import sys from pathlib import Path import subprocess import torch from ..utils.base_model import BaseModel d2net_path = Path(__file__).parent / "../../third_party/d2net" sys.path.append(str(d2net_path)) from lib.model_test import D2Net as _D2Net from lib.pyramid import process_multiscale class D2Net(BaseModel): default_conf = { "model_name": "d2_tf.pth", "checkpoint_dir": d2net_path / "models", "use_relu": True, "multiscale": False, "max_keypoints": 1024, } required_inputs = ["image"] def _init(self, conf): model_file = conf["checkpoint_dir"] / conf["model_name"] if not model_file.exists(): model_file.parent.mkdir(exist_ok=True) cmd = [ "wget", "https://dsmn.ml/files/d2-net/" + conf["model_name"], "-O", str(model_file), ] subprocess.run(cmd, check=True) self.net = _D2Net( model_file=model_file, use_relu=conf["use_relu"], use_cuda=False ) 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], }