third_party/dust3r/demo.py

#!/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).
#
# --------------------------------------------------------
# dust3r gradio demo executable
# --------------------------------------------------------
import os
import tempfile

import matplotlib.pyplot as pl
import torch
from dust3r.demo import get_args_parser, main_demo, set_print_with_timestamp
from dust3r.model import AsymmetricCroCo3DStereo

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()
    set_print_with_timestamp()

    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
    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,
        )