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name: evaluation-metrics description: "Evaluate StudioGAN image folders with IS/FID/PRDC, cache feature and moment inputs, choose backbones/resizers, and troubleshoot metric backends." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

StudioGAN evaluation metrics

Use this sub-skill when a task asks to evaluate generated or real image folders with StudioGAN metrics, choose an evaluation backbone or post-resizer, reuse precomputed reference features/moments, or diagnose metric input, CUDA, DDP, pretrained-weight, or cache failures.

First decision

• If the user has image folders to compare, use StudioGAN's standalone image-folder evaluator and read the CLI reference.
• If the user is configuring training-time -metrics, iFID, or CAS behavior from python src/main.py, read the metrics overview, then route config/dataset edits to training and configuration and checkpoint analysis execution to sampling and analysis.
• If the user asks about InceptionV3_tf, InceptionV3_torch, ResNet50_torch, SwAV_torch, DINO_torch, Swin-T_torch, legacy, clean, friendly, or cached .npz files, read backbones, resizers, and caches.
• For errors, read troubleshooting before retrying a long metric run.

Safe helper scripts

The bundled scripts do not execute StudioGAN metrics, do not download weights, and do not train. They require explicit paths supplied by the caller.

• Build a dry-run command:

  python scripts/evaluate_image_folders_command.py \
    --repo-root /path/to/PyTorch-StudioGAN \
    --dset1 /path/to/real_imagefolder \
    --dset2 /path/to/generated_imagefolder \
    --metrics fid prdc \
    --gpus 0
  

• Check input combinations and lightweight folder/cache structure:

  python scripts/check_metric_inputs.py \
    --dset1 /path/to/real_imagefolder \
    --dset2 /path/to/generated_imagefolder \
    --metrics fid prdc
  

Non-negotiable routing and safety notes

• Do not claim meaningful metric values from CLI help, config compatibility, tiny fixtures, or command-builder checks. Those only prove wiring and argument compatibility.
• The actual StudioGAN evaluation path is GPU-oriented: it queries torch.cuda.current_device() and moves models/tensors to CUDA devices. Treat CPU-only metric execution as unsupported unless the user has patched the checkout.
• Standalone folder metrics expect ImageFolder-like directories with class subdirectories, even for generated samples.
• --dset1_feats and --dset1_moments are different cache files. FID needs a real/reference folder or moments; PRDC needs a real/reference folder or features.
• Legacy TensorFlow 1.x inception-score code is not part of the current verified PyTorch metric path.