def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument("--embeddings_file",
default=None,
type=str,
required=True)
parser.add_argument("--output_dir", default=None, type=str, required=True)
parser.add_argument("--train_language",
default=None,
type=str,
required=True)
parser.add_argument("--train_steps", default=-1, type=int, required=True)
parser.add_argument("--eval_steps", default=-1, type=int, required=True)
parser.add_argument(
"--load_word2vec",
action='store_true',
help=
'if true, load word2vec file for the first time; if false, load generated word-vector csv file'
)
parser.add_argument("--generate_word2vec_csv",
action='store_true',
help='if true, generate word2vec csv file')
## normal parameters
parser.add_argument("--embedding_size", default=300, type=int)
parser.add_argument("--query_maxlen", default=30, type=int)
parser.add_argument("--hidden_size", default=300, type=int)
parser.add_argument("--learning_rate",
default=5e-4,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_classes", default=2, type=int)
parser.add_argument("--dropout", default=0.2, type=float)
parser.add_argument("--do_test",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--do_eval_train",
action='store_true',
help="Whether to run eval on the train set.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--per_gpu_eval_batch_size", default=10, type=int)
parser.add_argument("--per_gpu_train_batch_size", default=10, type=int)
parser.add_argument("--seed", default=1, type=int)
parser.add_argument("--adam_epsilon",
default=1e-8,
type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--gradient_accumulation_steps", default=1, type=int)
args = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
args.n_gpu = torch.cuda.device_count()
# device = torch.device("cpu")
args.device = device
# Set seed
set_seed(args)
logger.info("Training/evaluation parameters %s", args)
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
# Training
if args.do_train:
# build model
logger.info("*** building model ***")
embeddings = load_embeddings(args)
model = ESIM(args.hidden_size,
embeddings=embeddings,
dropout=args.dropout,
num_classes=args.num_classes,
device=args.device)
model.to(args.device)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
args.train_batch_size = args.per_gpu_train_batch_size * max(
1, args.n_gpu)
logger.info("*** Loading training data ***")
train_data = ATEC_Dataset(os.path.join(args.data_dir, 'train.csv'),
os.path.join(args.data_dir, 'vocab.csv'),
args.query_maxlen)
train_loader = DataLoader(train_data,
shuffle=True,
batch_size=args.train_batch_size)
logger.info("*** Loading validation data ***")
dev_data = ATEC_Dataset(os.path.join(args.data_dir, 'dev.csv'),
os.path.join(args.data_dir, 'vocab.csv'),
args.query_maxlen)
dev_loader = DataLoader(dev_data,
shuffle=False,
batch_size=args.eval_batch_size)
num_train_optimization_steps = args.train_steps
# 过滤出需要梯度更新的参数
parameters = filter(lambda p: p.requires_grad, model.parameters())
# optimizer = optim.Adadelta(parameters, params["LEARNING_RATE"])
optimizer = torch.optim.Adam(parameters, lr=args.learning_rate)
# optimizer = torch.optim.Adam(model.parameters(), lr=lr)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
mode="max",
factor=0.85,
patience=0)
criterion = nn.CrossEntropyLoss()
global_step = 0
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_data))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Gradient Accumulation steps = %d",
args.gradient_accumulation_steps)
logger.info(" Num steps = %d", num_train_optimization_steps)
best_acc = 0
model.train()
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
bar = tqdm(range(num_train_optimization_steps),
total=num_train_optimization_steps)
train_loader = cycle(train_loader)
output_dir = args.output_dir + "eval_results_{}_{}_{}_{}_{}_{}".format(
'ESIM', str(args.query_maxlen), str(args.learning_rate),
str(args.train_batch_size), str(args.train_language),
str(args.train_steps))
try:
os.makedirs(output_dir)
except:
pass
output_eval_file = os.path.join(output_dir, 'eval_result.txt')
with open(output_eval_file, "w") as writer:
writer.write('*' * 80 + '\n')
for step in bar:
batch = next(train_loader)
batch = tuple(t.to(device) for t in batch)
q1, q1_lens, q2, q2_lens, labels = batch
# 正常训练
optimizer.zero_grad()
logits, probs = model(q1, q1_lens, q2, q2_lens)
loss = criterion(logits, labels)
if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
tr_loss += loss.item()
train_loss = round(
tr_loss * args.gradient_accumulation_steps / (nb_tr_steps + 1),
4)
bar.set_description("loss {}".format(train_loss))
nb_tr_examples += q1.size(0)
nb_tr_steps += 1
loss.backward()
# 对抗训练
# fgm.attack() # 在embedding上添加对抗扰动
# loss_adv = model(input_ids=input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=label_ids)
# if args.n_gpu > 1:
# loss_adv = loss_adv.mean() # mean() to average on multi-gpu.
# if args.gradient_accumulation_steps > 1:
# loss_adv = loss_adv / args.gradient_accumulation_steps
# loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度
# fgm.restore() # 恢复embedding参数
if (nb_tr_steps + 1) % args.gradient_accumulation_steps == 0:
# scheduler.step()
optimizer.step()
global_step += 1
if (step + 1) % (args.eval_steps *
args.gradient_accumulation_steps) == 0:
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
logger.info("***** Report result *****")
logger.info(" %s = %s", 'global_step', str(global_step))
logger.info(" %s = %s", 'train loss', str(train_loss))
if args.do_eval and (step + 1) % (
args.eval_steps * args.gradient_accumulation_steps) == 0:
if args.do_eval_train:
file_list = ['train.csv', 'dev.csv']
else:
file_list = ['dev.csv']
for file in file_list:
inference_labels = []
gold_labels = []
inference_logits = []
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(dev_data))
logger.info(" Batch size = %d", args.eval_batch_size)
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
for q1, q1_lens, q2, q2_lens, labels in tqdm(dev_loader):
with torch.no_grad():
logits, probs = model(q1, q1_lens, q2, q2_lens)
probs = probs.detach().cpu().numpy()
# print(logits.shape, probs.shape)
# label_ids = labels.to('cpu').numpy()
inference_labels.append(np.argmax(probs, 1))
gold_labels.append(labels)
# eval_loss += tmp_eval_loss.mean().item()
nb_eval_examples += logits.size(0)
nb_eval_steps += 1
gold_labels = np.concatenate(gold_labels, 0)
inference_labels = np.concatenate(inference_labels, 0)
model.train()
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = get_f1(inference_labels, gold_labels)
result = {
# 'eval_loss': eval_loss,
'eval_accuracy': eval_accuracy,
'global_step': global_step,
'train_loss': train_loss
}
if 'dev' in file:
with open(output_eval_file, "a") as writer:
writer.write(file + '\n')
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" %
(key, str(result[key])))
writer.write('*' * 80)
writer.write('\n')
if eval_accuracy > best_acc and 'dev' in file:
print("=" * 80)
print("Best ACC", eval_accuracy)
print("Saving Model......")
best_acc = eval_accuracy
# Save a trained model
model_to_save = model.module if hasattr(
model,
'module') else model # Only save the model it-self
output_model_file = os.path.join(
output_dir, "pytorch_model.bin")
torch.save(model_to_save.state_dict(),
output_model_file)
print("=" * 80)
else:
print("=" * 80)
with open(output_eval_file, "a") as writer:
writer.write('bert_acc: %f' % best_acc)
if args.do_test:
if args.do_train == False:
output_dir = args.output_dir
# build model
logger.info("*** building model ***")
embeddings = load_embeddings(args)
model = ESIM(args.hidden_size,
embeddings=embeddings,
dropout=args.dropout,
num_classes=args.num_classes,
device=args.device)
model.load_state_dict(
torch.load(os.path.join(output_dir, 'pytorch_model.bin')))
model.to(args.device)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
inference_labels = []
gold_labels = []
logger.info("*** Loading testing data ***")
dev_data = ATEC_Dataset(os.path.join(args.data_dir, 'test.csv'),
os.path.join(args.data_dir, 'vocab.csv'),
args.query_maxlen)
dev_loader = DataLoader(dev_data,
shuffle=False,
batch_size=args.eval_batch_size)
logger.info(" *** Run Prediction ***")
logger.info(" Num examples = %d", len(dev_data))
logger.info(" Batch size = %d", args.eval_batch_size)
model.eval()
for q1, q1_lens, q2, q2_lens, labels in tqdm(dev_loader):
with torch.no_grad():
logits, probs = model(q1, q1_lens, q2, q2_lens)
probs = probs.detach().cpu().numpy()
inference_labels.append(np.argmax(probs, 1))
gold_labels.append(labels)
gold_labels = np.concatenate(gold_labels, 0)
logits = np.concatenate(inference_labels, 0)
test_f1 = get_f1(logits, gold_labels)
logger.info('predict f1:{}'.format(str(test_f1)))
(train_loss, train_acc * 100))
print('\t Val. Loss: %.3f | Val. Acc: %.2f %%' %
(valid_loss, valid_acc * 100))
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
best_valid_acc = valid_acc
torch.save(model.state_dict(), './saved_model/esim.pt')
print("New model saved!")
f_log.write("New model saved!\n")
f_log.flush()
f_log.close()
model.load_state_dict(torch.load('./saved_model/esim.pt'))
model.eval()
f_valid = open("data/test-set.data", "r", encoding='utf-8')
f_res = open('prediction.txt', 'w')
for i, rowlist in enumerate(f_valid):
rowlist = rowlist[:-1].split('\t')
input_sent = []
for sent in rowlist[:2]:
tokenized = tokenizer(sent)
indexed = [TEXT.vocab.stoi[t] for t in tokenized]
tensor = torch.LongTensor(indexed).to(device)
tensor = tensor.unsqueeze(1)
input_sent.append(tensor)
ans = F.softmax(model(input_sent[0], input_sent[1])[0])[1].item()
f_res.write(str(ans) + '\n')
f_valid.close()