print("Load trainData...")
if args.load_trainData and os.path.exists('./{}_trainData.pkl'.format(
args.task)):
with open('./{}_trainData.pkl'.format(args.task), 'rb') as f:
trainData = pickle.load(f)
else:
trainData = TextDataset(batch_size=args.batch_size,
id_bos='[CLS]',
id_eos='[EOS]',
id_unk='[UNK]',
max_sequence_length=args.max_sequence_length,
vocab_size=0,
file_list=args.train_file_list,
label_list=args.train_label_list,
tokenizer=tokenizer)
with open('./{}_trainData.pkl'.format(args.task), 'wb') as f:
pickle.dump(trainData, f)
add_log("Start train process.")
ae_model.train()
dis_model.train()
ae_model.to(device)
dis_model.to(device)
'''
Fixing or distilling BERT encoder
'''
if args.fix_first_6:
print("Try fixing first 6 bertlayers")
for layer in range(6):
for param in ae_model.bert_encoder.encoder.layer[layer].parameters(
):
param.requires_grad = False
elif args.fix_last_6:
print("Try fixing last 6 bertlayers")
for layer in range(6, 12):
for param in ae_model.bert_encoder.encoder.layer[layer].parameters(
):
param.requires_grad = False
if args.distill_2:
print("Get result from layer 2")
for layer in range(2, 12):
for param in ae_model.bert_encoder.encoder.layer[layer].parameters(
):
param.requires_grad = False
ae_optimizer = NoamOpt(
ae_model.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))
ae_criterion = LabelSmoothing(size=ae_model.bert_encoder.config.vocab_size,
padding_idx=0,
smoothing=0.1).to(device)
dis_criterion = nn.BCELoss(reduction='mean')
history = {'train': []}
for epoch in range(args.epochs):
print('-' * 94)
epoch_start_time = time.time()
total_rec_loss = 0
total_dis_loss = 0
train_data_loader = DataLoader(trainData,
batch_size=args.batch_size,
shuffle=True,
collate_fn=trainData.collate_fn,
num_workers=4)
num_batch = len(train_data_loader)
trange = tqdm(enumerate(train_data_loader),
total=num_batch,
desc='Training',
file=sys.stdout,
position=0,
leave=True)
for it, data in trange:
batch_sentences, tensor_labels, tensor_src, tensor_src_mask, tensor_tgt, tensor_tgt_y, tensor_tgt_mask, tensor_ntokens = data
tensor_labels = tensor_labels.to(device)
tensor_src = tensor_src.to(device)
tensor_tgt = tensor_tgt.to(device)
tensor_tgt_y = tensor_tgt_y.to(device)
tensor_src_mask = tensor_src_mask.to(device)
tensor_tgt_mask = tensor_tgt_mask.to(device)
# Forward pass
latent, out = ae_model.forward(tensor_src, tensor_tgt,
tensor_src_mask, tensor_tgt_mask)
# Loss calculation
loss_rec = ae_criterion(
out.contiguous().view(-1, out.size(-1)),
tensor_tgt_y.contiguous().view(-1)) / tensor_ntokens.data
ae_optimizer.optimizer.zero_grad()
loss_rec.backward()
ae_optimizer.step()
latent = latent.detach()
next_latent = latent.to(device)
# Classifier
dis_lop = dis_model.forward(next_latent)
loss_dis = dis_criterion(dis_lop, tensor_labels)
dis_optimizer.zero_grad()
loss_dis.backward()
dis_optimizer.step()
total_rec_loss += loss_rec.item()
total_dis_loss += loss_dis.item()
trange.set_postfix(total_rec_loss=total_rec_loss / (it + 1),
total_dis_loss=total_dis_loss / (it + 1))
if it % 100 == 0:
add_log(
'| epoch {:3d} | {:5d}/{:5d} batches | rec loss {:5.4f} | dis loss {:5.4f} |'
.format(epoch, it, num_batch, loss_rec, loss_dis))
print(id2text_sentence(tensor_tgt_y[0], tokenizer, args.task))
generator_text = ae_model.greedy_decode(
latent, max_len=args.max_sequence_length, start_id=bos_id)
print(id2text_sentence(generator_text[0], tokenizer,
args.task))
# 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')
history['train'].append({
'epoch': epoch,
'total_rec_loss': total_rec_loss / len(trange),
'total_dis_loss': total_dis_loss / len(trange)
})
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')
print("Save in ", args.current_save_path)
return
print('<<<<< Evaluate loss: %f' % dev_loss)
# 如果当前epoch的模型在dev集上的loss优于之前记录的最优loss则保存当前模型，并更新最优loss值
if dev_loss < best_dev_loss:
torch.save(model.state_dict(), SAVE_FILE)
best_dev_loss = dev_loss
print('****** Save model done... ******')
print()
if __name__ == '__main__':
print('处理数据')
data = PrepareData(TRAIN_FILE, DEV_FILE)
print('>>>开始训练')
train_start = time.time()
# 损失函数
criterion = LabelSmoothing(TGT_VOCAB, padding_idx=0, smoothing=0.0)
# 优化器
optimizer = NoamOpt(
D_MODEL, 1, 2000,
torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98),
eps=1e-9))
train(data, model, criterion, optimizer)
print(f'<<<训练结束, 花费时间 {time.time() - train_start:.4f}秒')
# 对测试数据集进行测试
# print('开始测试')
# from test import evaluate_test
# evaluate_test(data, model)
def val(ae_model, dis_model, eval_data_loader, epoch, args):
print("Transformer Validation process....")
ae_model.eval()
print('-' * 94)
epoch_start = time.time()
loss_ae = list()
loss_dis = list()
acc = list()
ae_criterion = get_cuda(
LabelSmoothing(size=args.vocab_size,
padding_idx=args.id_pad,
smoothing=0.1), args.gpu)
dis_criterion = torch.nn.BCELoss(size_average=True)
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) #(128, 256)
style, similarity = ae_model.getSim(
latent) #style (128, 2, 256), sim(128, 2)
dis_out = dis_model.forward(similarity)
one = get_cuda(torch.tensor(1), args.gpu)
zero = get_cuda(torch.tensor(0), args.gpu)
style_pred = torch.where(dis_out > 0.5, one, zero)
style_pred = style_pred.reshape(style_pred.size(0))
style_emb = get_cuda(style.clone()[torch.arange(style.size(0)),
tensor_labels.squeeze().long()],
args.gpu) #(128, 256)
add_latent = latent + style_emb #batch, dim
out = ae_model.getOutput(add_latent, tensor_tgt, tensor_tgt_mask)
loss_rec = ae_criterion(
out.contiguous().view(-1, out.size(-1)),
tensor_tgt_y.contiguous().view(-1)) / tensor_ntokens.data
loss_style = dis_criterion(dis_out, tensor_labels)
pred = style_pred.to('cpu').detach().tolist()
true = tensor_labels.squeeze().to('cpu').tolist()
dis_acc = accuracy_score(pred, true)
acc.append(dis_acc)
loss_ae.append(loss_rec.item())
loss_dis.append(loss_style.item())
if it % 200 == 0:
print(
'| epoch {:3d} | {:5d}/{:5d} batches |\n| rec loss {:5.4f} | dis loss {:5.4f} |\n'
.format(epoch, it, eval_data_loader.num_batch, loss_rec.item(),
loss_style.item()))
print('| end of epoch {:3d} | time: {:5.2f}s |'.format(
epoch, (time.time() - epoch_start)))
return np.mean(loss_ae), np.mean(loss_dis), np.mean(acc)
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 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
