Spaces:
Running
Running
File size: 2,421 Bytes
8320ccc 9223079 8320ccc 9223079 8811cfe 2134b25 9223079 8811cfe 9223079 e15a186 4c930ba 9223079 e15a186 49a0323 e15a186 9223079 8811cfe e15a186 9223079 8320ccc 9223079 6cb641c 3c77caa 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 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
import subprocess
import sys
from pathlib import Path
import torch
from hloc import MODEL_REPO_ID, logger
from hloc.utils.base_model import BaseModel
sys.path.append(str(Path(__file__).parent / "../../third_party"))
from ASpanFormer.src.ASpanFormer.aspanformer import ASpanFormer as _ASpanFormer
from ASpanFormer.src.config.default import get_cfg_defaults
from ASpanFormer.src.utils.misc import lower_config
aspanformer_path = Path(__file__).parent / "../../third_party/ASpanFormer"
class ASpanFormer(BaseModel):
default_conf = {
"model_name": "outdoor.ckpt",
"match_threshold": 0.2,
"sinkhorn_iterations": 20,
"max_keypoints": 2048,
"config_path": aspanformer_path / "configs/aspan/outdoor/aspan_test.py",
}
required_inputs = ["image0", "image1"]
def _init(self, conf):
config = get_cfg_defaults()
config.merge_from_file(conf["config_path"])
_config = lower_config(config)
# update: match threshold
_config["aspan"]["match_coarse"]["thr"] = conf["match_threshold"]
_config["aspan"]["match_coarse"]["skh_iters"] = conf[
"sinkhorn_iterations"
]
self.net = _ASpanFormer(config=_config["aspan"])
model_path = self._download_model(
repo_id=MODEL_REPO_ID,
filename="{}/{}".format(
Path(__file__).stem, self.conf["model_name"]
),
)
state_dict = torch.load(str(model_path), map_location="cpu")[
"state_dict"
]
self.net.load_state_dict(state_dict, strict=False)
logger.info("Loaded Aspanformer model")
def _forward(self, data):
data_ = {
"image0": data["image0"],
"image1": data["image1"],
}
self.net(data_, online_resize=True)
pred = {
"keypoints0": data_["mkpts0_f"],
"keypoints1": data_["mkpts1_f"],
"mconf": data_["mconf"],
}
scores = data_["mconf"]
top_k = self.conf["max_keypoints"]
if top_k is not None and len(scores) > top_k:
keep = torch.argsort(scores, descending=True)[:top_k]
scores = scores[keep]
pred["keypoints0"], pred["keypoints1"], pred["mconf"] = (
pred["keypoints0"][keep],
pred["keypoints1"][keep],
scores,
)
return pred
|