File size: 5,140 Bytes
8320ccc
9223079
8320ccc
9223079
8320ccc
9223079
8320ccc
49a0323
8320ccc
9223079
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e15a186
 
 
 
 
 
 
 
9223079
e15a186
 
 
9223079
 
 
69d8141
9223079
 
 
 
69d8141
9223079
b075789
9223079
b075789
9223079
 
 
 
 
 
e15a186
 
 
 
9223079
 
 
 
 
8320ccc
9223079
 
8004049
 
9223079
 
 
 
2eaeef9
 
 
 
 
 
9223079
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
472119d
9223079
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
import subprocess
import sys
from collections import OrderedDict, namedtuple
from pathlib import Path

import torch

from .. import logger
from ..utils.base_model import BaseModel

sgmnet_path = Path(__file__).parent / "../../third_party/SGMNet"
sys.path.append(str(sgmnet_path))

from sgmnet import matcher as SGM_Model

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


class SGMNet(BaseModel):
    default_conf = {
        "name": "SGM",
        "model_name": "model_best.pth",
        "seed_top_k": [256, 256],
        "seed_radius_coe": 0.01,
        "net_channels": 128,
        "layer_num": 9,
        "head": 4,
        "seedlayer": [0, 6],
        "use_mc_seeding": True,
        "use_score_encoding": False,
        "conf_bar": [1.11, 0.1],
        "sink_iter": [10, 100],
        "detach_iter": 1000000,
        "match_threshold": 0.2,
    }
    required_inputs = [
        "image0",
        "image1",
    ]
    weight_urls = {
        "model_best.pth": "https://drive.google.com/uc?id=1Ca0WmKSSt2G6P7m8YAOlSAHEFar_TAWb&confirm=t",
    }
    proxy = "http://localhost:1080"

    # Initialize the line matcher
    def _init(self, conf):
        sgmnet_weights = sgmnet_path / "weights/sgm/root" / conf["model_name"]

        link = self.weight_urls[conf["model_name"]]
        tar_path = sgmnet_path / "weights.tar.gz"
        # Download the model.
        if not sgmnet_weights.exists():
            if not tar_path.exists():
                cmd = [
                    "gdown",
                    link,
                    "-O",
                    str(tar_path),
                    "--proxy",
                    self.proxy,
                ]
                cmd_wo_proxy = ["gdown", link, "-O", str(tar_path)]
                logger.info(
                    f"Downloading the SGMNet model with `{cmd_wo_proxy}`."
                )
                try:
                    subprocess.run(cmd_wo_proxy, check=True)
                except subprocess.CalledProcessError as e:
                    logger.info(f"Downloading failed {e}.")
                    logger.info(f"Downloading the SGMNet model with `{cmd}`.")
                    try:
                        subprocess.run(cmd, check=True)
                    except subprocess.CalledProcessError as e:
                        logger.error("Failed to download the SGMNet model.")
                        raise e
            cmd = ["tar", "-xvf", str(tar_path), "-C", str(sgmnet_path)]
            logger.info(f"Unzip model file `{cmd}`.")
            subprocess.run(cmd, check=True)

        # config
        config = namedtuple("config", conf.keys())(*conf.values())
        self.net = SGM_Model(config)
        checkpoint = torch.load(sgmnet_weights, map_location="cpu")
        # for ddp model
        if (
            list(checkpoint["state_dict"].items())[0][0].split(".")[0]
            == "module"
        ):
            new_stat_dict = OrderedDict()
            for key, value in checkpoint["state_dict"].items():
                new_stat_dict[key[7:]] = value
            checkpoint["state_dict"] = new_stat_dict
        self.net.load_state_dict(checkpoint["state_dict"])
        logger.info("Load SGMNet model done.")

    def _forward(self, data):
        x1 = data["keypoints0"].squeeze()  # N x 2
        x2 = data["keypoints1"].squeeze()
        score1 = data["scores0"].reshape(-1, 1)  # N x 1
        score2 = data["scores1"].reshape(-1, 1)
        desc1 = data["descriptors0"].permute(0, 2, 1)  # 1 x N x 128
        desc2 = data["descriptors1"].permute(0, 2, 1)
        size1 = (
            torch.tensor(data["image0"].shape[2:]).flip(0).to(x1.device)
        )  # W x H -> x & y
        size2 = (
            torch.tensor(data["image1"].shape[2:]).flip(0).to(x2.device)
        )  # W x H
        norm_x1 = self.normalize_size(x1, size1)
        norm_x2 = self.normalize_size(x2, size2)

        x1 = torch.cat((norm_x1, score1), dim=-1)  # N x 3
        x2 = torch.cat((norm_x2, score2), dim=-1)
        input = {"x1": x1[None], "x2": x2[None], "desc1": desc1, "desc2": desc2}
        input = {
            k: v.to(device).float() if isinstance(v, torch.Tensor) else v
            for k, v in input.items()
        }
        pred = self.net(input, test_mode=True)

        p = pred["p"]  # shape: N * M
        indices0 = self.match_p(p[0, :-1, :-1])
        pred = {
            "matches0": indices0.unsqueeze(0),
            "matching_scores0": torch.zeros(indices0.size(0)).unsqueeze(0),
        }
        return pred

    def match_p(self, p):
        score, index = torch.topk(p, k=1, dim=-1)
        _, index2 = torch.topk(p, k=1, dim=-2)
        mask_th, index, index2 = (
            score[:, 0] > self.conf["match_threshold"],
            index[:, 0],
            index2.squeeze(0),
        )
        mask_mc = index2[index] == torch.arange(len(p)).to(device)
        mask = mask_th & mask_mc
        indices0 = torch.where(mask, index, index.new_tensor(-1))
        return indices0

    def normalize_size(self, x, size, scale=1):
        norm_fac = size.max()
        return (x - size / 2 + 0.5) / (norm_fac * scale)