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run_lstmcrf.py
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run_lstmcrf.py
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# -*- coding: utf-8 -*-
import logging
import sys
import os
import tqdm
import torch
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
from torch import nn, optim
from dataset import read_corpus, read_dictionary, vocab_build, crfDataset, prepare_databatches
from model import LSTMCRF, compute_forward
from lstmcrf_utils import evaluate, get_entity, save_parser
import argparse
def parser():
parser = argparse.ArgumentParser("This is a trying on argparse")
parser.add_argument('--model_name', type=str,
help="Model name, will create a fold to store model file")
parser.add_argument('--train_data_path', type=str, default="dataset/train_data",
help="train data path")
parser.add_argument('--test_data_path', type=str, default="dataset/test_data",
help="test data path")
parser.add_argument('--vocab_path', type=str,
default="vocab.pkl",help= "the vocab path under `model_name` folder")
parser.add_argument('--is_cuda', type=bool, default=True, help="Using cuda or not")
parser.add_argument('--cuda_device', type=int, default=0, help="When using gpu, use the ith one")
parser.add_argument('--seed', type=int, default=2021, help="Random seed")
parser.add_argument('--batch_size', type=int, default=64, help="batch size")
parser.add_argument("--embedding_size", type=int, default=128)
parser.add_argument("--hidden_szie", type=int, default=128)
parser.add_argument("--rnn_layer", type=int, default=1, help="number of stacked RNN layers")
parser.add_argument("--dropout", type=float, default=0.1)
parser.add_argument("--with_layer_norm", type=bool, default=False)
parser.add_argument("--lr", type=float, default=0.005, help="Learning rate")
parser.add_argument("--epochs", type=int, default=30, help="Learning rate")
parser.add_argument("--log_interval", type=int, default=30, help="Print loss every x steps")
parser.add_argument("--save_interval", type=int, default=30, help="save model every x steps")
parser.add_argument("--valid_interval", type=int, default=30,
help="Do validation on test set every x steps")
parser.add_argument("--patience", type=int, default=10,
help="Do early stopping when there's no approvment on test setafter x times validation")
parser.add_argument("--load_chkpoint", type=False, default=False,
help="Whether continuously trained on the previou model")
parser.add_argument('--chkpoint_model', type=str,
help= "chk point model path, needed when load_chkpoint is true")
parser.add_argument('--chkpoint_optim', type=str,
help= "chk point optimizer path, needed when load_chkpoint is true")
args = parser.parse_args()
return args
# class arguments:
# def __init__(self):
# self.model_name = "lstmcrf"
# self.train_data_path = "dataset/train_data"
# self.test_data_path = "dataset/test_data"
# self.vocab_path = "vocab.pkl"
# self.no_cuda = False
# self.seed = 2021
# self.batch_size = 64
# self.embedding_size = 128
# self.hidden_size = 128
# self.rnn_layer = 1
# self.dropout = 0.2
# self.with_layer_norm = False
# self.lr = 0.0005
# self.epochs = 50
# self.log_interval = 10
# self.save_interval = 30
# self.valid_interval = 60
# self.patience = 30
# self.load_chkpoint = True
# self.chkpoint_model = "lstmcrf/newest_model"
# self.chkpoint_optim = "lstmcrf/newest_optimizer"
def main(args):
START_TAG = "<START_TAG>"
END_TAG = "<END_TAG>"
O = "O"
BLOC = "B-LOC"
ILOC = "I-LOC"
BORG = "B-ORG"
IORG = "I-ORG"
BPER = "B-PER"
IPER = "I-PER"
PAD = "<PAD>"
UNK = "<UNK>"
token2idx = {
PAD: 0,
UNK: 1
}
tag2idx = {
START_TAG: 0,
END_TAG: 1,
O: 2,
BLOC: 3,
ILOC: 4,
BORG: 5,
IORG: 6,
BPER: 7,
IPER: 8
}
args = parser()
tb_writer = SummaryWriter(args.model_name)
# build vocab, word2id, id2tag, id2word
vocab_build(args.vocab_path, args.train_data_path, 10, token2idx)
word2id = read_dictionary(args.vocab_path)
id2word = {v:k for k,v in word2id.items()}
id2tag = {v:k for k,v in tag2idx.items()}
# set cuda device and seed
use_cuda = torch.cuda.is_available() and args.is_cuda
device = torch.device('cuda:{}'.format(args.cuda_device) if use_cuda else 'cpu')
torch.manual_seed(args.seed)
if use_cuda:
torch.cuda.manual_seed(args.seed)
# load datasets
print("Loading Datasets")
train_set = crfDataset(args.train_data_path)#os.path.join(args.data_path, "train_data"))
test_set = crfDataset(args.test_data_path)#os.path.join(args.data_path, "test_data"))
train_loader = DataLoader(train_set, args.batch_size, shuffle=False,
num_workers=1, pin_memory=True )
test_loader = DataLoader(test_set, args.batch_size, shuffle=False,
num_workers=1, pin_memory=True )
# Building Model
print("Building model")
model = LSTMCRF(vocab_size=len(word2id), tag_size=len(tag2idx), embedding_size=args.embedding_size,
hidden_size=args.hidden_size, dropout = args.dropout,
token2idx=word2id, PAD=PAD, tag2idx=tag2idx, START_TAG=START_TAG, END_TAG=END_TAG,
num_layers = args.rnn_layer, with_ln=False, bidirection=True)
if args.load_chkpoint:
print("==Loading Model from checkpoint: {}".format(args.chkpoint_model))
model.load_state_dict(torch.load(args.chkpoint_model))
print(model)
optimizer = optim.Adam(model.parameters(), lr=args.lr)
if args.load_chkpoint:
print("==Loading Model from checkpoint: {}".format(args.chkpoint_optim))
optimizer.load_state_dict(torch.load(args.chkpoint_optim))
print("Start training")
model.train()
step = 0
best_f1 = 0
patience = 0
early_stop = False
for eidx in range(1, args.epochs + 1):
if eidx == 2:
model.debug = True
if early_stop:
print("Early stop. epoch {} step {} best f1 {}".format(eidx, step, best_f1))
break
# sys.exit(0)
print("Start epoch {}".format(eidx).center(60,"="))
# with tqdm.tqdm(total = len(train_loader)) as pbar:
for bidx, batch in enumerate(train_loader):
seq, tags, mask = prepare_databatches(batch[0], batch[1], word2id, PAD, tag2idx,
END_TAG, UNK, device=device)
optimizer.zero_grad()
loss = compute_forward(model, seq, tags, mask)
tb_writer.add_scalar("train/loss", loss, step)
tb_writer.add_scalar("train/epoch", step, eidx)
optimizer.step()
# pbar.update(1)
step += 1
if step % args.log_interval == 0:
print("epoch {} step {} batch {} loss {}".format(eidx, step, bidx, loss))
if step % args.save_interval == 0:
torch.save(model.state_dict(), os.path.join(args.model_name, "newest_model"))
torch.save(optimizer.state_dict(), os.path.join(args.model_name, "newest_optimizer"))
if step % args.valid_interval == 0:
f1, precision, recall = evaluate(model, test_loader,word2id, PAD, id2tag, UNK,device)
tb_writer.add_scalar("eval/f1", f1, step)
tb_writer.add_scalar("eval/precision", precision, step)
tb_writer.add_scalar("eval/recall", recall, step)
print("[valid] epoch {} step {} f1 {} precision {} recall {}".format(eidx, step, f1, precision, recall))
if f1 > best_f1:
patience = 0
best_f1 = f1
torch.save(model.state_dict(), os.path.join(args.model_name, "best_model"))
torch.save(optimizer.state_dict(), os.path.join(args.model_name, "best_optimizer"))
else:
patience += 1
if patience == args.patience:
early_stop = True
if __name__ == "__main__":
__spec__ = "ModuleSpec(name='builtins', loader=<class '_frozen_importlib.BuiltinImporter'>)"
args = parser()
save_parser(args, os.path.join(args.model_name, "parser_config.json"))
main(args)