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#!/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).
#
# --------------------------------------------------------
# mast3r gradio demo executable
# --------------------------------------------------------
import os
import torch
import tempfile
import mast3r.utils.path_to_dust3r # noqa
from dust3r.model import AsymmetricCroCo3DStereo
from mast3r.model import AsymmetricMASt3R
from dust3r.demo import get_args_parser as dust3r_get_args_parser
from dust3r.demo import main_demo
import matplotlib.pyplot as pl
pl.ion()
torch.backends.cuda.matmul.allow_tf32 = True # for gpu >= Ampere and pytorch >= 1.12
def get_args_parser():
parser = dust3r_get_args_parser()
actions = parser._actions
for action in actions:
if action.dest == 'model_name':
action.choices.append('MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric')
# change defaults
parser.prog = 'mast3r demo'
return parser
if __name__ == '__main__':
parser = get_args_parser()
args = parser.parse_args()
if args.tmp_dir is not None:
tmp_path = args.tmp_dir
os.makedirs(tmp_path, exist_ok=True)
tempfile.tempdir = tmp_path
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
try:
model = AsymmetricMASt3R.from_pretrained(weights_path).to(args.device)
except Exception as e:
model = AsymmetricCroCo3DStereo.from_pretrained(weights_path).to(args.device)
# dust3r will write the 3D model inside tmpdirname
with tempfile.TemporaryDirectory(suffix='dust3r_gradio_demo') as tmpdirname:
if not args.silent:
print('Outputing stuff in', tmpdirname)
main_demo(tmpdirname, model, args.device, args.image_size, server_name, args.server_port, silent=args.silent)
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