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import sys
from pathlib import Path
import subprocess
import torch
from ..utils.base_model import BaseModel
from hloc import logger
rord_path = Path(__file__).parent / "../../third_party"
sys.path.append(str(rord_path))
from RoRD.lib.model_test import D2Net as _RoRD
from RoRD.lib.pyramid import process_multiscale
class RoRD(BaseModel):
default_conf = {
"model_name": "rord.pth",
"checkpoint_dir": rord_path / "RoRD" / "models",
"use_relu": True,
"multiscale": False,
"max_keypoints": 1024,
}
required_inputs = ["image"]
weight_urls = {
"rord.pth": "https://drive.google.com/uc?id=12414ZGKwgPAjNTGtNrlB4VV9l7W76B2o&confirm=t",
}
proxy = "http://localhost:1080"
def _init(self, conf):
model_path = conf["checkpoint_dir"] / conf["model_name"]
link = self.weight_urls[conf["model_name"]]
if not model_path.exists():
model_path.parent.mkdir(exist_ok=True)
cmd_wo_proxy = ["gdown", link, "-O", str(model_path)]
cmd = ["gdown", link, "-O", str(model_path), "--proxy", self.proxy]
logger.info(f"Downloading the RoRD model with `{cmd_wo_proxy}`.")
try:
subprocess.run(cmd_wo_proxy, check=True)
except subprocess.CalledProcessError as e:
logger.info(f"Downloading the RoRD model with `{cmd}`.")
try:
subprocess.run(cmd, check=True)
except subprocess.CalledProcessError as e:
logger.error(f"Failed to download the RoRD model.")
raise e
self.net = _RoRD(
model_file=model_path, use_relu=conf["use_relu"], use_cuda=False
)
logger.info(f"Load RoRD model done.")
def _forward(self, data):
image = data["image"]
image = image.flip(1) # RGB -> BGR
norm = image.new_tensor([103.939, 116.779, 123.68])
image = image * 255 - norm.view(1, 3, 1, 1) # caffe normalization
if self.conf["multiscale"]:
keypoints, scores, descriptors = process_multiscale(image, self.net)
else:
keypoints, scores, descriptors = process_multiscale(
image, self.net, scales=[1]
)
keypoints = keypoints[:, [1, 0]] # (x, y) and remove the scale
idxs = scores.argsort()[-self.conf["max_keypoints"] or None :]
keypoints = keypoints[idxs, :2]
descriptors = descriptors[idxs]
scores = scores[idxs]
return {
"keypoints": torch.from_numpy(keypoints)[None],
"scores": torch.from_numpy(scores)[None],
"descriptors": torch.from_numpy(descriptors.T)[None],
}
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