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| # -*- coding: UTF-8 -*- | |
| import sys | |
| from pathlib import Path | |
| import torchvision.transforms as tvf | |
| from .. import logger | |
| from ..utils.base_model import BaseModel | |
| pram_path = Path(__file__).parent / "../../third_party/pram" | |
| sys.path.append(str(pram_path)) | |
| from nets.sfd2 import load_sfd2 | |
| class SFD2(BaseModel): | |
| default_conf = { | |
| "max_keypoints": 4096, | |
| "model_name": "sfd2_20230511_210205_resnet4x.79.pth", | |
| "conf_th": 0.001, | |
| } | |
| required_inputs = ["image"] | |
| def _init(self, conf): | |
| self.conf = {**self.default_conf, **conf} | |
| self.norm_rgb = tvf.Normalize( | |
| mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] | |
| ) | |
| model_fn = pram_path / "weights" / self.conf["model_name"] | |
| self.net = load_sfd2(weight_path=model_fn).eval() | |
| logger.info("Load SFD2 model done.") | |
| def _forward(self, data): | |
| pred = self.net.extract_local_global( | |
| data={"image": self.norm_rgb(data["image"])}, config=self.conf | |
| ) | |
| out = { | |
| "keypoints": pred["keypoints"][0][None], | |
| "scores": pred["scores"][0][None], | |
| "descriptors": pred["descriptors"][0][None], | |
| } | |
| return out | |