def main():
parser = argparse.ArgumentParser()
parser.add_argument('--train', default='train_wiki', help='train file')
parser.add_argument('--val', default='val_wiki', help='val file')
parser.add_argument('--test', default='test_wiki', help='test file')
parser.add_argument('--adv', default=None, help='adv file')
parser.add_argument('--trainN', default=10, type=int, help='N in train')
parser.add_argument('--N', default=5, type=int, help='N way')
parser.add_argument('--K', default=5, type=int, help='K shot')
parser.add_argument('--Q',
default=5,
type=int,
help='Num of query per class')
parser.add_argument('--batch_size', default=4, type=int, help='batch size')
parser.add_argument('--train_iter',
default=30000,
type=int,
help='num of iters in training')
parser.add_argument('--val_iter',
default=1000,
type=int,
help='num of iters in validation')
parser.add_argument('--test_iter',
default=10000,
type=int,
help='num of iters in testing')
parser.add_argument('--val_step',
default=2000,
type=int,
help='val after training how many iters')
parser.add_argument('--model', default='proto', help='model name')
parser.add_argument('--encoder',
default='cnn',
help='encoder: cnn or bert or roberta')
parser.add_argument('--max_length',
default=128,
type=int,
help='max length')
parser.add_argument('--lr', default=1e-1, type=float, help='learning rate')
parser.add_argument('--weight_decay',
default=1e-5,
type=float,
help='weight decay')
parser.add_argument('--dropout',
default=0.0,
type=float,
help='dropout rate')
parser.add_argument('--na_rate',
default=0,
type=int,
help='NA rate (NA = Q * na_rate)')
parser.add_argument('--grad_iter',
default=1,
type=int,
help='accumulate gradient every x iterations')
parser.add_argument('--optim', default='sgd', help='sgd / adam / adamw')
parser.add_argument('--hidden_size',
default=230,
type=int,
help='hidden size')
parser.add_argument('--load_ckpt', default=None, help='load ckpt')
parser.add_argument('--save_ckpt', default=None, help='save ckpt')
parser.add_argument('--fp16',
action='store_true',
help='use nvidia apex fp16')
parser.add_argument('--only_test', action='store_true', help='only test')
# only for bert / roberta
parser.add_argument('--pair', action='store_true', help='use pair model')
parser.add_argument('--pretrain_ckpt',
default=None,
help='bert / roberta pre-trained checkpoint')
parser.add_argument(
'--cat_entity_rep',
action='store_true',
help='concatenate entity representation as sentence rep')
# only for prototypical networks
parser.add_argument('--dot',
action='store_true',
help='use dot instead of L2 distance for proto')
opt = parser.parse_args()
trainN = opt.trainN
N = opt.N
K = opt.K
Q = opt.Q
batch_size = opt.batch_size
model_name = opt.model
encoder_name = opt.encoder
max_length = opt.max_length
print("{}-way-{}-shot Few-Shot Relation Classification".format(N, K))
print("model: {}".format(model_name))
print("encoder: {}".format(encoder_name))
print("max_length: {}".format(max_length))
if encoder_name == 'cnn':
try:
glove_mat = np.load('./pretrain/glove/glove_mat.npy')
glove_word2id = json.load(
open('./pretrain/glove/glove_word2id.json'))
except:
raise Exception(
"Cannot find glove files. Run glove/download_glove.sh to download glove files."
)
sentence_encoder = CNNSentenceEncoder(glove_mat, glove_word2id,
max_length)
elif encoder_name == 'bert':
pretrain_ckpt = opt.pretrain_ckpt or 'bert-base-uncased'
if opt.pair:
sentence_encoder = BERTPAIRSentenceEncoder(pretrain_ckpt,
max_length)
else:
sentence_encoder = BERTSentenceEncoder(
pretrain_ckpt, max_length, cat_entity_rep=opt.cat_entity_rep)
elif encoder_name == 'roberta':
pretrain_ckpt = opt.pretrain_ckpt or 'roberta-base'
if opt.pair:
sentence_encoder = RobertaPAIRSentenceEncoder(
pretrain_ckpt, max_length)
else:
sentence_encoder = RobertaSentenceEncoder(
pretrain_ckpt, max_length, cat_entity_rep=opt.cat_entity_rep)
else:
raise NotImplementedError
if opt.pair:
train_data_loader = get_loader_pair(opt.train,
sentence_encoder,
N=trainN,
K=K,
Q=Q,
na_rate=opt.na_rate,
batch_size=batch_size,
encoder_name=encoder_name)
val_data_loader = get_loader_pair(opt.val,
sentence_encoder,
N=N,
K=K,
Q=Q,
na_rate=opt.na_rate,
batch_size=batch_size,
encoder_name=encoder_name)
test_data_loader = get_loader_pair(opt.test,
sentence_encoder,
N=N,
K=K,
Q=Q,
na_rate=opt.na_rate,
batch_size=batch_size,
encoder_name=encoder_name)
else:
train_data_loader = get_loader(opt.train,
sentence_encoder,
N=trainN,
K=K,
Q=Q,
na_rate=opt.na_rate,
batch_size=batch_size)
val_data_loader = get_loader(opt.val,
sentence_encoder,
N=N,
K=K,
Q=Q,
na_rate=opt.na_rate,
batch_size=batch_size)
test_data_loader = get_loader(opt.test,
sentence_encoder,
N=N,
K=K,
Q=Q,
na_rate=opt.na_rate,
batch_size=batch_size)
if opt.adv:
adv_data_loader = get_loader_unsupervised(opt.adv,
sentence_encoder,
N=trainN,
K=K,
Q=Q,
na_rate=opt.na_rate,
batch_size=batch_size)
if opt.optim == 'sgd':
pytorch_optim = optim.SGD
elif opt.optim == 'adam':
pytorch_optim = optim.Adam
elif opt.optim == 'adamw':
from transformers import AdamW
pytorch_optim = AdamW
else:
raise NotImplementedError
if opt.adv:
d = Discriminator(opt.hidden_size)
framework = FewShotREFramework(train_data_loader,
val_data_loader,
test_data_loader,
adv_data_loader,
adv=opt.adv,
d=d)
else:
framework = FewShotREFramework(train_data_loader, val_data_loader,
test_data_loader)
prefix = '-'.join(
[model_name, encoder_name, opt.train, opt.val,
str(N), str(K)])
if opt.adv is not None:
prefix += '-adv_' + opt.adv
if opt.na_rate != 0:
prefix += '-na{}'.format(opt.na_rate)
if opt.dot:
prefix += '-dot'
if opt.cat_entity_rep:
prefix += '-catentity'
if model_name == 'proto':
model = Proto(sentence_encoder, dot=opt.dot)
elif model_name == 'gnn':
model = GNN(sentence_encoder, N, hidden_size=opt.hidden_size)
elif model_name == 'snail':
model = SNAIL(sentence_encoder, N, K, hidden_size=opt.hidden_size)
elif model_name == 'metanet':
model = MetaNet(N, K, sentence_encoder.embedding, max_length)
elif model_name == 'siamese':
model = Siamese(sentence_encoder,
hidden_size=opt.hidden_size,
dropout=opt.dropout)
elif model_name == 'pair':
model = Pair(sentence_encoder, hidden_size=opt.hidden_size)
else:
raise NotImplementedError
if not os.path.exists('checkpoint'):
os.mkdir('checkpoint')
ckpt = 'checkpoint/{}.pth.tar'.format(prefix)
if opt.save_ckpt:
ckpt = opt.save_ckpt
if torch.cuda.is_available():
model.cuda()
if not opt.only_test:
if encoder_name in ['bert', 'roberta']:
bert_optim = True
else:
bert_optim = False
framework.train(model,
prefix,
batch_size,
trainN,
N,
K,
Q,
pytorch_optim=pytorch_optim,
load_ckpt=opt.load_ckpt,
save_ckpt=ckpt,
na_rate=opt.na_rate,
val_step=opt.val_step,
fp16=opt.fp16,
pair=opt.pair,
train_iter=opt.train_iter,
val_iter=opt.val_iter,
bert_optim=bert_optim)
else:
ckpt = opt.load_ckpt
acc = framework.eval(model,
batch_size,
N,
K,
Q,
opt.test_iter,
na_rate=opt.na_rate,
ckpt=ckpt,
pair=opt.pair)
print("RESULT: %.2f" % (acc * 100))
def main():
parser = argparse.ArgumentParser()
# 模型相关
parser.add_argument('--do_train', default=True, type=bool, help='do train')
parser.add_argument('--do_eval', default=True, type=bool, help='do eval')
parser.add_argument('--do_predict',
default=False,
type=bool,
help='do predict')
parser.add_argument('--do_cn_eval',
default=False,
type=bool,
help='do CN eval')
parser.add_argument(
'--proto_emb',
default=False,
help='Get root cause proto emb or sentence emb. Require do_predict=True'
)
parser.add_argument('--train_file',
default='./data/source_add_CN_V2.xlsx',
help='source file')
parser.add_argument('--trainN', default=5, type=int, help='N in train')
parser.add_argument('--N', default=5, type=int, help='N way')
parser.add_argument('--K', default=3, type=int, help='K shot')
parser.add_argument('--Q',
default=2,
type=int,
help='Num of query per class')
parser.add_argument('--batch_size', default=8, type=int, help='batch size')
parser.add_argument('--train_iter',
default=10000,
type=int,
help='num of iters in training')
parser.add_argument('--warmup_rate', default=0.1, type=float)
parser.add_argument('--max_length',
default=128,
type=int,
help='max length')
parser.add_argument('--lr', default=1e-5, type=float, help='learning rate')
parser.add_argument('--dropout',
default=0.0,
type=float,
help='dropout rate')
parser.add_argument('--seed', default=100, type=int) # 100
# 保存与加载
parser.add_argument('--load_ckpt',
default='./check_points/model_54000.bin',
help='load ckpt')
parser.add_argument('--save_ckpt',
default='./check_points/',
help='save ckpt')
parser.add_argument('--save_emb',
default='./data/emb.json',
help='save embedding')
parser.add_argument('--save_root_emb',
default='./data/root_emb.json',
help='save embedding')
parser.add_argument('--use_cuda', default=True, help='whether to use cuda')
parser.add_argument('--eval_step', default=100)
parser.add_argument('--save_step', default=500)
parser.add_argument('--threshold', default=5)
# bert pretrain
parser.add_argument("--vocab_file",
default="./pretrain/vocab.txt",
type=str,
help="Init vocab to resume training from.")
parser.add_argument("--config_path",
default="./pretrain/bert_config.json",
type=str,
help="Init config to resume training from.")
parser.add_argument("--init_checkpoint",
default="./pretrain/pytorch_model.bin",
type=str,
help="Init checkpoint to resume training from.")
opt = parser.parse_args()
trainN = opt.trainN
K = opt.K
Q = opt.Q
batch_size = opt.batch_size
max_length = opt.max_length
logger.info("{}-way-{}-shot Few-Shot Dignose".format(trainN, K))
logger.info("max_length: {}".format(max_length))
random.seed(opt.seed)
np.random.seed(opt.seed)
torch.manual_seed(opt.seed)
if not os.path.exists(opt.save_ckpt):
os.mkdir(opt.save_ckpt)
bert_tokenizer = BertTokenizer.from_pretrained(opt.vocab_file)
bert_config = BertConfig.from_pretrained(opt.config_path)
bert_model = BertModel.from_pretrained(opt.init_checkpoint,
config=bert_config)
model = Proto(bert_model, opt)
if opt.use_cuda:
model.cuda()
if opt.do_train:
train_data, eval_data = read_data(opt.train_file, opt.threshold)
train_data_loader = get_loader(train_data,
bert_tokenizer,
max_length,
N=trainN,
K=K,
Q=Q,
batch_size=batch_size)
framework = FewShotREFramework(tokenizer=bert_tokenizer,
train_data_loader=train_data_loader,
train_data=train_data,
eval_data=eval_data)
framework.train(model, batch_size, trainN, K, Q, opt)
if opt.do_eval:
train_data, eval_data = read_data(opt.train_file, opt.threshold)
state_dict = torch.load(opt.load_ckpt)
own_state = bert_model.state_dict()
for name, param in state_dict.items():
name = name.replace('sentence_encoder.module.', '')
if name not in own_state:
continue
own_state[name].copy_(param)
step = opt.load_ckpt.split('/')[-1].replace('model_', '').split('.')[0]
bert_model.eval()
train_data_emb, train_rc_emb = util.get_emb(bert_model, bert_tokenizer,
train_data, opt)
eval_data_emb, _ = util.get_emb(bert_model, bert_tokenizer, eval_data,
opt)
acc1 = util.single_acc(train_data_emb, eval_data_emb)
acc2 = util.proto_acc(train_rc_emb, eval_data_emb)
acc3 = util.policy_acc(train_data_emb, eval_data_emb)
logger.info(
"single eval accuracy: [top1: %.4f] [top3: %.4f] [top5: %.4f]" %
(acc1[0], acc1[1], acc1[2]))
logger.info(
"proto eval accuracy: [top1: %.4f] [top3: %.4f] [top5: %.4f]" %
(acc2[0], acc2[1], acc2[2]))
logger.info(
"policy eval accuracy: [top1: %.4f] [top3: %.4f] [top5: %.4f]" %
(acc3[0], acc3[1], acc3[2]))
with open('./data/train_emb_%s.json' % step, 'w',
encoding='utf8') as f:
json.dump(train_data_emb, f, ensure_ascii=False)
with open('./data/train_rc_emb_%s.json' % step, 'w',
encoding='utf8') as f:
json.dump(train_rc_emb, f, ensure_ascii=False)
with open('./data/eval_emb_%s.json' % step, 'w', encoding='utf8') as f:
json.dump(eval_data_emb, f, ensure_ascii=False)
if opt.do_predict:
test_data = read_data(opt.train_file, opt.threshold, False)
# predict proto emb or sentence emb
state_dict = torch.load(opt.load_ckpt)
own_state = bert_model.state_dict()
for name, param in state_dict.items():
name = name.replace('sentence_encoder.module.', '')
if name not in own_state:
continue
own_state[name].copy_(param)
bert_model.eval()
id_to_emd, root_cause_emb = util.get_emb(bert_model, bert_tokenizer,
test_data, opt)
if opt.save_emb and opt.save_root_emb:
with open(opt.save_emb, 'w', encoding='utf8') as f:
json.dump(id_to_emd, f, ensure_ascii=False)
with open(opt.save_root_emb, 'w', encoding='utf8') as f:
json.dump(root_cause_emb, f, ensure_ascii=False)
if len(sys.argv) > 3:
K = int(sys.argv[3])
print("{}-way-{}-shot Few-Shot Relation Classification".format(N, K))
print("Model: {}".format(model_name))
max_length = 40
train_data_loader = JSONFileDataLoader('./data/train.json', './data/glove.6B.50d.json', max_length=max_length)
val_data_loader = JSONFileDataLoader('./data/val.json', './data/glove.6B.50d.json', max_length=max_length)
test_data_loader = JSONFileDataLoader('./data/test.json', './data/glove.6B.50d.json', max_length=max_length)
framework = FewShotREFramework(train_data_loader, val_data_loader, test_data_loader)
sentence_encoder = CNNSentenceEncoder(train_data_loader.word_vec_mat, max_length)
if model_name == 'proto':
model = Proto(sentence_encoder)
framework.train(model, model_name, 4, 20, N, K, 5)
elif model_name == 'gnn':
model = GNN(sentence_encoder, N)
framework.train(model, model_name, 2, N, N, K, 1, learning_rate=1e-3, weight_decay=0, optimizer=optim.Adam)
elif model_name == 'snail':
print("HINT: SNAIL works only in PyTorch 0.3.1")
model = SNAIL(sentence_encoder, N, K)
framework.train(model, model_name, 25, N, N, K, 1, learning_rate=1e-2, weight_decay=0, optimizer=optim.SGD)
elif model_name == 'metanet':
model = MetaNet(N, K, train_data_loader.word_vec_mat, max_length)
framework.train(model, model_name, 1, N, N, K, 1, learning_rate=5e-3, weight_decay=0, optimizer=optim.Adam, train_iter=300000)
else:
raise NotImplementedError
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--train', default='train_wiki', help='train file')
parser.add_argument('--val', default='val_wiki', help='val file')
parser.add_argument('--test', default='test_wiki', help='test file')
parser.add_argument('--adv', default=None, help='adv file')
parser.add_argument('--trainN', default=10, type=int, help='N in train')
parser.add_argument('--N', default=5, type=int, help='N way')
parser.add_argument('--K', default=5, type=int, help='K shot')
parser.add_argument('--Q',
default=5,
type=int,
help='Num of query per class')
parser.add_argument('--batch_size', default=4, type=int, help='batch size')
parser.add_argument('--train_iter',
default=20000,
type=int,
help='num of iters in training')
parser.add_argument('--val_iter',
default=1000,
type=int,
help='num of iters in validation')
parser.add_argument('--test_iter',
default=2000,
type=int,
help='num of iters in testing')
parser.add_argument('--val_step',
default=2000,
type=int,
help='val after training how many iters')
parser.add_argument('--model', default='proto', help='model name')
parser.add_argument('--encoder',
default='cnn',
help='encoder: cnn or bert')
parser.add_argument('--max_length',
default=128,
type=int,
help='max length')
parser.add_argument('--lr', default=1e-1, type=float, help='learning rate')
parser.add_argument('--weight_decay',
default=1e-5,
type=float,
help='weight decay')
parser.add_argument('--dropout',
default=0.0,
type=float,
help='dropout rate')
parser.add_argument('--na_rate',
default=0,
type=int,
help='NA rate (NA = Q * na_rate)')
parser.add_argument('--grad_iter',
default=1,
type=int,
help='accumulate gradient every x iterations')
parser.add_argument('--optim',
default='sgd',
help='sgd / adam / bert_adam')
parser.add_argument('--hidden_size',
default=230,
type=int,
help='hidden size')
parser.add_argument('--load_ckpt', default=None, help='load ckpt')
parser.add_argument('--save_ckpt', default=None, help='save ckpt')
parser.add_argument('--fp16',
action='store_true',
help='use nvidia apex fp16')
parser.add_argument('--only_test', action='store_true', help='only test')
parser.add_argument('--pair', action='store_true', help='use pair model')
parser.add_argument('--language', type=str, default='eng', help='language')
parser.add_argument('--sup_cost',
type=int,
default=0,
help='use sup classifier')
opt = parser.parse_args()
trainN = opt.trainN
N = opt.N
K = opt.K
Q = opt.Q
batch_size = opt.batch_size
model_name = opt.model
encoder_name = opt.encoder
max_length = opt.max_length
sup_cost = bool(opt.sup_cost)
print(sup_cost)
print("{}-way-{}-shot Few-Shot Relation Classification".format(N, K))
print("model: {}".format(model_name))
print("encoder: {}".format(encoder_name))
print("max_length: {}".format(max_length))
embsize = 50
if opt.language == 'chn':
embsize = 100
if encoder_name == 'cnn':
try:
if opt.language == 'chn':
glove_mat = np.load('./pretrain/chinese_emb/emb.npy')
glove_word2id = json.load(
open('./pretrain/chinese_emb/word2id.json'))
else:
glove_mat = np.load('./pretrain/glove/glove_mat.npy')
glove_word2id = json.load(
open('./pretrain/glove/glove_word2id.json'))
except:
raise Exception(
"Cannot find glove files. Run glove/download_glove.sh to download glove files."
)
sentence_encoder = CNNSentenceEncoder(glove_mat,
glove_word2id,
max_length,
word_embedding_dim=embsize)
elif encoder_name == 'bert':
if opt.pair:
if opt.language == 'chn':
sentence_encoder = BERTPAIRSentenceEncoder(
'bert-base-chinese', #'./pretrain/bert-base-uncased',
max_length)
else:
sentence_encoder = BERTPAIRSentenceEncoder(
'bert-base-uncased', max_length)
else:
if opt.language == 'chn':
sentence_encoder = BERTSentenceEncoder(
'bert-base-chinese', #'./pretrain/bert-base-uncased',
max_length)
else:
sentence_encoder = BERTSentenceEncoder('bert-base-uncased',
max_length)
else:
raise NotImplementedError
if opt.pair:
train_data_loader = get_loader_pair(opt.train,
sentence_encoder,
N=trainN,
K=K,
Q=Q,
na_rate=opt.na_rate,
batch_size=batch_size)
val_data_loader = get_loader_pair(opt.val,
sentence_encoder,
N=N,
K=K,
Q=Q,
na_rate=opt.na_rate,
batch_size=batch_size)
test_data_loader = get_loader_pair(opt.test,
sentence_encoder,
N=N,
K=K,
Q=Q,
na_rate=opt.na_rate,
batch_size=batch_size)
else:
train_data_loader = get_loader(opt.train,
sentence_encoder,
N=trainN,
K=K,
Q=Q,
na_rate=opt.na_rate,
batch_size=batch_size)
val_data_loader = get_loader(opt.val,
sentence_encoder,
N=N,
K=K,
Q=Q,
na_rate=opt.na_rate,
batch_size=batch_size)
test_data_loader = get_loader(opt.test,
sentence_encoder,
N=N,
K=K,
Q=Q,
na_rate=opt.na_rate,
batch_size=batch_size)
if opt.adv:
adv_data_loader = get_loader_unsupervised(opt.adv,
sentence_encoder,
N=trainN,
K=K,
Q=Q,
na_rate=opt.na_rate,
batch_size=batch_size)
if opt.optim == 'sgd':
pytorch_optim = optim.SGD
elif opt.optim == 'adam':
pytorch_optim = optim.Adam
elif opt.optim == 'bert_adam':
from transformers import AdamW
pytorch_optim = AdamW
else:
raise NotImplementedError
if opt.adv:
d = Discriminator(opt.hidden_size)
framework = FewShotREFramework(train_data_loader,
val_data_loader,
test_data_loader,
adv_data_loader,
adv=opt.adv,
d=d)
else:
framework = FewShotREFramework(train_data_loader, val_data_loader,
test_data_loader)
prefix = '-'.join(
[model_name, encoder_name, opt.train, opt.val,
str(N), str(K)])
if opt.adv is not None:
prefix += '-adv_' + opt.adv
if opt.na_rate != 0:
prefix += '-na{}'.format(opt.na_rate)
if model_name == 'proto':
model = Proto(sentence_encoder, hidden_size=opt.hidden_size)
elif model_name == 'gnn':
model = GNN(sentence_encoder, N, use_sup_cost=sup_cost)
elif model_name == 'snail':
print("HINT: SNAIL works only in PyTorch 0.3.1")
model = SNAIL(sentence_encoder, N, K)
elif model_name == 'metanet':
model = MetaNet(N,
K,
sentence_encoder.embedding,
max_length,
use_sup_cost=sup_cost)
elif model_name == 'siamese':
model = Siamese(sentence_encoder,
hidden_size=opt.hidden_size,
dropout=opt.dropout)
elif model_name == 'pair':
model = Pair(sentence_encoder, hidden_size=opt.hidden_size)
else:
raise NotImplementedError
if not os.path.exists('checkpoint'):
os.mkdir('checkpoint')
ckpt = 'checkpoint/{}.pth.tar'.format(prefix)
if opt.save_ckpt:
ckpt = opt.save_ckpt
if torch.cuda.is_available():
model.cuda()
if not opt.only_test:
if encoder_name == 'bert':
bert_optim = True
else:
bert_optim = False
framework.train(model,
prefix,
batch_size,
trainN,
N,
K,
Q,
pytorch_optim=pytorch_optim,
load_ckpt=opt.load_ckpt,
save_ckpt=ckpt,
na_rate=opt.na_rate,
val_step=opt.val_step,
fp16=opt.fp16,
pair=opt.pair,
train_iter=opt.train_iter,
val_iter=opt.val_iter,
bert_optim=bert_optim,
sup_cls=sup_cost)
else:
ckpt = opt.load_ckpt
acc = framework.eval(model,
batch_size,
N,
K,
Q,
opt.test_iter,
na_rate=opt.na_rate,
ckpt=ckpt,
pair=opt.pair)
wfile = open('logs/' + ckpt.replace('checkpoint/', '') + '.txt', 'a')
wfile.write(str(N) + '\t' + str(K) + '\t' + str(acc * 100) + '\n')
wfile.close()
print("RESULT: %.2f" % (acc * 100))
'./data/glove.6B.50d.json',
max_length=max_length)
val_data_loader = JSONFileDataLoader('./data/val.json',
'./data/glove.6B.50d.json',
max_length=max_length)
test_data_loader = JSONFileDataLoader('./data/test.json',
'./data/glove.6B.50d.json',
max_length=max_length)
framework = FewShotREFramework(train_data_loader, val_data_loader,
test_data_loader)
sentence_encoder = CNNSentenceEncoder(train_data_loader.word_vec_mat,
max_length)
if model_name == 'proto':
model = Proto(sentence_encoder).cuda()
ckpt = 'checkpoint/proto.pth.tar'
elif model_name == 'proto_hatt':
model = ProtoHATT(sentence_encoder, K).cuda()
ckpt = 'checkpoint/proto_hatt.pth.tar'
else:
raise NotImplementedError
acc = 0
for i in range(5):
acc += framework.eval(model,
4,
N,
K,
100,
3000,
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--train', default='train', help='train file')
parser.add_argument('--val', default='dev', help='val file')
parser.add_argument('--test', default='test', help='test file')
parser.add_argument('--N',
default=2,
type=int,
help='Number of example concencate')
parser.add_argument('--batch_size',
default=32,
type=int,
help='batch size')
parser.add_argument('--train_iter',
default=30000,
type=int,
help='num of iters in training')
parser.add_argument('--val_iter',
default=1000,
type=int,
help='num of iters in validation')
parser.add_argument('--test_iter',
default=10000,
type=int,
help='num of iters in testing')
parser.add_argument('--val_step',
default=2000,
type=int,
help='val after training how many iters')
parser.add_argument('--model', default='proto', help='model name')
parser.add_argument('--encoder', default='cnn', help='cnn, lstm')
parser.add_argument('--max_length',
default=32,
type=int,
help='max length')
parser.add_argument('--lr', default=1e-5, type=float, help='learning rate')
parser.add_argument('--dropout',
default=0.0,
type=float,
help='dropout rate')
parser.add_argument('--grad_iter',
default=1,
type=int,
help='accumulate gradient every x iterations')
parser.add_argument('--optim', default='sgd', help='sgd / adam')
parser.add_argument('--hidden_size',
default=64,
type=int,
help='hidden size')
parser.add_argument('--load_ckpt', default=None, help='load ckpt')
parser.add_argument('--save_ckpt', default=None, help='save ckpt')
parser.add_argument('--only_test', action='store_true', help='only test')
opt = parser.parse_args()
N = opt.N
batch_size = opt.batch_size
model_name = opt.model
encoder_name = opt.encoder
max_length = opt.max_length
print("model: {}".format(model_name))
print("encoder: {}".format(encoder_name))
print("max_length: {}".format(max_length))
print('Preparing vocab.')
vocab = Vocab(opt.train, './data')
char2id = vocab.prepare_vocab()
print('Prepare done!')
sentence_encoder = SentenceEncoder(char2id,
max_length,
opt.hidden_size,
opt.hidden_size,
encoder=opt.encoder)
train_data_loader = get_loader(opt.train,
sentence_encoder,
N=N,
batch_size=batch_size)
val_data_loader = get_loader(opt.val,
sentence_encoder,
N=N,
batch_size=batch_size)
test_data_loader = get_loader(opt.test,
sentence_encoder,
N=N,
batch_size=batch_size)
if opt.optim == 'sgd':
pytorch_optim = optim.SGD
elif opt.optim == 'adam':
pytorch_optim = optim.Adam
else:
raise NotImplementedError
framework = Framework(train_data_loader, val_data_loader, test_data_loader)
prefix = '-'.join([model_name, encoder_name, opt.train, opt.val, str(N)])
model = Proto(sentence_encoder, hidden_size=opt.hidden_size)
if not os.path.exists('checkpoint'):
os.mkdir('checkpoint')
ckpt = 'checkpoint/{}.pth.tar'.format(prefix)
if opt.save_ckpt:
ckpt = opt.save_ckpt
if torch.cuda.is_available():
model.cuda()
framework.train(model,
prefix,
batch_size,
pytorch_optim=pytorch_optim,
load_ckpt=opt.load_ckpt,
save_ckpt=ckpt,
val_step=opt.val_step,
train_iter=opt.train_iter,
val_iter=opt.val_iter)
acc = framework.eval(model, batch_size, opt.test_iter, ckpt=ckpt)
print("RESULT: %.2f" % (acc * 100))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--train', default='train_data',
help='train file')
parser.add_argument('--val', default='val_data',
help='val file')
parser.add_argument('--test', default='test_data',
help='test file')
parser.add_argument('--adv', default=None,
help='adv file')
parser.add_argument('--trainN', default=10, type=int,
help='N in train')
parser.add_argument('--N', default=5, type=int,
help='N way')
parser.add_argument('--K', default=5, type=int,
help='K shot')
parser.add_argument('--Q', default=5, type=int,
help='Num of query per class')
parser.add_argument('--batch_size', default=4, type=int,
help='batch size')
parser.add_argument('--train_iter', default=30000, type=int,
help='num of iters in training')
parser.add_argument('--val_iter', default=1000, type=int,
help='num of iters in validation')
parser.add_argument('--test_iter', default=1000, type=int,
help='num of iters in testing')
parser.add_argument('--val_step', default=2000, type=int,
help='val after training how many iters')
parser.add_argument('--repeat_test', default=8, type=int,
help='repeat test stage')
parser.add_argument('--model', default='proto',
help='model name')
parser.add_argument('--encoder', default='bert',
help='encoder: cnn or bert or roberta')
parser.add_argument('--cross_modality', default='concate',
help='cross-modality module')
parser.add_argument('--max_length', default=128, type=int,
help='max length')
parser.add_argument('--lr', default=1e-1, type=float,
help='learning rate')
parser.add_argument('--weight_decay', default=1e-5, type=float,
help='weight decay')
parser.add_argument('--dropout', default=0.0, type=float,
help='dropout rate')
parser.add_argument('--na_rate', default=0, type=int,
help='NA rate (NA = Q * na_rate)')
parser.add_argument('--grad_iter', default=1, type=int,
help='accumulate gradient every x iterations')
parser.add_argument('--optim', default='sgd',
help='sgd / adam / adamw')
parser.add_argument('--hidden_size', default=230, type=int,
help='hidden size')
parser.add_argument('--multi_choose', default=1, type=int,
help='choose multi random faces')
parser.add_argument('--load_ckpt', default=None,
help='load ckpt')
parser.add_argument('--root_data', default='./data',
help='the root path stores data')
parser.add_argument('--save_ckpt', default=None,
help='save ckpt')
parser.add_argument('--fp16', action='store_true',
help='use nvidia apex fp16')
parser.add_argument('--only_test', action='store_true',
help='only test')
# only for bert / roberta
parser.add_argument('--pair', action='store_true',
help='use pair model')
parser.add_argument('--pretrain_ckpt', default=None,
help='bert / roberta pre-trained checkpoint')
parser.add_argument('--cat_entity_rep', action='store_true',
help='concatenate entity representation as sentence rep')
parser.add_argument('--use_img', action='store_true',
help='use img info')
# only for prototypical networks
parser.add_argument('--dot', action='store_true',
help='use dot instead of L2 distance for proto')
parser.add_argument('--differ_scene', action='store_true',
help='use face image in different scenes')
opt = parser.parse_args()
trainN = opt.trainN
N = opt.N
K = opt.K
Q = opt.Q
batch_size = opt.batch_size
model_name = opt.model
encoder_name = opt.encoder
max_length = opt.max_length
print("{}-way-{}-shot Multimodal Social Relation Classification".format(N, K))
print("model: {}".format(model_name))
print("encoder: {}".format(encoder_name))
print("max_length: {}".format(max_length))
if encoder_name == 'cnn':
try:
glove_mat = np.load('./pretrain/glove/glove_mat.npy')
glove_word2id = json.load(open('./pretrain/glove/glove_word2id.json'))
except:
raise Exception("Cannot find glove files. Run glove/download_glove.sh to download glove files.")
sentence_encoder = CNNSentenceEncoder(
glove_mat,
glove_word2id,
max_length)
elif encoder_name == 'bert':
pretrain_ckpt = opt.pretrain_ckpt or 'bert-base-chinese'
sentence_encoder = BERTSentenceEncoder(pretrain_ckpt, max_length)
elif encoder_name == 'roberta':
pretrain_ckpt = opt.pretrain_ckpt or 'roberta-base'
sentence_encoder = RobertaSentenceEncoder(
pretrain_ckpt,
max_length,
cat_entity_rep=opt.cat_entity_rep)
else:
raise NotImplementedError
train_data_loader = get_loader(opt.train, sentence_encoder,
N=trainN, K=K, Q=Q, root=opt.root_data, batch_size=batch_size, use_img=opt.use_img, differ_scene=opt.differ_scene, multi_choose=opt.multi_choose)
val_data_loader = get_loader(opt.val, sentence_encoder,
N=N, K=K, Q=Q, root=opt.root_data, batch_size=batch_size, use_img=opt.use_img, differ_scene=opt.differ_scene, multi_choose=opt.multi_choose)
test_data_loader = get_loader(opt.test, sentence_encoder,
N=N, K=K, Q=Q, root=opt.root_data, batch_size=batch_size, use_img=opt.use_img, differ_scene=opt.differ_scene, multi_choose=opt.multi_choose)
if opt.optim == 'sgd':
pytorch_optim = optim.SGD
elif opt.optim == 'adam':
pytorch_optim = optim.Adam
elif opt.optim == 'adamw':
from transformers import AdamW
pytorch_optim = AdamW
else:
raise NotImplementedError
framework = FewShotREFramework(train_data_loader, val_data_loader, test_data_loader)
if opt.differ_scene:
prefix = '-'.join(['differ', model_name, encoder_name, opt.train, opt.val, str(N), str(K)])
else:
prefix = '-'.join(['same', model_name, encoder_name, opt.train, opt.val, str(N), str(K)])
if opt.use_img:
prefix = 'img_' + prefix
if opt.cross_modality == 'transformer':
prefix = 'transformer' + '-' + prefix
if opt.multi_choose > 1:
prefix = 'choose-' + str(opt.multi_choose) + '-' + prefix
prefix = '0227' + prefix
if model_name == 'proto':
if opt.use_img:
face_encoder = FacenetEncoder()
multimodal_encoder = MultimodalEncoder(sentence_encoder=sentence_encoder, face_encoder=face_encoder, cross_modality=opt.cross_modality)
model = Proto(multimodal_encoder, dot=opt.dot)
else:
model = Proto(sentence_encoder, dot=opt.dot)
else:
raise NotImplementedError
# if not os.path.exists('checkpoint'):
# os.mkdir('checkpoint')
# ckpt = 'checkpoint/{}.pth.tar'.format(prefix)
checkpoint_dir = os.path.join(opt.root_data, 'checkpoint')
if not os.path.exists(checkpoint_dir):
os.mkdir(checkpoint_dir)
ckpt = os.path.join(checkpoint_dir, '{}.pth.tar'.format(prefix))
if opt.save_ckpt:
ckpt = opt.save_ckpt
if torch.cuda.is_available():
model.cuda()
if not opt.only_test:
if encoder_name in ['bert', 'roberta']:
bert_optim = True
else:
bert_optim = False
framework.train(model, prefix, batch_size, trainN, N, K, Q,
pytorch_optim=pytorch_optim, load_ckpt=opt.load_ckpt, save_ckpt=ckpt,
na_rate=opt.na_rate, val_step=opt.val_step, grad_iter=opt.grad_iter, fp16=opt.fp16,
pair=opt.pair, train_iter=opt.train_iter, val_iter=opt.val_iter, bert_optim=bert_optim,
multi_choose=opt.multi_choose)
else:
ckpt = opt.load_ckpt
result_txt = os.path.join(checkpoint_dir, '{}.txt'.format(opt.root_data.split('/')[1] + '_' + prefix))
with open(result_txt, 'a', encoding='utf-8') as f:
for _ in range (opt.repeat_test):
acc = framework.eval(model, batch_size, N, K, Q, opt.test_iter, na_rate=opt.na_rate, ckpt=ckpt, pair=opt.pair, multi_choose=opt.multi_choose)
print("RESULT: %.2f" % (acc * 100))
f.write(str(acc * 100))
f.write('\n')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--train', default='train_wiki', help='train file')
parser.add_argument('--val', default='val_wiki', help='val file')
parser.add_argument('--test', default='test_wiki', help='test file')
parser.add_argument('--adv', default=None, help='adv file')
parser.add_argument('--trainN', default=10, type=int, help='N in train')
parser.add_argument('--N', default=5, type=int, help='N way')
parser.add_argument('--K', default=5, type=int, help='K shot')
parser.add_argument('--Q',
default=5,
type=int,
help='Num of query per class')
parser.add_argument('--batch_size', default=4, type=int, help='batch size')
parser.add_argument('--train_iter',
default=30000,
type=int,
help='num of iters in training')
parser.add_argument('--val_iter',
default=1000,
type=int,
help='num of iters in validation')
parser.add_argument('--test_iter',
default=3000,
type=int,
help='num of iters in testing')
parser.add_argument('--val_step',
default=2000,
type=int,
help='val after training how many iters')
parser.add_argument('--model', default='proto', help='model name')
parser.add_argument('--encoder',
default='cnn',
help='encoder: cnn or bert')
parser.add_argument('--max_length',
default=128,
type=int,
help='max length')
parser.add_argument('--lr', default=1e-1, type=float, help='learning rate')
parser.add_argument('--weight_decay',
default=1e-5,
type=float,
help='weight decay')
parser.add_argument('--dropout',
default=0.0,
type=float,
help='dropout rate')
parser.add_argument('--na_rate',
default=0,
type=int,
help='NA rate (NA = Q * na_rate)')
parser.add_argument('--grad_iter',
default=1,
type=int,
help='accumulate gradient every x iterations')
parser.add_argument('--optim',
default='sgd',
help='sgd / adam / bert_adam')
parser.add_argument('--hidden_size',
default=230,
type=int,
help='hidden size')
parser.add_argument('--load_ckpt', default=None, help='load ckpt')
parser.add_argument('--save_ckpt', default=None, help='save ckpt')
parser.add_argument('--fp16',
action='store_true',
help='use nvidia apex fp16')
parser.add_argument('--only_test', action='store_true', help='only test')
parser.add_argument('--pair', action='store_true', help='use pair model')
opt = parser.parse_args()
trainN = opt.trainN
N = opt.N
K = opt.K
Q = opt.Q
batch_size = opt.batch_size
model_name = opt.model
encoder_name = opt.encoder
max_length = opt.max_length
print("{}-way-{}-shot Few-Shot Relation Classification".format(N, K))
print("model: {}".format(model_name))
print("encoder: {}".format(encoder_name))
print("max_length: {}".format(max_length))
if encoder_name == 'cnn':
try:
glove_mat = np.load('./pretrain/glove/glove_mat.npy')
glove_word2id = json.load(
open('./pretrain/glove/glove_word2id.json'))
except:
raise Exception(
"Cannot find glove files. Run glove/download_glove.sh to download glove files."
)
sentence_encoder = CNNSentenceEncoder(glove_mat, glove_word2id,
max_length)
elif encoder_name == 'bert':
if opt.pair:
sentence_encoder = BERTPAIRSentenceEncoder(
'./pretrain/bert-base-uncased', max_length)
else:
sentence_encoder = BERTSentenceEncoder(
'./pretrain/bert-base-uncased', max_length)
else:
raise NotImplementedError
if opt.pair:
train_data_loader = get_loader_pair(opt.train,
sentence_encoder,
N=trainN,
K=K,
Q=Q,
na_rate=opt.na_rate,
batch_size=batch_size)
val_data_loader = get_loader_pair(opt.val,
sentence_encoder,
N=N,
K=K,
Q=Q,
na_rate=opt.na_rate,
batch_size=batch_size)
test_data_loader = get_loader_pair(opt.test,
sentence_encoder,
N=N,
K=K,
Q=Q,
na_rate=opt.na_rate,
batch_size=batch_size)
else:
train_data_loader = get_loader(opt.train,
sentence_encoder,
N=trainN,
K=K,
Q=Q,
na_rate=opt.na_rate,
batch_size=batch_size)
val_data_loader = get_loader(opt.val,
sentence_encoder,
N=N,
K=K,
Q=Q,
na_rate=opt.na_rate,
batch_size=batch_size)
test_data_loader = get_loader(opt.test,
sentence_encoder,
N=N,
K=K,
Q=Q,
na_rate=opt.na_rate,
batch_size=batch_size)
if opt.adv:
adv_data_loader = get_loader_unsupervised(opt.adv,
sentence_encoder,
N=trainN,
K=K,
Q=Q,
na_rate=opt.na_rate,
batch_size=batch_size)
if opt.optim == 'sgd':
pytorch_optim = optim.SGD
elif opt.optim == 'adam':
pytorch_optim = optim.Adam
elif opt.optim == 'bert_adam':
from pytorch_transformers import AdamW
pytorch_optim = AdamW
else:
raise NotImplementedError
if opt.adv:
d = Discriminator(opt.hidden_size)
framework = FewShotREFramework(train_data_loader,
val_data_loader,
test_data_loader,
adv_data_loader,
adv=opt.adv,
d=d)
else:
framework = FewShotREFramework(train_data_loader, val_data_loader,
test_data_loader)
prefix = '-'.join(
[model_name, encoder_name, opt.train, opt.val,
str(N), str(K)])
if opt.adv is not None:
prefix += '-adv_' + opt.adv
if opt.na_rate != 0:
prefix += '-na{}'.format(opt.na_rate)
if model_name == 'proto':
model = Proto(sentence_encoder, hidden_size=opt.hidden_size)
elif model_name == 'gnn':
model = GNN(sentence_encoder, N)
elif model_name == 'snail':
print("HINT: SNAIL works only in PyTorch 0.3.1")
model = SNAIL(sentence_encoder, N, K)
elif model_name == 'metanet':
model = MetaNet(N, K, sentence_encoder.embedding, max_length)
elif model_name == 'siamese':
model = Siamese(sentence_encoder,
hidden_size=opt.hidden_size,
dropout=opt.dropout)
elif model_name == 'pair':
model = Pair(sentence_encoder, hidden_size=opt.hidden_size)
else:
raise NotImplementedError
if not os.path.exists('checkpoint'):
os.mkdir('checkpoint')
ckpt = 'checkpoint/{}.pth.tar'.format(prefix)
if opt.save_ckpt:
ckpt = opt.save_ckpt
if torch.cuda.is_available():
model.cuda()
if not opt.only_test:
if encoder_name == 'bert':
bert_optim = True
else:
bert_optim = False
framework.train(model,
prefix,
batch_size,
trainN,
N,
K,
Q,
pytorch_optim=pytorch_optim,
load_ckpt=opt.load_ckpt,
save_ckpt=ckpt,
na_rate=opt.na_rate,
val_step=opt.val_step,
fp16=opt.fp16,
pair=opt.pair,
train_iter=opt.train_iter,
val_iter=opt.val_iter,
bert_optim=bert_optim)
else:
ckpt = opt.load_ckpt
acc = 0
his_acc = []
total_test_round = 5
for i in range(total_test_round):
cur_acc = framework.eval(model,
batch_size,
N,
K,
Q,
opt.test_iter,
na_rate=opt.na_rate,
ckpt=ckpt,
pair=opt.pair)
his_acc.append(cur_acc)
acc += cur_acc
acc /= total_test_round
nhis_acc = np.array(his_acc)
error = nhis_acc.std() * 1.96 / (nhis_acc.shape[0]**0.5)
print("RESULT: %.2f\\pm%.2f" % (acc * 100, error * 100))
result_file = open('./result.txt', 'a+')
result_file.write(
"test data: %12s | model: %45s | acc: %.6f\n | error: %.6f\n" %
(opt.test, prefix, acc, error))
result_file = open('./result_detail.txt', 'a+')
result_detail = {
'test': opt.test,
'model': prefix,
'acc': acc,
'his': his_acc
}
result_file.write("%s\n" % (json.dumps(result_detail)))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--train', default='train', help='train file')
parser.add_argument('--test', default='test_sim', help='test file')
parser.add_argument('--N',
default=2,
type=int,
help='Number of example concencate')
parser.add_argument('--batch_size', default=1, type=int, help='batch size')
parser.add_argument('--model', default='proto', help='model name')
parser.add_argument('--encoder', default='cnn', help='cnn, lstm')
parser.add_argument('--max_length',
default=32,
type=int,
help='max length')
parser.add_argument('--hidden_size',
default=64,
type=int,
help='hidden size')
opt = parser.parse_args()
N = opt.N
model_name = opt.model
encoder_name = opt.encoder
max_length = opt.max_length
print("model: {}".format(model_name))
print("encoder: {}".format(encoder_name))
print("max_length: {}".format(max_length))
print('Preparing vocab.')
vocab = Vocab(opt.train, './data')
char2id = vocab.prepare_vocab()
print('Prepare done!')
sentence_encoder = SentenceEncoder(char2id,
max_length,
opt.hidden_size,
opt.hidden_size,
encoder=opt.encoder)
prefix = '-'.join([model_name, encoder_name, opt.train, 'dev', str(N)])
model = Proto(sentence_encoder, hidden_size=opt.hidden_size)
ckpt = 'checkpoint/{}.pth.tar'.format(prefix)
if torch.cuda.is_available():
model.cuda()
model.eval()
state_dict = __load_model__(ckpt)['state_dict']
own_state = model.state_dict()
for name, param in state_dict.items():
if name not in own_state:
continue
own_state[name].copy_(param)
abbs, fulls = __load_data__(opt.test, './data')
ground_truth = {}
for i in range(len(abbs)):
if abbs[i] in ground_truth:
ground_truth[abbs[i]].append(fulls[i])
else:
ground_truth[abbs[i]] = []
abbs_set = list(set(abbs))
fulls_set = list(set(fulls))
abbs_data = []
abbs_mask = []
fulls_data = []
fulls_mask = []
for i in range(len(abbs_set)):
abbs_data_ = []
abbs_mask_ = []
fulls_data_ = []
fulls_mask_ = []
for j in range(len(fulls_set)):
indexed_abbs, mask_abb, indexed_target, mask_target = sentence_encoder.tokenize_test(
abbs_set[i], fulls_set[j])
indexed_abbs = torch.tensor(indexed_abbs).long().unsqueeze(0)
mask_abb = torch.tensor(mask_abb).long().unsqueeze(0)
indexed_target = torch.tensor(indexed_target).long().unsqueeze(0)
mask_target = torch.tensor(mask_target).long().unsqueeze(0)
abbs_data_.append(indexed_abbs)
abbs_mask_.append(mask_abb)
fulls_data_.append(indexed_target)
fulls_mask_.append(mask_target)
abbs_data.append(abbs_data_)
abbs_mask.append(abbs_mask_)
fulls_data.append(fulls_data_)
fulls_mask.append(fulls_mask_)
print('Start Evaluating...')
with torch.no_grad():
scores = []
for i in tqdm(range(len(abbs_set))):
scores_ = []
for j in range(len(fulls_set)):
input1, mask1, input2, mask2 = abbs_data[i][j], abbs_mask[i][
j], fulls_data[i][j], fulls_mask[i][j]
if torch.cuda.is_available():
input1 = input1.cuda()
input2 = input2.cuda()
mask1 = mask1.cuda()
mask2 = mask2.cuda()
logits = model(input1, mask1, input2, mask2)
scores_.append(logits.cpu().numpy()[0][0])
scores.append(scores_)
scores = np.array(scores)
print(scores.shape)
score_arg = np.argsort(scores, axis=1)
score_sorted = np.sort(scores, axis=1)
score_arg = score_arg[:, :10]
score_sorted = score_sorted[:, :10]
result = []
for i in range(score_arg.shape[0]):
result.append({
'abbreviation':
abbs_set[i],
'predicted':
list(map(lambda x: fulls_set[x], score_arg[i])),
'ground truth':
ground_truth[abbs_set[i]],
'score':
str(score_sorted[i])
})
f = open('result.json', 'w', encoding='utf8')
json.dump(result, f)
f.close()
