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import lmdb
import os, sys
from data_utils import get_tokenizer
def initialize(file_name):
env = lmdb.open(file_name, "r")
return env
def insert(env, sid, name):
txn = env.begin(write=True)
txn.put(str(sid).encode('utf-8'), name.encode('utf-8'))
txn.commit()
def delete(env, sid):
txn = env.begin(write=True)
txn.delete(str(sid).encode('utf-8'))
txn.commit()
def update(env, sid, name):
txn = env.begin(write=True)
txn.put(str(sid).encode('utf-8'), name.encode('utf-8'))
txn.commit()
import pickle
def search(env, sid):
txn = env.begin()
data = pickle.loads(txn.get(str(sid).encode('utf-8')))
return data
import argparse
import torch
from torchvision.utils import save_image
if __name__ == "__main__":
# settings
lmdb_path = "data/ali_vqvae_hard_biggerset_011.lmdb"
output_path = f"test_lmdb_{lmdb_path.split('/')[-1]}.jpg"
args = argparse.Namespace()
args.img_tokenizer_path = 'pretrained/vqvae/vqvae_hard_biggerset_011.pt'
args.img_tokenizer_num_tokens = None
device = 'cuda:0'
torch.cuda.set_device(device)
tokenizer = get_tokenizer(args)
with lmdb.open(lmdb_path, readonly=True, lock=False) as env:
imgs = []
txts = []
for i in range(20,50):
txt, images = tokenizer.DecodeIds(search(env, i))
txts.append(txt)
imgs.append(images[0])
print(txts)
imgs = torch.cat(imgs, dim=0)
save_image(imgs, output_path, normalize=True, range=None)
