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import sys
from pathlib import Path
import subprocess
import torch
from ..utils.base_model import BaseModel
d2net_path = Path(__file__).parent / "../../third_party/d2net"
sys.path.append(str(d2net_path))
from lib.model_test import D2Net as _D2Net
from lib.pyramid import process_multiscale
class D2Net(BaseModel):
default_conf = {
"model_name": "d2_tf.pth",
"checkpoint_dir": d2net_path / "models",
"use_relu": True,
"multiscale": False,
"max_keypoints": 1024,
}
required_inputs = ["image"]
def _init(self, conf):
model_file = conf["checkpoint_dir"] / conf["model_name"]
if not model_file.exists():
model_file.parent.mkdir(exist_ok=True)
cmd = [
"wget",
"https://dsmn.ml/files/d2-net/" + conf["model_name"],
"-O",
str(model_file),
]
subprocess.run(cmd, check=True)
self.net = _D2Net(
model_file=model_file, use_relu=conf["use_relu"], use_cuda=False
)
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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