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import sys
from pathlib import Path
import torch
from ..utils.base_model import BaseModel
from hloc import logger
alike_path = Path(__file__).parent / "../../third_party/ALIKE"
sys.path.append(str(alike_path))
from alike import ALike as Alike_
from alike import configs
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class Alike(BaseModel):
default_conf = {
"model_name": "alike-t", # 'alike-t', 'alike-s', 'alike-n', 'alike-l'
"use_relu": True,
"multiscale": False,
"max_keypoints": 1000,
"detection_threshold": 0.5,
"top_k": -1,
"sub_pixel": False,
}
required_inputs = ["image"]
def _init(self, conf):
self.net = Alike_(
**configs[conf["model_name"]],
device=device,
top_k=conf["top_k"],
scores_th=conf["detection_threshold"],
n_limit=conf["max_keypoints"],
)
logger.info(f"Load Alike model done.")
def _forward(self, data):
image = data["image"]
image = image.permute(0, 2, 3, 1).squeeze()
image = image.cpu().numpy() * 255.0
pred = self.net(image, sub_pixel=self.conf["sub_pixel"])
keypoints = pred["keypoints"]
descriptors = pred["descriptors"]
scores = pred["scores"]
return {
"keypoints": torch.from_numpy(keypoints)[None],
"scores": torch.from_numpy(scores)[None],
"descriptors": torch.from_numpy(descriptors.T)[None],
}
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