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name: evaluation-inference description: "Evaluate GeoSeg benchmark tiles and run CUDA inference on UAVid sequences or large remote-sensing images with checkpoint-aware TTA and mask output handling." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

Evaluation and inference

Use this route after selecting a compatible GeoSeg config and checkpoint. Run the bundled preflight before any GPU entry point. For actual execution, use the bundled root wrapper run_geoseg_entrypoint.py with an explicit user-supplied GeoSeg checkout; it preserves the checkout as the process root while keeping the skill itself self-contained. This sub-skill is reference-only for the GPU entry points: the checkout contains no datasets or model checkpoints, and all three routes require CUDA.

Choose one route

• Benchmark tile evaluation: the Vaihingen, Potsdam, or LoveDA tile evaluator entry point. These load config.test_dataset, predict each prepared tile, optionally compute Evaluator metrics, and write PNG masks. See CLI reference and tile workflow.
• UAVid sequence inference: the UAVid sequence entry point. Use when the input is a UAVid-style directory of sequences containing Images/; it writes one Labels/ directory per sequence. See the UAVid workflow.
• Huge-image inference: the huge-image entry point. Use for a flat folder of .tif, .png, or .jpg images. It bottom/right pads each image, predicts non-overlapping tiles in row-major order, stitches them, crops back to the original height and width, and writes one output per input image. See the huge-image workflow.

Do not use a tile evaluator for unsplit huge images, and do not pass a UAVid sequence root to the flat-folder huge-image route.

Required preflight

1. Read the selected config and verify num_classes, model family, dataset roots, and the checkpoint convention os.path.join(config.weights_path, config.test_weights_name + ".ckpt"). Relative config paths are resolved from the process working directory. The usual checkpoint locations are model_weights/<dataset>/...; these files are external and absent from the checkout.
2. Prepare the input layout described in the output/workflow references and use the matching dataset/config. Vaihingen and Potsdam use six classes; LoveDA uses seven; UAVid uses eight.
3. Confirm a CUDA-capable PyTorch runtime. The verified inspection runtime used Python 3.8, torch 2.0.1+cu118, and an NVIDIA A100 (compute capability 8.0). CPU is suitable for static checks and preprocessing only, not truthful execution of these entry points.
4. Choose TTA deliberately. Omit -t for no TTA; use -t lr for the script's horizontal+vertical flip wrapper or -t d4 for its script-specific multiscale/flip composition. Do not pass the literal string None.
5. Decide indexed versus RGB output. Tile evaluators accept --rgb; UAVid sequence inference always emits the UAVid palette, and huge-image inference always maps the selected -d palette. Validate the mapping in output formats before submitting masks.

A safe, dependency-free preflight is the operational first step when its checks match the workflow. Run it from the GeoSeg repository root (or replace paths with absolute paths):

python <path-to-this-skill>/sub-skills/evaluation-inference/scripts/validate_inference_inputs.py \
  --mode huge --image-path /abs/data/vaihingen/test_images \
  --config /abs/GeoSeg/config/vaihingen/dcswin.py \
  --output-path /abs/results/vaihingen/dcswin_huge \
  --dataset pv --check-padding

It does not import GeoSeg, load data, download anything, test CUDA, create output directories, or prove checkpoint tensor compatibility. Treat its warnings as a prompt to inspect the references, not as permission to run without a checkpoint. The exact original entry-point flags and defaults are preserved in CLI reference; execute those flags through the future wrapper or a user-supplied checkout path only after this preflight succeeds.

Sibling routes

• Prepare or repair dataset folders and masks with data-preparation.
• Select a model, class count, and config/checkpoint pairing with model-and-config.
• Resolve dependency, CUDA, data, output, and shape failures with troubleshooting.