---

name: conversion description: "Plan and safely inspect cross-framework model conversions without loading models, downloading artifacts, or running conversion code." disable-model-invocation: true metadata: disco-role: operating license: MIT

Conversion planning

Use this sub-skill when a task needs a framework direction, checkpoint contract, conversion flag review, or a no-side-effect argument check for this repository's convert_models.py. The source code in this snapshot dispatches only the edges listed in the direction matrix. Framework labels are lower-case and exact: gluon, mxnet, pytorch, chainer, keras, tensorflow (legacy TF1), tf2 (TF2/Keras), and tfl (TFLite destination only).

This is a planning and validation route. It does not load a model, install a backend, download weights, or execute conversion code. Route model construction and checkpoint loading to the model-inference sub-skill, and route backend installation or compatibility diagnosis to the framework-compatibility sub-skill. The conversion script imports cvutil and other backends before it can do useful work, so do not use it as a dry-run parser.

Safe route

1. Read the direction matrix. Treat an absent edge as unsupported by this CLI; do not reverse the flags or compose two conversions implicitly.

2. From this sub-skill directory, inspect the CLI contract without repository imports:

   python3 scripts/inspect_conversion_args.py --list
   

   The bundled inspector is standard-library only. It reports the exact dispatch function for each edge, required identity/checkpoint flags, and the inspector-only policies.

3. Validate a concrete plan before running anything. Supply local source and destination paths when available; this checks required arguments, direction, positive class/channel counts, model type, path collision, and policy gates:

   python3 scripts/inspect_conversion_args.py \
     --src-fwk gluon --dst-fwk tf2 \
     --src-model resnet18 --dst-model resnet18 \
     --src-params ./resnet18.params --dst-params ./resnet18.tf2.h5
   

   Add --check-files only when a filesystem-presence check is wanted. It does not open a checkpoint. --cpu-only, --entrypoint, and --output-dir are inspector policy controls, not flags accepted by convert_models.py.

4. Only after a valid plan, use the exact flags in the matrix. Preserve model identity, class count, input channel count, and checkpoint provenance. The conversion functions assert parameter counts, names, and shapes (with documented special cases); the inspector cannot prove those conditions.

5. Validate the converted model through the model-inference route. Do not use prep_model.py as an argument check: it requires a local Gluon --resume file and sibling train.log, evaluates four publication targets, and writes a _result directory plus metadata.

Hard gates

• tensorflow is the legacy TensorFlow 1.x graph/session path. tf2 is the TensorFlow 2.x/Keras path. Never exchange these labels. tfl is only the destination of the TF2-to-TensorFlow-Lite branch.
• The CLI parser itself does not restrict --model-type, but destination TF2 code uses exactly image for its image branch and treats every other string as the audio branch. The inspector accepts only the documented image or audio values to prevent accidental selection of that fallback.
• --load-ignore-extra and --remove-module are parsed globally. --load-ignore-extra is meaningful for Gluon and PyTorch sources: Gluon passes it to load_parameters(ignore_extra=...), while PyTorch filters extra stored keys. --remove-module is meaningful only for a PyTorch source; its branch is used only when load-ignore-extra is false and handles a DataParallel module. wrapper. Neither flag repairs shape or architecture mismatches.
• A CPU-only gluon -> pytorch plan is blocked/unverified by policy. The focused Gluon/PyTorch tests explicitly use mx.gpu(0) and .cuda(); the tests do not prove CPU behavior. The main CLI initializes use_cuda=False, but that implementation detail is not CPU verification.
• The convert_models.py tf2 -> tfl branch calls TF2 prepare_model with use_pretrained=True and an empty weight path; it does not consume a local --src-params file, and its --dst-model is not used for model construction. For no-network local work, use a separately prepared local-input TF2-to-TFLite wrapper with its --input weight path. That wrapper should write <output-dir>/<model>.tflite only when the directory already exists, then allocate a TFLite interpreter and compare a random-input result. This sub-skill's inspector only validates the direction and does not perform export.
• The local TF2-to-TFLite example declares --input-shape as one int with a tuple default. Its default is (1, 640, 480, 3), but a custom multi-value shape is not supported by this snapshot's parser: one custom integer later fails during slicing and four shell integers are rejected by argparse. Treat custom shape requests as blocked/unverified unless separately wrapped and reviewed.
• Do not run native conversion tests, long training, weight or dataset downloads, or backend installation as part of this planning route.

For exact input/output contracts and failure recovery, use troubleshooting.