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inference

inference

Run MMSegmentation semantic segmentation and depth inference from configs, checkpoints, or model aliases, with safe visualization, saved masks, device selection, and remote-sensing prerequisites.

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Documentation de la Skill

GitHub

name: inference description: "Run MMSegmentation semantic segmentation and depth inference from configs, checkpoints, or model aliases, with safe visualization, saved masks, device selection, and remote-sensing prerequisites." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Inference

Use this sub-skill when an agent needs to run MMSegmentation inference on images, image batches, videos, or tiled remote-sensing rasters from an existing model. It covers the high-level MMSegInferencer path, the lower-level init_model / inference_model / show_result_pyplot path, and the remote-sensing RSImage / RSInferencer concepts.

Choose the entry point

  • Prefer MMSegInferencer when the user gives a model alias, a config path plus optional weights, wants one-call outputs, or needs saved visualization and predicted masks under an output directory.
  • Prefer init_model + inference_model when the user needs direct access to SegDataSample, custom Python control flow, video-frame loops, or explicit show_result_pyplot rendering.
  • Use RSImage + RSInferencer only for geospatial/tiled remote-sensing images after confirming GDAL/osgeo is installed and the model config/checkpoint are local.
  • For dataset conversion or config authoring, route to ../data-configuration/SKILL.md; for training or evaluation launch, route to ../training-evaluation/SKILL.md; for custom model code, route to ../model-customization/SKILL.md.

Fast starts

Safe import/signature smoke checks

Run these without model downloads or checkpoint loading:

python sub-skills/inference/scripts/mmseg_inference_smoke.py --help
python sub-skills/inference/scripts/mmseg_inference_smoke.py
python sub-skills/inference/scripts/mmseg_inferencer_smoke.py --help
python sub-skills/inference/scripts/mmseg_inferencer_smoke.py

Config/checkpoint image inference

Use local files for deterministic behavior:

python sub-skills/inference/scripts/mmseg_inference_smoke.py \
  --config PATH/TO/MMSEG_CONFIG.py \
  --checkpoint PATH/TO/CHECKPOINT.pth \
  --image PATH/TO/IMAGE.png \
  --device cpu \
  --out-file outputs/demo_overlay.png

The script sets show=False whenever --out-file is provided, so it is safe for headless sessions.

Inferencer one-call outputs

Use a local config and local weights unless the user explicitly allows model-alias downloads:

python sub-skills/inference/scripts/mmseg_inferencer_smoke.py \
  --model PATH/TO/MMSEG_CONFIG.py \
  --weights PATH/TO/CHECKPOINT.pth \
  --image PATH/TO/IMAGE.png \
  --device cpu \
  --out-dir outputs/inferencer

out_dir saves color overlays under vis/ and predicted label-index masks under pred/.

Reference map

  • references/api-reference.md documents callable signatures, return objects, labels/palettes, devices, and saved-output behavior.
  • references/workflows.md gives image, batch, video, headless, and remote-sensing workflows.
  • references/deployment.md summarizes when to switch from PyTorch inference to MMDeploy/runtime inference.
  • references/troubleshooting.md covers missing checkpoints, downloads, headless display, CPU/CUDA, GDAL, MMCV, and dependency compatibility.
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