---

name: pytorch-semseg description: "Use pytorch-semseg for PyTorch semantic segmentation model selection, config/data preparation, training and validation planning, single-image inference, and legacy compatibility troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

pytorch-semseg

Use this repo skill when a task involves the ptsemseg package or the historical pytorch-semseg semantic-segmentation workflows: FCN, U-Net, SegNet, PSPNet, ICNet, LinkNet, FRRN, dataset YAML configs, training/validation entry points, single-image mask inference, or legacy dependency errors.

Before acting

1. Confirm the user is working with pytorch-semseg or an equivalent checkout/package that exposes ptsemseg.

2. Read references/repo-provenance.md when deciding whether this skill matches the current repository snapshot or should be refreshed.

3. Read references/compatibility.md before installing dependencies, adapting old scripts, or promising exact legacy reproduction.

4. Run the bundled environment check when imports or backend support matter:

   python scripts/check_environment.py
   python scripts/check_environment.py --smoke
   

The environment check is safe by default: it imports packages, reports torch/CUDA visibility, flags protobuf/SciPy/DenseCRF hazards, and optionally runs a tiny no-download FRRN CPU smoke.

Route map

User task	Read this
Choose a model id, instantiate a model, inspect losses/optimizers/schedulers/augmentations/metrics, or debug registry/API failures.	sub-skills/model-zoo-and-apis/SKILL.md
Write or validate a YAML config, choose dataset loader keys/splits, check data layout, or explain config drift.	sub-skills/data-and-configs/SKILL.md
Plan training or validation commands, reason about checkpoints/logs/metrics, or avoid expensive data-bound runs.	sub-skills/training-and-evaluation/SKILL.md
Build or debug one-image segmentation inference commands, output mask writing, checkpoint filename parsing, or DenseCRF.	sub-skills/single-image-inference/SKILL.md
Diagnose install/import/backend issues that cut across workflows.	references/troubleshooting.md and references/compatibility.md

Install and import baseline

This snapshot exposes ptsemseg but may not behave like a modern packaged project. A future agent should first make ptsemseg importable from the user's selected package or checkout, then verify:

python - <<'PY'
from ptsemseg.models import get_model
from ptsemseg.loader import get_loader
print(get_model)
print(get_loader('pascal'))
PY

If importing ptsemseg.models fails with a generated protobuf descriptor error, read references/compatibility.md before changing package versions. If tensorboardX, pydensecrf, or legacy SciPy image helpers are missing, route to the workflow-specific troubleshooting reference instead of installing broad extras blindly.

Key capabilities

• Model registry guidance for fcn32s, fcn16s, fcn8s, unet, segnet, pspnet, icnet, icnetBN, linknet, frrnA, and frrnB.
• Dataset/config guidance for pascal, camvid, ade20k, mit_sceneparsing_benchmark, cityscapes, nyuv2, sunrgbd, and vistas.
• Static YAML validation that avoids dataset reads and catches unsupported keys, machine-specific paths, legacy l_rate/l_schedule, unsafe img_rows/img_cols: same, Pascal SBD pitfalls, and registry typos.
• Dry-run command builders for training, validation, and single-image inference. These helpers print commands and warnings; they do not run long training, validation, image inference, downloads, or destructive writes.
• Troubleshooting for protobuf/caffe metadata, PyYAML loader behavior, FCN/SegNet pretrained VGG side effects, DenseCRF optional dependency, SciPy image helper removal, checkpoint module. prefixes, and CPU/CUDA expectations.

Safety and backend notes

• CPU checks are sufficient for import, parser, config, registry, metrics, and a small FRRN smoke. CUDA is optional for performance and realistic full-size training/inference; do not use a CPU import check as proof of GPU performance.
• Real training and validation require user-supplied datasets and may write logs/checkpoints. Use the dry-run builders and obtain explicit approval before running expensive commands.
• Real single-image inference requires a trained checkpoint and input image; optional DenseCRF requires pydensecrf.
• FCN and SegNet instantiation through get_model may request pretrained VGG weights. Use FRRN or other no-download checks for routine smoke tests unless weight downloads/cache access are allowed.

Bundled files

• references/repo-provenance.md — source snapshot and refresh baseline.
• references/repo-routing-metadata.json — structured router metadata for managed imports.
• references/compatibility.md — dependency, protobuf, SciPy, CUDA, and packaging constraints.
• references/troubleshooting.md — cross-cutting failure triage.
• scripts/check_environment.py — safe import/backend/API smoke helper.