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| import sys | |
| from pathlib import Path | |
| import subprocess | |
| import torch | |
| from ..utils.base_model import BaseModel | |
| from hloc import logger | |
| rord_path = Path(__file__).parent / "../../third_party" | |
| sys.path.append(str(rord_path)) | |
| from RoRD.lib.model_test import D2Net as _RoRD | |
| from RoRD.lib.pyramid import process_multiscale | |
| class RoRD(BaseModel): | |
| default_conf = { | |
| "model_name": "rord.pth", | |
| "checkpoint_dir": rord_path / "RoRD" / "models", | |
| "use_relu": True, | |
| "multiscale": False, | |
| "max_keypoints": 1024, | |
| } | |
| required_inputs = ["image"] | |
| weight_urls = { | |
| "rord.pth": "https://drive.google.com/uc?id=12414ZGKwgPAjNTGtNrlB4VV9l7W76B2o&confirm=t", | |
| } | |
| proxy = "http://localhost:1080" | |
| def _init(self, conf): | |
| model_path = conf["checkpoint_dir"] / conf["model_name"] | |
| link = self.weight_urls[conf["model_name"]] | |
| if not model_path.exists(): | |
| model_path.parent.mkdir(exist_ok=True) | |
| cmd_wo_proxy = ["gdown", link, "-O", str(model_path)] | |
| cmd = ["gdown", link, "-O", str(model_path), "--proxy", self.proxy] | |
| logger.info(f"Downloading the RoRD model with `{cmd_wo_proxy}`.") | |
| try: | |
| subprocess.run(cmd_wo_proxy, check=True) | |
| except subprocess.CalledProcessError as e: | |
| logger.info(f"Downloading the RoRD model with `{cmd}`.") | |
| try: | |
| subprocess.run(cmd, check=True) | |
| except subprocess.CalledProcessError as e: | |
| logger.error(f"Failed to download the RoRD model.") | |
| raise e | |
| self.net = _RoRD( | |
| model_file=model_path, use_relu=conf["use_relu"], use_cuda=False | |
| ) | |
| logger.info(f"Load RoRD model done.") | |
| def _forward(self, data): | |
| image = data["image"] | |
| image = image.flip(1) # RGB -> BGR | |
| norm = image.new_tensor([103.939, 116.779, 123.68]) | |
| image = image * 255 - norm.view(1, 3, 1, 1) # caffe normalization | |
| if self.conf["multiscale"]: | |
| keypoints, scores, descriptors = process_multiscale(image, self.net) | |
| else: | |
| keypoints, scores, descriptors = process_multiscale( | |
| image, self.net, scales=[1] | |
| ) | |
| keypoints = keypoints[:, [1, 0]] # (x, y) and remove the scale | |
| idxs = scores.argsort()[-self.conf["max_keypoints"] or None :] | |
| keypoints = keypoints[idxs, :2] | |
| descriptors = descriptors[idxs] | |
| scores = scores[idxs] | |
| return { | |
| "keypoints": torch.from_numpy(keypoints)[None], | |
| "scores": torch.from_numpy(scores)[None], | |
| "descriptors": torch.from_numpy(descriptors.T)[None], | |
| } | |