Spaces:
Running
Running
File size: 1,288 Bytes
9223079 b74523b 9223079 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 |
import torch
import warnings
from ..utils.base_model import BaseModel
import sys
from pathlib import Path
sys.path.append(str(Path(__file__).parent / "../../third_party"))
from TopicFM.src.models.topic_fm import TopicFM as _TopicFM
from TopicFM.src import get_model_cfg
topicfm_path = Path(__file__).parent / "../../third_party/TopicFM"
class TopicFM(BaseModel):
default_conf = {
"weights": "outdoor",
"match_threshold": 0.2,
"n_sampling_topics": 4,
}
required_inputs = ["image0", "image1"]
def _init(self, conf):
_conf = dict(get_model_cfg())
_conf["match_coarse"]["thr"] = conf["match_threshold"]
_conf["coarse"]["n_samples"] = conf["n_sampling_topics"]
weight_path = topicfm_path / "pretrained/model_best.ckpt"
self.net = _TopicFM(config=_conf)
ckpt_dict = torch.load(weight_path, map_location="cpu")
self.net.load_state_dict(ckpt_dict["state_dict"])
def _forward(self, data):
data_ = {
"image0": data["image0"],
"image1": data["image1"],
}
self.net(data_)
pred = {
"keypoints0": data_["mkpts0_f"],
"keypoints1": data_["mkpts1_f"],
"mconf": data_["mconf"],
}
return pred
|