Awesome
System setup
xView2 inference requires a tremendous amount of computing power. Currently, CPU inference is wildly impractical. To that end, unless you have a dedicated workstation with ample GPU power such as an Nvidia DGX station, we recommend a cloud based solution such as AWS or Google Cloud Compute utilizing a GPU optimized instance. Prices vary on instance type and area to be inferred. Example instances:
- AWS EC2
- P4d.24xlarge
- P3.16xlarge
- G Cloud
- Todo!
Installation
Install from source
Note: Only tested on Linux systems.
- Close repository:
git clone https://github.com/fdny-imt/xView2_FDNY.git. - Create Conda environment:
conda create --name xv2 --file spec-file.txt. - Activate conda environment:
conda activate xv2.
Docker
Todo.
Usage
| Argument | Required | Default | Help |
|---|---|---|---|
| --pre_directory | Yes | None | Directory containing pre-disaster imagery. This is searched recursively. |
| --post_directory | Yes | None | Directory containing post-disaster imagery. This is searched recursively. |
| --output_directory | Yes | None | Directory to store output files. This will be created if it does not exist. Existing files may be overwritten. |
| --n_procs | Yes | 8 | Number of processors for multiprocessing |
| --batch_size | Yes | 2 | Number of chips to run inference on at once |
| --num_workers | Yes | 4 | Number of workers loading data into RAM. Recommend 4 * num_gpu |
| --pre_crs | No | None | The Coordinate Reference System (CRS) for the pre-disaster imagery. This will only be utilized if images lack CRS data. |
| --post_crs | No | None | The Coordinate Reference System (CRS) for the post-disaster imagery. This will only be utilized if images lack CRS data. |
| --destination_crs | No | EPSG:4326 | The Coordinate Reference System (CRS) for the output overlays. |
| --output_resolution | No | None | Override minimum resolution calculator. This should be a lower resolution (higher number) than source imagery for decreased inference time. Must be in units of destinationCRS. |
| --dp_mode | No | False | Run models serially, but using DataParallel |
| --save_intermediates | No | False | Store intermediate runfiles |
| --aoi_file | No | None | Shapefile or GeoJSON file of AOI polygons |
Example invocation for damage assessment
On 2 GPUs:
CUDA_VISIBLE_DEVICES=0,1 python handler.py --pre_directory <pre dir> --post_directory <post dir> --output_directory <output dir> --aoi_file <aoi file (GeoJSON or shapefile)> --n_procs <n_proc> --batch_size 2 --num_workers 6
Notes:
- CRS between input types (pre/post/building footprints/AOI) need not match. However CRS within input types must match.
Sources
xView2 1st place solution
- Model weights from 1st place solution for "xView2: Assess Building Damage" challenge. https://github.com/DIUx-xView/xView2_first_place
- More information from original submission see commit: 3fe4a7327f1a19b8c516e0b0930c38c29ac3662b