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import os
import pickle
import random
import faiss
from src.index import Indexer
import torch
import torch.nn.functional as F
import numpy as np
from torch.utils.data import DataLoader
from lightning import Fabric
from tqdm import tqdm
import argparse
from src.text_embedding import TextEmbeddingModel
from utils.load_dataset import load_dataset, TextDataset, load_outdomain_dataset
def load_pkl(path):
with open(path, 'rb') as f:
return pickle.load(f)
def infer(passages_dataloder,fabric,tokenizer,model,ood=False):
if fabric.global_rank == 0 :
passages_dataloder=tqdm(passages_dataloder,total=len(passages_dataloder))
if ood:
allids, allembeddings,alllabels,all_is_mixed= [],[],[],[]
else:
allids, allembeddings,alllabels,all_is_mixed,all_write_model= [],[],[],[],[]
model.model.eval()
with torch.no_grad():
for batch in passages_dataloder:
if ood:
ids, text, label, is_mixed = batch
encoded_batch = tokenizer.batch_encode_plus(
text,
return_tensors="pt",
max_length=512,
padding="max_length",
# padding=True,
truncation=True,
)
encoded_batch = {k: v.cuda() for k, v in encoded_batch.items()}
# output = model(**encoded_batch).last_hidden_state
# embeddings = pooling(output, encoded_batch)
# print(encoded_batch)
embeddings = model(encoded_batch)
# print(encoded_batch['input_ids'].shape)
embeddings = fabric.all_gather(embeddings).view(-1, embeddings.size(1))
label = fabric.all_gather(label).view(-1)
ids = fabric.all_gather(ids).view(-1)
is_mixed = fabric.all_gather(is_mixed).view(-1)
if fabric.global_rank == 0 :
allembeddings.append(embeddings.cpu())
allids.extend(ids.cpu().tolist())
alllabels.extend(label.cpu().tolist())
all_is_mixed.extend(is_mixed.cpu().tolist())
else:
ids, text, label, is_mixed, write_model = batch
encoded_batch = tokenizer.batch_encode_plus(
text,
return_tensors="pt",
max_length=512,
padding="max_length",
# padding=True,
truncation=True,
)
encoded_batch = {k: v.cuda() for k, v in encoded_batch.items()}
# output = model(**encoded_batch).last_hidden_state
# embeddings = pooling(output, encoded_batch)
# print(encoded_batch)
embeddings = model(encoded_batch)
# print(encoded_batch['input_ids'].shape)
embeddings = fabric.all_gather(embeddings).view(-1, embeddings.size(1))
label = fabric.all_gather(label).view(-1)
ids = fabric.all_gather(ids).view(-1)
is_mixed = fabric.all_gather(is_mixed).view(-1)
write_model = fabric.all_gather(write_model).view(-1)
if fabric.global_rank == 0 :
allembeddings.append(embeddings.cpu())
allids.extend(ids.cpu().tolist())
alllabels.extend(label.cpu().tolist())
all_is_mixed.extend(is_mixed.cpu().tolist())
all_write_model.extend(write_model.cpu().tolist())
if fabric.global_rank == 0 :
allembeddings = torch.cat(allembeddings, dim=0)
epsilon = 1e-6
if ood:
emb_dict,label_dict,is_mixed_dict={},{},{}
allembeddings= F.normalize(allembeddings,dim=-1)
for i in range(len(allids)):
emb_dict[allids[i]]=allembeddings[i]
label_dict[allids[i]]=alllabels[i]
is_mixed_dict[allids[i]]=all_is_mixed[i]
allids,allembeddings,alllabels,all_is_mixed=[],[],[],[]
for key in emb_dict:
allids.append(key)
allembeddings.append(emb_dict[key])
alllabels.append(label_dict[key])
all_is_mixed.append(is_mixed_dict[key])
allembeddings = torch.stack(allembeddings, dim=0)
return allids,allembeddings.numpy(),alllabels,all_is_mixed
else:
emb_dict,label_dict,is_mixed_dict,write_model_dict={},{},{},{}
allembeddings= F.normalize(allembeddings,dim=-1)
for i in range(len(allids)):
emb_dict[allids[i]]=allembeddings[i]
label_dict[allids[i]]=alllabels[i]
is_mixed_dict[allids[i]]=all_is_mixed[i]
write_model_dict[allids[i]]=all_write_model[i]
allids,allembeddings,alllabels,all_is_mixed,all_write_model=[],[],[],[],[]
for key in emb_dict:
allids.append(key)
allembeddings.append(emb_dict[key])
alllabels.append(label_dict[key])
all_is_mixed.append(is_mixed_dict[key])
all_write_model.append(write_model_dict[key])
allembeddings = torch.stack(allembeddings, dim=0)
return allids, allembeddings.numpy(),alllabels,all_is_mixed,all_write_model
else:
if ood:
return [],[],[],[]
return [],[],[],[],[]
def set_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed) # if you are using multi-GPU.
np.random.seed(seed) # Numpy module.
random.seed(seed) # Python random module.
def test(opt):
if opt.device_num>1:
fabric = Fabric(accelerator="cuda",devices=opt.device_num,strategy='ddp')
else:
fabric = Fabric(accelerator="cuda",devices=opt.device_num)
fabric.launch()
model = TextEmbeddingModel(opt.model_name).cuda()
state_dict = torch.load(opt.model_path, map_location=model.model.device)
new_state_dict={}
for key in state_dict.keys():
if key.startswith('model.'):
new_state_dict[key[6:]]=state_dict[key]
model.load_state_dict(state_dict)
tokenizer=model.tokenizer
database = load_dataset(opt.dataset_name,opt.database_path)[opt.database_name]
passage_dataset = TextDataset(database,need_ids=True)
print(len(passage_dataset))
passages_dataloder = DataLoader(passage_dataset, batch_size=opt.batch_size, num_workers=opt.num_workers, pin_memory=True)
passages_dataloder=fabric.setup_dataloaders(passages_dataloder)
model=fabric.setup(model)
train_ids, train_embeddings,train_labels, train_is_mixed, train_write_model = infer(passages_dataloder,fabric,tokenizer,model)
fabric.barrier()
if fabric.global_rank == 0:
index = Indexer(opt.embedding_dim)
index.index_data(train_ids, train_embeddings)
label_dict={}
is_mixed_dict={}
write_model_dict={}
for i in range(len(train_ids)):
label_dict[train_ids[i]]=train_labels[i]
is_mixed_dict[train_ids[i]]=train_is_mixed[i]
write_model_dict[train_ids[i]]=train_write_model[i]
if not os.path.exists(opt.save_path):
os.makedirs(opt.save_path)
index.serialize(opt.save_path)
#save label_dict using pickle
with open(os.path.join(opt.save_path, 'label_dict.pkl'), 'wb') as f:
pickle.dump(label_dict, f)
#save is_mixed_dict using pickle
with open(os.path.join(opt.save_path, 'is_mixed_dict.pkl'), 'wb') as f:
pickle.dump(is_mixed_dict, f)
#save write_model_dict using pickle
with open(os.path.join(opt.save_path, 'write_model_dict.pkl'), 'wb') as f:
pickle.dump(write_model_dict, f)
def add_to_existed_index(opt):
if opt.device_num>1:
fabric = Fabric(accelerator="cuda",devices=opt.device_num,strategy='ddp')
else:
fabric = Fabric(accelerator="cuda",devices=opt.device_num)
fabric.launch()
model = TextEmbeddingModel(opt.model_name).cuda()
state_dict = torch.load(opt.model_path, map_location=model.model.device)
new_state_dict={}
for key in state_dict.keys():
if key.startswith('model.'):
new_state_dict[key[6:]]=state_dict[key]
model.load_state_dict(state_dict)
tokenizer=model.tokenizer
if opt.ood:
database = load_outdomain_dataset(opt.database_path)[opt.database_name]
else:
database = load_dataset(opt.dataset_name,opt.database_path)[opt.database_name]
passage_dataset = TextDataset(database,need_ids=True,out_domain=opt.ood)
print(len(passage_dataset))
passages_dataloder = DataLoader(passage_dataset, batch_size=opt.batch_size, num_workers=opt.num_workers, pin_memory=True)
passages_dataloder=fabric.setup_dataloaders(passages_dataloder)
model=fabric.setup(model)
if opt.ood:
train_ids, train_embeddings,train_labels, train_is_mixed = infer(passages_dataloder,fabric,tokenizer,model,ood=True)
else:
train_ids, train_embeddings,train_labels, train_is_mixed, train_write_model = infer(passages_dataloder,fabric,tokenizer,model)
fabric.barrier()
if fabric.global_rank == 0:
new_index = Indexer(opt.embedding_dim)
new_index.index_data(train_ids, train_embeddings)
old_index = Indexer(opt.embedding_dim)
old_index.deserialize_from(opt.existed_index_path)
old_ids = old_index.index_id_to_db_id
# Ensure both indexes are of type IndexFlatIP
# assert isinstance(new_index.index, faiss.IndexFlatIP)
# assert isinstance(old_index.index, faiss.IndexFlatIP)
# Ensure both indexes have the same dimensionality
assert new_index.index.d == old_index.index.d
# Extract vectors from old_index.index
vectors = old_index.index.reconstruct_n(0, old_index.index.ntotal)
# Add vectors to new_index.index
new_index.index_data(old_ids, vectors)
if not os.path.exists(opt.new_save_path):
os.makedirs(opt.new_save_path)
new_index.serialize(opt.new_save_path)
if opt.ood:
label_dict=load_pkl(os.path.join(opt.existed_index_path, 'label_dict.pkl'))
is_mixed_dict=load_pkl(os.path.join(opt.existed_index_path, 'is_mixed_dict.pkl'))
for i in range(len(train_ids)):
label_dict[train_ids[i]]=train_labels[i]
is_mixed_dict[train_ids[i]]=train_is_mixed[i]
#save label_dict using pickle
with open(os.path.join(opt.new_save_path, 'label_dict.pkl'), 'wb') as f:
pickle.dump(label_dict, f)
#save is_mixed_dict using pickle
with open(os.path.join(opt.new_save_path, 'is_mixed_dict.pkl'), 'wb') as f:
pickle.dump(is_mixed_dict, f)
else:
label_dict=load_pkl(os.path.join(opt.existed_index_path, 'label_dict.pkl'))
is_mixed_dict=load_pkl(os.path.join(opt.existed_index_path, 'is_mixed_dict.pkl'))
write_model_dict=load_pkl(os.path.join(opt.existed_index_path, 'write_model_dict.pkl'))
for i in range(len(train_ids)):
label_dict[train_ids[i]]=train_labels[i]
is_mixed_dict[train_ids[i]]=train_is_mixed[i]
write_model_dict[train_ids[i]]=train_write_model[i]
#save label_dict using pickle
with open(os.path.join(opt.new_save_path, 'label_dict.pkl'), 'wb') as f:
pickle.dump(label_dict, f)
#save is_mixed_dict using pickle
with open(os.path.join(opt.new_save_path, 'is_mixed_dict.pkl'), 'wb') as f:
pickle.dump(is_mixed_dict, f)
#save write_model_dict using pickle
with open(os.path.join(opt.new_save_path, 'write_model_dict.pkl'), 'wb') as f:
pickle.dump(write_model_dict, f)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--device_num', type=int, default=1)
parser.add_argument('--batch_size', type=int, default=128)
parser.add_argument('--num_workers', type=int, default=8)
parser.add_argument('--embedding_dim', type=int, default=768)
# parser.add_argument('--mode', type=str, default='deepfake', help="deepfake,MGT or MGTDetect_CoCo")
parser.add_argument("--database_path", type=str, default="data/FALCONSet", help="Path to the data")
parser.add_argument('--dataset_name', type=str, default='falconset', help="falconset, llmdetectaive, hart")
parser.add_argument('--database_name', type=str, default='train', help="train,valid,test,test_ood")
parser.add_argument("--model_path", type=str, default="runs/authscan_v6/model_best.pth",\
help="Path to the embedding model checkpoint")
parser.add_argument('--model_name', type=str, default="FacebookAI/xlm-roberta-base", help="Model name")
parser.add_argument("--save_path", type=str, default="/output", help="Path to save the database")
parser.add_argument("--add_to_existed_index", type=int, default=0)
# parser.add_argument("--add_to_existed_index_path", type=str, default="/output", help="Path to save the database")
parser.add_argument("--ood", type=int, default=0)
parser.add_argument("--existed_index_path", type=str, default="/output", help="Path of existed index")
parser.add_argument("--new_save_path", type=str, default="/new_db", help="Path to save the database")
parser.add_argument('--seed', type=int, default=0)
opt = parser.parse_args()
set_seed(opt.seed)
if not opt.add_to_existed_index:
test(opt)
else:
add_to_existed_index(opt)
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