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# -*- coding: UTF-8 -*-
import sys
from pathlib import Path
import torch
from .. import device, logger
from ..utils.base_model import BaseModel
pram_path = Path(__file__).parent / "../../third_party/pram"
sys.path.append(str(pram_path))
from nets.gml import GML
class IMP(BaseModel):
default_conf = {
"match_threshold": 0.2,
"features": "sfd2",
"model_name": "imp_gml.920.pth",
"sinkhorn_iterations": 20,
}
required_inputs = [
"image0",
"keypoints0",
"scores0",
"descriptors0",
"image1",
"keypoints1",
"scores1",
"descriptors1",
]
def _init(self, conf):
self.conf = {**self.default_conf, **conf}
weight_path = pram_path / "weights" / self.conf["model_name"]
self.net = GML(self.conf).eval().to(device)
self.net.load_state_dict(
torch.load(weight_path, map_location="cpu")["model"], strict=True
)
logger.info("Load IMP model done.")
def _forward(self, data):
data["descriptors0"] = data["descriptors0"].transpose(2, 1).float()
data["descriptors1"] = data["descriptors1"].transpose(2, 1).float()
return self.net.produce_matches(data, p=0.2)
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