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import sys
from pathlib import Path
import subprocess
import torch
from ..utils.base_model import BaseModel
lanet_path = Path(__file__).parent / "../../third_party/lanet"
sys.path.append(str(lanet_path))
from network_v0.model import PointModel
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class LANet(BaseModel):
default_conf = {
"model_name": "v0",
"keypoint_threshold": 0.1,
}
required_inputs = ["image"]
def _init(self, conf):
model_path = lanet_path / "checkpoints" / f'PointModel_{conf["model_name"]}.pth'
if not model_path.exists():
print(f"No model found at {model_path}")
self.net = PointModel(is_test=True)
state_dict = torch.load(model_path, map_location="cpu")
self.net.load_state_dict(state_dict["model_state"])
def _forward(self, data):
image = data["image"]
keypoints, scores, descriptors = self.net(image)
_, _, Hc, Wc = descriptors.shape
# Scores & Descriptors
kpts_score = (
torch.cat([keypoints, scores], dim=1).view(3, -1).t().cpu().detach().numpy()
)
descriptors = (
descriptors.view(256, Hc, Wc).view(256, -1).t().cpu().detach().numpy()
)
# Filter based on confidence threshold
descriptors = descriptors[kpts_score[:, 0] > self.conf["keypoint_threshold"], :]
kpts_score = kpts_score[kpts_score[:, 0] > self.conf["keypoint_threshold"], :]
keypoints = kpts_score[:, 1:]
scores = kpts_score[:, 0]
return {
"keypoints": torch.from_numpy(keypoints)[None],
"scores": torch.from_numpy(scores)[None],
"descriptors": torch.from_numpy(descriptors.T)[None],
}
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