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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).
#
# --------------------------------------------------------
# gradio demo executable
# --------------------------------------------------------
#!/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
# --------------------------------------------------------
#!/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
# --------------------------------------------------------
#!/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
# --------------------------------------------------------
#!/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
# --------------------------------------------------------
#!/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
# --------------------------------------------------------
#!/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 contextlib import nullcontext
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
import argparse
def get_args_parser():
parser = argparse.ArgumentParser(description="MASt3R Demo")
parser.add_argument("--weights", type=str, default=None, help="Path to the weights file.")
parser.add_argument("--model_name", type=str, default=None, choices=[
'MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric'], help="Name of the model to use.")
parser.add_argument("--device", type=str, default='cuda', help="Device to run the model on.")
parser.add_argument("--server_name", type=str, default=None, help="Server name to use.")
parser.add_argument("--local_network", action='store_true', help="Run on local network.")
parser.add_argument("--image_size", type=int, choices=[512, 224], default=512, help="Size of the images.")
parser.add_argument("--server_port", type=int, default=None, help="Port for the server.")
parser.add_argument("--tmp_dir", type=str, default=None, help="Temporary directory.")
parser.add_argument("--silent", action='store_true', help="Run silently.")
parser.add_argument("--share", action='store_true', help="Share the application.")
parser.add_argument("--gradio_delete_cache", action='store_true', help="Delete Gradio cache.")
return parser
def get_default_weights_path(model_name):
# Construct default weights path based on model_name
return f"naver/{model_name}"
if __name__ == '__main__':
parser = get_args_parser()
args = parser.parse_args()
# Set default values for required arguments
if args.weights is None and args.model_name is None:
args.model_name = 'MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric'
if args.weights is None:
args.weights = f"naver/{args.model_name}"
# Rest of the code for setting up the server and loading the model
server_name = args.server_name or ('0.0.0.0' if args.local_network else '127.0.0.1')
weights_path = args.weights
# Load the model
model = AsymmetricMASt3R.from_pretrained(weights_path).to(args.device)
chkpt_tag = hash_md5(weights_path)
def get_context(tmp_dir):
return tempfile.TemporaryDirectory(suffix='_mast3r_gradio_demo') if tmp_dir is None \
else nullcontext(tmp_dir)
with get_context(args.tmp_dir) as tmpdirname:
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, gradio_delete_cache=args.gradio_delete_cache)
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