#!/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)