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name: edge-connect description: "Route EdgeConnect requests for image inpainting setup, data preparation, training, checkpoint-backed testing, and evaluation." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

EdgeConnect

EdgeConnect is a generative image inpainting repository built around adversarial edge learning. The practical workflow is:

1. prepare images, masks, edges, and file lists,
2. choose a training stage and checkpoints,
3. run checkpoint-backed test-time inference,
4. score the generated outputs.

Use this skill whenever the request names EdgeConnect or refers to the repo's train.py, test.py, config.yml.example, EdgeModel_gen.pth, InpaintingModel_gen.pth, samples/, results/, flist files, masked images, edge maps, or PSNR/SSIM/MAE/FID evaluation on EdgeConnect outputs.

Read first

• references/repo-provenance.md if you need to know whether this skill matches the current checkout.
• references/installation.md for the legacy-compatible environment and the recommended smoke check.
• references/troubleshooting.md for cross-cutting environment, YAML, and checkpoint issues.

Router map

• sub-skills/data-preparation/ — image, mask, and edge layouts; flists; config path validation.
• sub-skills/training/ — stage selection, checkpoint resume, losses, sampling, and internal validation.
• sub-skills/testing/ — checkpoint-backed inference, command construction, and checkpoint layout checks.
• sub-skills/evaluation/ — PSNR/SSIM/MAE/FID scoring and input validation after outputs exist.

How to choose

• Pick data-preparation when the user is asking how to lay out images, masks, or edge maps, or how to build and validate file lists.
• Pick training when the user wants to start, resume, or explain a training run, stage, loss, or checkpoint bundle.
• Pick testing when the user wants to run test.py, validate checkpoint contents, or construct an inference command.
• Pick evaluation when the user already has generated outputs and wants pixel metrics or FID input checks.

Environment notes

• This repo is legacy. Use a compatibility set that keeps scipy.misc.imread, scipy.misc.imresize, and yaml.load(...) working.
• A known-working baseline is Python 3.7 with CUDA-enabled Torch plus the legacy scientific stack listed in references/installation.md.
• A modern PyYAML 6.x install breaks src.config.Config; use a compatible PyYAML version or patch the loader call.
• Do not rely on train.py --help or test.py --help as side-effect-free smoke checks; use scripts/check_env.py instead.

Minimal smoke check

Run the bundled environment checker from a neutral working directory:

python scripts/check_env.py --repo-root <EdgeConnect checkout> --cuda

If you only need CPU importability, omit --cuda. The checker imports the repo modules, instantiates the example config, and performs an optional tiny CUDA allocation when requested.

Key terms

• MODE selects train, test, or eval.
• MODEL selects edge, inpaint, edge-inpaint, or joint behavior.
• MASK=6 is the test-only one-to-one mask mode.
• EDGE=2 means external edge maps are required.
• The checkpoint directory is both the config home and the weight home.

Common launch patterns

• Data prep: build flists, validate config paths, and confirm paired image/mask/edge ordering.
• Training: generate config.yml, confirm the stage and GPU list, then launch the right model family.
• Testing: validate the checkpoint directory first, then run stage-appropriate inference.
• Evaluation: confirm prediction/ground-truth pairings before scoring.

When to read the subskill references

• Read a subskill's references if you need exact command shape, config fields, data layout rules, or failure recovery steps.
• Read references/installation.md before creating an environment or changing dependency versions.
• Read references/troubleshooting.md when import or runtime behavior looks inconsistent with the expected legacy stack.