shaoxiongduan
f6275f8005
Adds an in-process evaluation suite covering native (SSIM/PSNR/LPIPS/ optical_flow), audio, physics_iq, vbench (16 sub-metrics), and videoscore2. Public API: create_evaluator/evaluate, BaseMetric + @register, ensure_checkpoint, get_cache_dir. CLI: fastvideo eval list/run. VBench upstream pinned as a git submodule at fastvideo/third_party/eval/vbench (Vchitect/VBench@45e79ec); modern-dep compat is achieved via runtime shims in vbench/__init__.py rather than on-disk patches. CLIP/torch.hub caches are routed through ${FASTVIDEO_EVAL_CACHE}; HF cache stays at the system default. ensure_checkpoint delegates to fastvideo.utils.get_lock + huggingface_hub primitives. Evaluator supports release_cuda_memory(), unload(), reload() for training-time eval that frees GPU between calls. Verified parity against upstream vbench (5/8 metrics bit-exact, others within 1% drift driven by transformers/torch version skew) and against upstream VideoScore2 (regex anchored on the actual model's output format, soft-score formula matches upstream's argmax*max_prob/total). Includes docs/contributing/eval-metrics.md as the porting guide. Out of scope (deferred follow-ups): MIND, VBench-2.0, FVD as a registered metric, training-time EvalCallback.
FastVideo Documentation
This directory contains the FastVideo documentation built with MkDocs.
Build the docs locally
# Install dependencies
pip install -r requirements-mkdocs.txt
# Serve docs with live reload (recommended for development)
mkdocs serve
# Or build static site
mkdocs build
View the docs
Development server (with live reload)
mkdocs serve
Then open your browser to: http://127.0.0.1:8000
Static build
mkdocs build
python -m http.server -d site/
Then open your browser to: http://localhost:8000
Automatic Deployment
Documentation is automatically built and deployed to GitHub Pages when changes are pushed to the main branch via the .github/workflows/infra-docs.yml workflow.