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| import sys | |
| from pathlib import Path | |
| from ..utils.base_model import BaseModel | |
| from .. import logger | |
| lightglue_path = Path(__file__).parent / "../../third_party/LightGlue" | |
| sys.path.append(str(lightglue_path)) | |
| from lightglue import LightGlue as LG | |
| class LightGlue(BaseModel): | |
| default_conf = { | |
| "match_threshold": 0.2, | |
| "filter_threshold": 0.2, | |
| "width_confidence": 0.99, # for point pruning | |
| "depth_confidence": 0.95, # for early stopping, | |
| "features": "superpoint", | |
| "model_name": "superpoint_lightglue.pth", | |
| "flash": True, # enable FlashAttention if available. | |
| "mp": False, # enable mixed precision | |
| } | |
| required_inputs = [ | |
| "image0", | |
| "keypoints0", | |
| "scores0", | |
| "descriptors0", | |
| "image1", | |
| "keypoints1", | |
| "scores1", | |
| "descriptors1", | |
| ] | |
| def _init(self, conf): | |
| weight_path = lightglue_path / "weights" / conf["model_name"] | |
| conf["weights"] = str(weight_path) | |
| conf["filter_threshold"] = conf["match_threshold"] | |
| self.net = LG(**conf) | |
| logger.info(f"Load lightglue model done.") | |
| def _forward(self, data): | |
| input = {} | |
| input["image0"] = { | |
| "image": data["image0"], | |
| "keypoints": data["keypoints0"], | |
| "descriptors": data["descriptors0"].permute(0, 2, 1), | |
| } | |
| input["image1"] = { | |
| "image": data["image1"], | |
| "keypoints": data["keypoints1"], | |
| "descriptors": data["descriptors1"].permute(0, 2, 1), | |
| } | |
| return self.net(input) | |