import torch import warnings from kornia.feature.loftr.loftr import default_cfg from kornia.feature import LoFTR as LoFTR_ from hloc import logger from ..utils.base_model import BaseModel class LoFTR(BaseModel): default_conf = { "weights": "outdoor", "match_threshold": 0.2, "max_num_matches": None, } required_inputs = ["image0", "image1"] def _init(self, conf): cfg = default_cfg cfg["match_coarse"]["thr"] = conf["match_threshold"] self.net = LoFTR_(pretrained=conf["weights"], config=cfg) logger.info(f"Loaded LoFTR with weights {conf['weights']}") def _forward(self, data): # For consistency with hloc pairs, we refine kpts in image0! rename = { "keypoints0": "keypoints1", "keypoints1": "keypoints0", "image0": "image1", "image1": "image0", "mask0": "mask1", "mask1": "mask0", } data_ = {rename[k]: v for k, v in data.items()} with warnings.catch_warnings(): warnings.simplefilter("ignore") pred = self.net(data_) scores = pred["confidence"] top_k = self.conf["max_num_matches"] if top_k is not None and len(scores) > top_k: keep = torch.argsort(scores, descending=True)[:top_k] pred["keypoints0"], pred["keypoints1"] = ( pred["keypoints0"][keep], pred["keypoints1"][keep], ) scores = scores[keep] # Switch back indices pred = {(rename[k] if k in rename else k): v for k, v in pred.items()} pred["scores"] = scores del pred["confidence"] return pred