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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).
#
# --------------------------------------------------------
# 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,
)
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