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Sat2Graph Inference Server Docker Container

We containerize Sat2Graph inference server with several models (so far, 7) into one container image.

The inference server supports two inference modes. (1) Given a lat/lon coordinate and the size of the tile, the inference server can automatically download MapBox images and run inference on it. (2) Run on custom input images as long as the ground sampling distance (e.g., 50 cm/pixel) is provided.

We also add code into this container to make it easy to download OpenStreetMap graphs (often used as ground truth). Please check out the detailed instruction below.

Usage

Docker

To install docker (with GPU support), you can check out the instruction here.

Start the Inference Server

Start the server with GPU support.

docker run -p8010:8000 -p8011:8001 --gpus all -it --rm songtaohe/sat2graph_inference_server_gpu:latest

Or just run it on CPUs (the image is smaller).

docker run -p8010:8000 -p8011:8001 -it --rm songtaohe/sat2graph_inference_server_cpu:latest

You can find these two commands in run-cpu.sh and run-gpu.sh.

Inference on MapBox Images

You can use the Python scripts in the scripts folder to start inference tasks.

For example, to run inference on MapBox images, use the following command,

python infer_mapbox_input.py -lat 47.601214 -lon -122.134466 -tile_size 500 -model_id 2 -output out.json

This script uses meter as the unit for the tile_size. The model_id argument determines which model to use. We show the supported models below.

Inference on Custom Images

To run inference on custom images (e.g., sample.png), use the following command,

python infer_custom_input.py -input sample.png -gsd 0.5 -model_id 2 -output out.json

Here, the gsd argument indicates the ground sampling distance or the spatial resolution of the image. The unit is in meter.

Supported Models

Model ID Note
0 Sat2Graph-V1, 80-City US, 1 meter GSD, DLA backbone (2x more channels compared with model-1)
1 Sat2Graph-V1, 20-City US, 1 meter GSD, DLA backbone (the one used in the Sat2Graph paper)
2 Sat2Graph-V2, 20-City US, 50cm GSD (preview, same GTE representation but new backbone)
3 Sat2Graph-V2, 80-City Global, 50cm GSD (preview, same GTE representation but new backbone)
4 UNet segmentation (our implementation), 20-City US, 1 meter GSD
5 DeepRoadMapper segmentation (our implementation), 20-City US, 1 meter GSD
6 JointOrientation segmentation (our implementation), 20-City US, 1 meter GSD

OpenStreetMap Graph

To get OSM graphs, set the osm_only flag to 1 when using infer_mapbox_input.py. For example,

python infer_mapbox_input.py -lat 47.601214 -lon -122.134466 -tile_size 500 -osm_only 1 -output out.json

The out.json will be the road graph from OpenStreetMap.

Inference Result

The inference result (graph) is stored in a json format. It is basically a list of edges. Each edge stores the coordinates of its two vertices. We add a simple script to visualize it (need opencv).

python vis.py tile_size input_json output_image

You can also convert this json format into the pickle format that is compatible with the Sat2Graph evaluation code (topo and apls).

python convert.py input.json output.p

Intermediate Results

The intermediate results (e.g., segmentation mask, the downloaded satellite imagery, etc.) of each inference task are stored at http://localhost:8010/t(task_id)/ (replace the task_id with the task id you get after each run.)