Link to google creds https://drive.google.com/file/d/1e6AJ6tSdS1dVmAF4KuOcWVLpn5_opASH/view?usp=drive_link
Predicted road network graph in a large region (2km x 2km).
Predicted road network graphs and corresponding masks in dense urban with complex and irregular structures.
You need the following:
- an Nvidia GPU with latest CUDA and driver.
- the latest pytorch.
- pytorch lightning.
- wandb.
- Go, just for the APLS metric (we should really re-write this with pure python when time allows).
- and pip install whatever is missing.
Download the ViT-B checkpoint from the official SAM directory. Put it under:
-sam_road
--sam_ckpts
---sam_vit_b_01ec64.pth
Refer to the instructions in the RNGDet++ repo (https://github.com/TonyXuQAQ/RNGDetPlusPlus) to download City-scale and SpaceNet datasets.
Put them in the main directory, structure like:
-sam_road
--cityscale
---20cities
--spacenet
---RGB_1.0_meter
Download links copied from https://github.com/TonyXuQAQ/RNGDetPlusPlus
https://drive.google.com/uc?id=1FiZVkEEEVir_iUJpEH5NQunrtlG0Ff1W
The data_split.json is copied from the dataset.json in this folder.
https://drive.google.com/uc?id=1R8sI1RmFe3rUfWMQaOfsYlBDHpQxFH-H
Find the 20cities folder under this folder.
Then, run "python generate_labes.py" under both dirs.
City-scale dataset:
python train.py --config=config/toponet_vitb_512_cityscale.yaml
SpaceNet dataset:
python train.py --config=config/toponet_vitb_256_spacenet.yaml
You can find the checkpoints under lightning_logs dir.
python inferencer.py --config=path_to_the_same_config_for_training --checkpoint=path_to_ckpt
This saves the inference results and visualizations.
Inferencing with our checkpoints:
Cityscale:
python inferencer.py --config=config/toponet_vitb_512_cityscale.yaml --checkpoint=/path_to/cityscale_vitb_512_e10.ckpt
Spacenet:
python inferencer.py --config=config/toponet_vitb_256_spacenet.yaml --checkpoint=/path_to/spacenet_vitb_256_e10.ckpt
Go to cityscale_metrics or spacenet_metrics, and run
bash eval_schedule.bash