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| import subprocess | |
| import sys | |
| from pathlib import Path | |
| import torch | |
| import torchvision.transforms as transforms | |
| from hloc import logger | |
| from ..utils.base_model import BaseModel | |
| dedode_path = Path(__file__).parent / "../../third_party/DeDoDe" | |
| sys.path.append(str(dedode_path)) | |
| from DeDoDe import dedode_descriptor_B, dedode_detector_L | |
| from DeDoDe.utils import to_pixel_coords | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| class DeDoDe(BaseModel): | |
| default_conf = { | |
| "name": "dedode", | |
| "model_detector_name": "dedode_detector_L.pth", | |
| "model_descriptor_name": "dedode_descriptor_B.pth", | |
| "max_keypoints": 2000, | |
| "match_threshold": 0.2, | |
| "dense": False, # Now fixed to be false | |
| } | |
| required_inputs = [ | |
| "image", | |
| ] | |
| weight_urls = { | |
| "dedode_detector_L.pth": "https://github.com/Parskatt/DeDoDe/releases/download/dedode_pretrained_models/dedode_detector_L.pth", | |
| "dedode_descriptor_B.pth": "https://github.com/Parskatt/DeDoDe/releases/download/dedode_pretrained_models/dedode_descriptor_B.pth", | |
| } | |
| # Initialize the line matcher | |
| def _init(self, conf): | |
| model_detector_path = ( | |
| dedode_path / "pretrained" / conf["model_detector_name"] | |
| ) | |
| model_descriptor_path = ( | |
| dedode_path / "pretrained" / conf["model_descriptor_name"] | |
| ) | |
| self.normalizer = transforms.Normalize( | |
| mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] | |
| ) | |
| # Download the model. | |
| if not model_detector_path.exists(): | |
| model_detector_path.parent.mkdir(exist_ok=True) | |
| link = self.weight_urls[conf["model_detector_name"]] | |
| cmd = ["wget", "--quiet", link, "-O", str(model_detector_path)] | |
| logger.info(f"Downloading the DeDoDe detector model with `{cmd}`.") | |
| subprocess.run(cmd, check=True) | |
| if not model_descriptor_path.exists(): | |
| model_descriptor_path.parent.mkdir(exist_ok=True) | |
| link = self.weight_urls[conf["model_descriptor_name"]] | |
| cmd = ["wget", "--quiet", link, "-O", str(model_descriptor_path)] | |
| logger.info( | |
| f"Downloading the DeDoDe descriptor model with `{cmd}`." | |
| ) | |
| subprocess.run(cmd, check=True) | |
| # load the model | |
| weights_detector = torch.load(model_detector_path, map_location="cpu") | |
| weights_descriptor = torch.load( | |
| model_descriptor_path, map_location="cpu" | |
| ) | |
| self.detector = dedode_detector_L( | |
| weights=weights_detector, device=device | |
| ) | |
| self.descriptor = dedode_descriptor_B( | |
| weights=weights_descriptor, device=device | |
| ) | |
| logger.info("Load DeDoDe model done.") | |
| def _forward(self, data): | |
| """ | |
| data: dict, keys: {'image0','image1'} | |
| image shape: N x C x H x W | |
| color mode: RGB | |
| """ | |
| img0 = self.normalizer(data["image"].squeeze()).float()[None] | |
| H_A, W_A = img0.shape[2:] | |
| # step 1: detect keypoints | |
| detections_A = None | |
| batch_A = {"image": img0} | |
| if self.conf["dense"]: | |
| detections_A = self.detector.detect_dense(batch_A) | |
| else: | |
| detections_A = self.detector.detect( | |
| batch_A, num_keypoints=self.conf["max_keypoints"] | |
| ) | |
| keypoints_A, P_A = detections_A["keypoints"], detections_A["confidence"] | |
| # step 2: describe keypoints | |
| # dim: 1 x N x 256 | |
| description_A = self.descriptor.describe_keypoints( | |
| batch_A, keypoints_A | |
| )["descriptions"] | |
| keypoints_A = to_pixel_coords(keypoints_A, H_A, W_A) | |
| return { | |
| "keypoints": keypoints_A, # 1 x N x 2 | |
| "descriptors": description_A.permute(0, 2, 1), # 1 x 256 x N | |
| "scores": P_A, # 1 x N | |
| } | |