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High-level inference for the JKTSV DINOv3 geolocation model.
Example
-------
from inference import GeoTagPredictor
predictor = GeoTagPredictor("nadh0708/JKTSV-modelD") # HF repo id
# ...or a local checkpoint:
predictor = GeoTagPredictor("model/modelD_40e_2.pth")
result = predictor.predict("street.jpg")
# {'lat': -6.21, 'lon': 106.84}
results = predictor.predict(["a.jpg", "b.jpg"]) # batched
# [{'lat': ..., 'lon': ...}, ...]
The model was trained on Google Street View perspective crops of Jakarta roads
(8 headings, 0-315 deg). Predictions are only meaningful for Jakarta street
imagery.
"""
from __future__ import annotations
from typing import Union
import torch
from PIL import Image
from torchvision import transforms
from modeling_geotag import DinoGeoRegressor
_IMAGENET_MEAN = [0.485, 0.456, 0.406]
_IMAGENET_STD = [0.229, 0.224, 0.225]
ImageInput = Union[str, Image.Image]
class GeoTagPredictor:
def __init__(
self,
model_id_or_path: str,
device: str | torch.device | None = None,
filename: str = "pytorch_model.bin",
):
self.device = torch.device(
device or ("cuda" if torch.cuda.is_available() else "cpu")
)
self.model = DinoGeoRegressor.from_pretrained(
model_id_or_path, filename=filename, device=self.device
)
self.transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=_IMAGENET_MEAN, std=_IMAGENET_STD),
])
def _load(self, image: ImageInput) -> Image.Image:
if isinstance(image, Image.Image):
return image.convert("RGB")
return Image.open(image).convert("RGB")
@torch.no_grad()
def predict(
self, images: Union[ImageInput, list[ImageInput]]
) -> Union[dict, list[dict]]:
"""Predict (lat, lon) for one image or a list of images."""
single = not isinstance(images, (list, tuple))
batch = [images] if single else list(images)
pixel_values = torch.stack([self.transform(self._load(im)) for im in batch])
pixel_values = pixel_values.to(self.device)
lonlat = self.model.predict_lonlat(pixel_values).cpu()
results = [
{"lat": float(lat), "lon": float(lon)}
for lon, lat in lonlat.tolist()
]
return results[0] if single else results
def _cli() -> None:
import argparse
import json
parser = argparse.ArgumentParser(description="Geolocate Jakarta street imagery.")
parser.add_argument("model", help="HF repo id or local checkpoint path")
parser.add_argument("images", nargs="+", help="image file path(s)")
parser.add_argument("--device", default=None)
parser.add_argument(
"--filename", default="pytorch_model.bin",
help="weights filename inside the HF repo",
)
args = parser.parse_args()
predictor = GeoTagPredictor(args.model, device=args.device, filename=args.filename)
out = predictor.predict(args.images)
print(json.dumps(out, indent=2))
if __name__ == "__main__":
_cli()
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