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main.py
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import dataset as dt
import os
import params
import re
import siamese
import torch
import torch.nn.functional as F
from datetime import datetime
from mosestokenizer import MosesTokenizer
from torch.utils.data import DataLoader
# Initialization
tokenizer = MosesTokenizer('en')
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print(device)
# Load word dictionary
word_dic = {}
with open(os.path.join('dataset', 'wordvector', 'word_to_id.txt'), 'r', encoding='utf-8') as f:
i = 1
for line in f:
word = line.strip()
word_dic[word] = i
i += 1
def get_token(text):
# text = ' '.join(re.findall(r'\w+', text, flags=re.UNICODE))
tokens = tokenizer(text)
if len(tokens) == 0:
return ['N/A']
else:
return tokens
def get_embed_id(word):
if word == 'sepunktoken':
return 0
else:
return word_dic.get(word, 0)
def get_padded_tensor(texts):
text_t = [torch.tensor([get_embed_id(w) for w in get_token(text)], dtype=torch.long, device=device) for text in texts]
text_l = [a.size(0) for a in text_t]
text_max = max(text_l)
if text_max < 3:
text_max = 3
text_p = [text_max - a for a in text_l]
text_tp = [F.pad(a.view(1,1,1,-1), (0, text_p[i], 0, 0)).view(1,-1) for i, a in enumerate(text_t)]
text_tp = torch.cat(text_tp, 0)
return text_tp
def build_tensor(rels_kb, rels_oie, labels):
label_t = torch.tensor(labels, dtype=torch.float32, device=device)
rel_kb_t = get_padded_tensor(rels_kb)
rel_oie_t = get_padded_tensor(rels_oie)
return rel_kb_t, rel_oie_t, label_t
def eval(loader, model):
model.eval()
avg_loss, size = 0, 0
c_loss = siamese.ContrastiveLoss()
for rel_kb, rel_oie, label in loader:
rel_kb_t, rel_oie_t, label_t = build_tensor(rel_kb, rel_oie, label)
output1, output2 = model(rel_kb_t, rel_oie_t)
loss = c_loss(output1, output2, label_t)
avg_loss += loss.data[0]
size += 1
avg_loss = avg_loss / size
print('Evaluation - Current loss {}'.format(avg_loss.data[0]))
def save(model, save_dir, save_prefix, steps):
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
save_prefix = os.path.join(save_dir, save_prefix)
if steps > 0:
save_path = '{}_steps_{}.pt'.format(save_prefix, steps)
else:
save_path = '{}.pt'.format(save_prefix)
torch.save(model.state_dict(), save_path)
def train(model, train_loader, valid_loader, args):
parameters = filter(lambda p: p.requires_grad, model.parameters())
optimizer = torch.optim.Adam(parameters, lr=args.lr)
model.train()
steps = 0
c_loss = siamese.ContrastiveLoss()
for epoch in range(1, args.epochs + 1):
print('\nStart epoch', epoch)
for rel_kb, rel_oie, label in train_loader:
rel_kb_t, rel_oie_t, label_t = build_tensor(rel_kb, rel_oie, label)
optimizer.zero_grad()
output1, output2 = model(rel_kb_t, rel_oie_t)
loss = c_loss(output1, output2, label_t)
loss.backward()
optimizer.step()
steps += 1
if steps % args.log_interval == 0:
print('Batch[{}] - Current loss: {:.6f}'.format(steps, loss.data[0]))
# Eval validation data
eval(valid_loader, model)
# if epoch % args.save_interval == 0:
# save(model, args.save_dir, args.mode + '_model_temp', steps)
def test(test_loader, model, args):
predict = []
# restore the best parameters
print('Load model parameters...')
model_file = os.path.join(args.save_dir, args.model_filename + '.pt')
model.load_state_dict(torch.load(model_file, map_location=lambda storage, loc: storage))
model.eval()
corrects = 0
print('Predict test data...')
with torch.no_grad():
for rel_kb, rel_oie, label in test_loader:
rel_kb_t, rel_oie_t, label_t = build_tensor(rel_kb, rel_oie, label)
output1, output2 = model(rel_kb_t, rel_oie_t)
euclidean_dist = F.pairwise_distance(output1, output2)
gold = label_t.cpu().numpy().tolist()
dist = euclidean_dist.cpu().numpy().tolist()
for i in range(len(gold)):
predict.append((rel_kb[i], rel_oie[i], dist[i], gold[i], output1[i], output2[i]))
return predict
def main():
# Load parameters
args = params.args()
# Load train, valid, and test data
print('[' + datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '] Loading dataset')
train_dataset = dt.MyDataset(args.train_filename, args.mode)
valid_dataset = dt.MyDataset(args.valid_filename, args.mode)
test_dataset = dt.MyDataset(args.test_filename, args.mode)
gold_dataset = dt.MyDataset(args.gold_filename, args.mode)
print('train, valid, test num:', len(train_dataset), len(valid_dataset), len(test_dataset))
# Load dataset to DataLoader
train_loader = DataLoader(dataset=train_dataset, batch_size=args.BATCH_SIZE, shuffle=True)
valid_loader = DataLoader(dataset=valid_dataset, batch_size=args.BATCH_SIZE, shuffle=False)
test_loader = DataLoader(dataset=test_dataset, batch_size=args.BATCH_SIZE, shuffle=False)
gold_loader = DataLoader(dataset=gold_dataset, batch_size=args.BATCH_SIZE, shuffle=False)
# Initialize model
model = siamese.SiameseNetwork(args)
model.to(device)
# Train model
print('[' + datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '] Start training')
try:
train(model, train_loader, valid_loader, args)
except KeyboardInterrupt:
print('\n' + '-' * 89)
print('Exit from training early')
# Save final model
save(model, args.save_dir, args.model_filename, -1)
print('[' + datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '] Training finished')
# Test model
print('[' + datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '] Start prediction')
predict = test(test_loader, model, args)
pred_filename = args.predict_dir + '/predict_result.tsv'
with open(pred_filename, 'w') as f:
for item in predict:
f.write(item[0] + '\t' + item[1] + '\t' + str(item[2]) + '\t' + str(item[3]) + '\n')
f.closed
print('Successfully save prediction result to', pred_filename)
with open(args.predict_dir + '/rel_embed_vector.tsv', 'w') as f:
for item in predict:
out1 = item[5].cpu().numpy().tolist()
f.write('\t'.join(str(x) for x in out1))
f.write('\n')
f.closed
with open(args.predict_dir + '/rel_embed_label.tsv', 'w') as f:
for item in predict:
f.write(item[1])
f.write('\n')
f.closed
print('[' + datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '] Prediction finished')
# Gold Prediction
print('[' + datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '] Start gold prediction')
predict = test(gold_loader, model, args)
pred_filename = args.gold_dir + '/predict_result.tsv'
with open(pred_filename, 'w') as f:
for item in predict:
f.write(item[0] + '\t' + item[1] + '\t' + str(item[2]) + '\t' + str(item[3]) + '\n')
f.closed
print('Successfully save prediction result to', pred_filename)
with open(args.gold_dir + '/rel_embed_vector.tsv', 'w') as f:
for item in predict:
out1 = item[5].cpu().numpy().tolist()
f.write('\t'.join(str(x) for x in out1))
f.write('\n')
f.closed
with open(args.gold_dir + '/rel_embed_label.tsv', 'w') as f:
for item in predict:
f.write(item[1])
f.write('\n')
f.closed
print('[' + datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '] Gold prediction finished')
if __name__ == '__main__':
main()