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main.py
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main.py
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import random
import json
import logging
import sys
import os
import torch
import argparse
import pickle
import numpy as np
from utils.metrics import *
from utils.helper import *
from model.pfn import PFN
from dataloader.dataloader import dataloader
import torch.optim as optim
from tqdm import tqdm
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO)
logger = logging.getLogger(__name__)
def evaluate(test_batch, rel2idx, ner2idx, args, test_or_dev):
steps, test_loss = 0, 0
total_triple_num = [0, 0, 0]
total_entity_num = [0, 0, 0]
if args.eval_metric == "macro":
total_triple_num *= len(rel2idx)
total_entity_num *= len(ner2idx)
if args.eval_metric == "micro":
metric = micro(rel2idx, ner2idx)
else:
metric = macro(rel2idx, ner2idx)
with torch.no_grad():
for data in test_batch:
steps += 1
text = data[0]
ner_label = data[1].to(device)
re_label = data[2].to(device)
mask = data[-1].to(device)
ner_pred, re_pred = model(text, mask)
loss = BCEloss(ner_pred, ner_label, re_pred, re_label)
test_loss += loss
entity_num = metric.count_ner_num(ner_pred, ner_label)
triple_num = metric.count_num(ner_pred, ner_label, re_pred, re_label)
for i in range(len(entity_num)):
total_entity_num[i] += entity_num[i]
for i in range(len(triple_num)):
total_triple_num[i] += triple_num[i]
triple_result = f1(total_triple_num)
entity_result = f1(total_entity_num)
logger.info("------ {} Results ------".format(test_or_dev))
logger.info("loss : {:.4f}".format(test_loss / steps))
logger.info("entity: p={:.4f}, r={:.4f}, f={:.4f}".format(entity_result["p"], entity_result["r"], entity_result["f"]))
logger.info("triple: p={:.4f}, r={:.4f}, f={:.4f}".format(triple_result["p"], triple_result["r"], triple_result["f"]))
return triple_result, entity_result, test_loss / steps
def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--data", default=None, type=str, required=True,
help="which dataset to use")
parser.add_argument("--epoch", default=100, type=int,
help="number of training epoch")
parser.add_argument("--hidden_size", default=300, type=int,
help="number of hidden neurons in the model")
parser.add_argument("--batch_size", default=20, type=int,
help="number of samples in one training batch")
parser.add_argument("--eval_batch_size", default=10, type=int,
help="number of samples in one testing batch")
parser.add_argument("--do_train", action="store_true",
help="whether or not to train from scratch")
parser.add_argument("--do_eval", action="store_true",
help="whether or not to evaluate the model")
parser.add_argument("--embed_mode", default=None, type=str, required=True,
help="BERT or ALBERT pretrained embedding")
parser.add_argument("--eval_metric", default="micro", type=str,
help="micro f1 or macro f1")
parser.add_argument("--lr", default=None, type=float,
help="initial learning rate")
parser.add_argument("--weight_decay", default=0, type=float,
help="weight decaying rate")
parser.add_argument("--linear_warmup_rate", default=0.0, type=float,
help="warmup at the start of training")
parser.add_argument("--seed", default=0, type=int,
help="random seed initiation")
parser.add_argument("--dropout", default=0.1, type=float,
help="dropout rate for input word embedding")
parser.add_argument("--dropconnect", default=0.1, type=float,
help="dropconnect rate for partition filter layer")
parser.add_argument("--steps", default=50, type=int,
help="show result for every 50 steps")
parser.add_argument("--output_file", default="test", type=str, required=True,
help="name of result file")
parser.add_argument("--clip", default=0.25, type=float,
help="grad norm clipping to avoid gradient explosion")
parser.add_argument("--max_seq_len", default=128, type=int,
help="maximum length of sequence")
args = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
set_seed(args.seed)
output_dir = "save/" + args.output_file
os.mkdir(output_dir)
logger.addHandler(logging.FileHandler(output_dir + "/" + args.output_file + ".log", 'w'))
logger.info(sys.argv)
logger.info(args)
saved_file = save_results(output_dir + "/" + args.output_file + ".txt", header="# epoch \t train_loss \t dev_loss \t test_loss \t dev_ner \t dev_rel \t test_ner \t test_rel")
model_file = args.output_file + ".pt"
with open("data/" + args.data + "/ner2idx.json", "r") as f:
ner2idx = json.load(f)
with open("data/" + args.data + "/rel2idx.json", "r") as f:
rel2idx = json.load(f)
train_batch, test_batch, dev_batch = dataloader(args, ner2idx, rel2idx)
if args.do_train:
logger.info("------Training------")
if args.embed_mode == "albert":
input_size = 4096
else:
input_size = 768
model = PFN(args, input_size, ner2idx, rel2idx)
model.to(device)
optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
if args.eval_metric == "micro":
metric = micro(rel2idx, ner2idx)
else:
metric = macro(rel2idx, ner2idx)
BCEloss = loss()
best_result = 0
triple_best = None
entity_best = None
for epoch in range(args.epoch):
steps, train_loss = 0, 0
model.train()
for data in tqdm(train_batch):
steps+=1
optimizer.zero_grad()
text = data[0]
ner_label = data[1].to(device)
re_label = data[2].to(device)
mask = data[-1].to(device)
ner_pred, re_pred = model(text, mask)
loss = BCEloss(ner_pred, ner_label, re_pred, re_label)
loss.backward()
train_loss += loss.item()
torch.nn.utils.clip_grad_norm_(parameters=model.parameters(), max_norm=args.clip)
optimizer.step()
if steps % args.steps == 0:
logger.info("Epoch: {}, step: {} / {}, loss = {:.4f}".format
(epoch, steps, len(train_batch), train_loss / steps))
logger.info("------ Training Set Results ------")
logger.info("loss : {:.4f}".format(train_loss / steps))
if args.do_eval:
model.eval()
logger.info("------ Testing ------")
dev_triple, dev_entity, dev_loss = evaluate(dev_batch, rel2idx, ner2idx, args, "dev")
test_triple, test_entity, test_loss = evaluate(test_batch, rel2idx, ner2idx, args, "test")
average_f1 = dev_triple["f"] + dev_entity["f"]
if epoch == 0 or average_f1 > best_result:
best_result = average_f1
triple_best = test_triple
entity_best = test_entity
torch.save(model.state_dict(), output_dir + "/" + model_file)
logger.info("Best result on dev saved!!!")
saved_file.save("{} \t {:.4f} \t {:.4f} \t {:.4f} \t {:.4f} \t {:.4f} \t {:.4f} \t {:.4f}".format(epoch, train_loss/steps, dev_loss, test_loss, dev_entity["f"],
dev_triple["f"], test_entity["f"], test_triple["f"]))
saved_file.save("best test result ner-p: {:.4f} \t ner-r: {:.4f} \t ner-f: {:.4f} \t re-p: {:.4f} \t re-r: {:.4f} \t re-f: {:.4f} ".format(entity_best["p"],
entity_best["r"], entity_best["f"], triple_best["p"], triple_best["r"], triple_best["f"]))