hloc/extractors/rekd.py

import sys
from pathlib import Path
import subprocess
import torch

from ..utils.base_model import BaseModel

rekd_path = Path(__file__).parent / "../../third_party"
sys.path.append(str(rekd_path))
from REKD.training.model.REKD import REKD as REKD_

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


class REKD(BaseModel):
    default_conf = {
        "model_name": "v0",
        "keypoint_threshold": 0.1,
    }
    required_inputs = ["image"]

    def _init(self, conf):
        model_path = (
            rekd_path / "checkpoints" / f'PointModel_{conf["model_name"]}.pth'
        )
        if not model_path.exists():
            print(f"No model found at {model_path}")
        self.net = REKD_(is_test=True)
        state_dict = torch.load(model_path, map_location="cpu")
        self.net.load_state_dict(state_dict["model_state"])

    def _forward(self, data):
        image = data["image"]
        keypoints, scores, descriptors = self.net(image)
        _, _, Hc, Wc = descriptors.shape

        # Scores & Descriptors
        kpts_score = (
            torch.cat([keypoints, scores], dim=1)
            .view(3, -1)
            .t()
            .cpu()
            .detach()
            .numpy()
        )
        descriptors = (
            descriptors.view(256, Hc, Wc)
            .view(256, -1)
            .t()
            .cpu()
            .detach()
            .numpy()
        )

        # Filter based on confidence threshold
        descriptors = descriptors[
            kpts_score[:, 0] > self.conf["keypoint_threshold"], :
        ]
        kpts_score = kpts_score[
            kpts_score[:, 0] > self.conf["keypoint_threshold"], :
        ]
        keypoints = kpts_score[:, 1:]
        scores = kpts_score[:, 0]

        return {
            "keypoints": torch.from_numpy(keypoints)[None],
            "scores": torch.from_numpy(scores)[None],
            "descriptors": torch.from_numpy(descriptors.T)[None],
        }