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name: model-zoo-and-apis description: "Select pytorch-semseg model architectures, instantiate safe segmentation models, and inspect API registries." disable-model-invocation: true metadata: disco-role: operating license: MIT

model-zoo-and-apis

Use this sub-skill when the user needs to choose a pytorch-semseg architecture id, instantiate a segmentation model through the package API, inspect registries for losses/optimizers/schedulers/augmentations, or debug model/API import failures.

Route here

• Choose among model ids: fcn32s, fcn16s, fcn8s, unet, segnet, pspnet, icnet, icnetBN, linknet, frrnA, frrnB.
• Build a ptsemseg.models.get_model(model_dict, n_classes, version=None) payload.
• Instantiate a no-download architecture for import/API smoke testing.
• Inspect loss, optimizer, scheduler, augmentation, runningScore, averageMeter, or convert_state_dict APIs.
• Explain FCN/SegNet VGG weight-download side effects, FRRN A/B ambiguity, protobuf/caffe import errors, model shape errors, and DataParallel state-dict prefixes.

Route elsewhere

• Dataset layouts, dataset keys, filesystem paths, and YAML schema validation: use data-and-configs.
• train.py or validate.py command execution, checkpoint training/evaluation, and metric interpretation from full runs: use training-and-evaluation.
• test.py single-image CLI, checkpoint filename parsing for inference, palette decoding, and DenseCRF: use single-image-inference.

Safe workflow

1. Read references/api-reference.md for model ids, verified constructor signatures, registry keys, and safe examples.

2. Run the bundled registry helper from an environment where ptsemseg is importable:

   python scripts/smoke_model_registry.py --list-only
   

3. For a no-download CPU model smoke, prefer explicit FRRN payloads:

   python scripts/smoke_model_registry.py --smoke --model-id frrnA --n-classes 2 --height 64 --width 64
   

4. If the user requests frrnA or frrnB, always include model_type: "A" or model_type: "B" in the model_dict; the registry maps both ids to the same constructor.

5. Do not call get_model for fcn32s, fcn16s, fcn8s, or segnet merely to test imports unless the user accepts the torchvision.models.vgg16(pretrained=True) weight-cache/network side effect.

6. Use references/troubleshooting.md when imports fail, unknown keys are reported, small tensors fail in deep models, or checkpoints contain module. prefixes.

Bundled files

• references/api-reference.md — verified signatures, registry tables, examples, and API caveats.
• references/troubleshooting.md — failure-mode matrix and fixes.
• scripts/smoke_model_registry.py — safe argparse helper for registry listing and optional FRRN CPU smoke.