File size: 1,874 Bytes
83ae704 f64a2f2 83ae704 f64a2f2 83ae704 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 |
#!/usr/bin/env python3
# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
# gradio demo executable
# --------------------------------------------------------
import os
import torch
import tempfile
from mast3r.demo import get_args_parser, main_demo
from mast3r.model import AsymmetricMASt3R
from mast3r.utils.misc import hash_md5
import matplotlib.pyplot as pl
pl.ion()
torch.backends.cuda.matmul.allow_tf32 = True # for gpu >= Ampere and pytorch >= 1.12
if __name__ == '__main__':
parser = get_args_parser()
args = parser.parse_args()
if args.server_name is not None:
server_name = args.server_name
else:
server_name = '0.0.0.0' if args.local_network else '127.0.0.1'
if args.weights is not None:
weights_path = args.weights
else:
weights_path = "naver/" + args.model_name
model = AsymmetricMASt3R.from_pretrained(weights_path).to(args.device)
chkpt_tag = hash_md5(weights_path)
# mast3r will write the 3D model inside tmpdirname/chkpt_tag
if args.tmp_dir is not None:
tmpdirname = args.tmp_dir
cache_path = os.path.join(tmpdirname, chkpt_tag)
os.makedirs(cache_path, exist_ok=True)
main_demo(cache_path, model, args.device, args.image_size, server_name, args.server_port, silent=args.silent,
share=args.share)
else:
with tempfile.TemporaryDirectory(suffix='_mast3r_gradio_demo') as tmpdirname:
cache_path = os.path.join(tmpdirname, chkpt_tag)
os.makedirs(cache_path, exist_ok=True)
main_demo(tmpdirname, model, args.device, args.image_size,
server_name, args.server_port, silent=args.silent,
share=args.share)
|