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# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
# MASt3R model class
# --------------------------------------------------------
import torch
import torch.nn.functional as F
import os
from mast3r.catmlp_dpt_head import mast3r_head_factory
import mast3r.utils.path_to_dust3r # noqa
from dust3r.model import AsymmetricCroCo3DStereo # noqa
from dust3r.utils.misc import transpose_to_landscape # noqa
import urllib
inf = float('inf')
def load_model(model_url, device, landscape_only=False, verbose=True):
if verbose:
print('... loading model from', model_url)
ckpt = torch.hub.load_state_dict_from_url(model_url, map_location='cpu', progress=verbose)
args = ckpt['args'].model.replace("ManyAR_PatchEmbed", "PatchEmbedDust3R")
if 'landscape_only' not in args:
args = args[:-1] + ', landscape_only=False)'
else:
args = args.replace(" ", "").replace('landscape_only=True', 'landscape_only=False')
assert "landscape_only=False" in args
if verbose:
print(f"instantiating : {args}")
net = eval(args)
s = net.load_state_dict(ckpt['model'], strict=False)
if verbose:
print(s)
return net.to(device)
class AsymmetricMASt3R(AsymmetricCroCo3DStereo):
def __init__(self, desc_mode=('norm'), two_confs=False, desc_conf_mode=None, **kwargs):
self.desc_mode = desc_mode
self.two_confs = two_confs
self.desc_conf_mode = desc_conf_mode
super().__init__(**kwargs)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kw):
if os.path.isfile(pretrained_model_name_or_path) or urllib.parse.urlparse(pretrained_model_name_or_path).scheme in ('http', 'https'):
return load_model(pretrained_model_name_or_path, device='cpu', **kw)
else:
return super(AsymmetricCroCo3DStereo, cls).from_pretrained(pretrained_model_name_or_path, **kw)
def set_downstream_head(self, output_mode, head_type, landscape_only, depth_mode, conf_mode, patch_size, img_size, **kw):
assert img_size[0] % patch_size == 0 and img_size[
1] % patch_size == 0, f'{img_size=} must be multiple of {patch_size=}'
self.output_mode = output_mode
self.head_type = head_type
self.depth_mode = depth_mode
self.conf_mode = conf_mode
if self.desc_conf_mode is None:
self.desc_conf_mode = conf_mode
# allocate heads
self.downstream_head1 = mast3r_head_factory(head_type, output_mode, self, has_conf=bool(conf_mode))
self.downstream_head2 = mast3r_head_factory(head_type, output_mode, self, has_conf=bool(conf_mode))
# magic wrapper
self.head1 = transpose_to_landscape(self.downstream_head1, activate=landscape_only)
self.head2 = transpose_to_landscape(self.downstream_head2, activate=landscape_only)
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