3d-resnets-pytorch

# 3D ResNets PyTorch Use this root skill when the user asks about the 3D ResNets PyTorch repository, its CLI flags, its dataset layouts, or its video action-recognition workflows. ## Read first - `references/cli-reference.md` - `references/troubleshooting.md` - `references/repo-provenance.md` - `scripts/check_imports.py` - `scripts/check_main_help.py` ## What this skill covers - Training, validation, inference, and fine-tuning for video action recognition. - Dataset preparation from raw videos into JPEG frame trees, RGB HDF5 files, and annotation JSONs. - Checkpoint handling, model-family selection, and result evaluation. - Common data-layout and runtime compatibility pitfalls. ## Route to a sub-skill ### `training-and-inference` Use this route for: - Fresh training runs, resume flows, and pretrained fine-tuning. - Validation-only runs and sliding-window inference. - Result scoring and DataParallel checkpoint cleanup. - Questions about model families, class counts, `ft_begin_module`, or `resume_path` / `pretrain_path` behavior. Read: - `sub-skills/training-and-inference/SKILL.md` - `sub-skills/training-and-inference/references/workflows.md` - `sub-skills/training-and-inference/references/model-catalog.md` - `sub-skills/training-and-inference/references/troubleshooting.md` - `sub-skills/training-and-inference/scripts/evaluate_results.py` - `sub-skills/training-and-inference/scripts/strip_dataparallel.py` ### `data-preparation` Use this route for: - Extracting JPEG frames or RGB HDF5 files from raw videos. - Building Kinetics, UCF101, HMDB51, MIT, or ActivityNet JSON metadata. - Adding ActivityNet `fps` fields. - Questions about class directories, split files, HDF5 manifests, or `jpg` versus `hdf5` layout. Read: - `sub-skills/data-preparation/SKILL.md` - `sub-skills/data-preparation/references/workflows.md` - `sub-skills/data-preparation/references/data-formats.md` - `sub-skills/data-preparation/references/troubleshooting.md` - `sub-skills/data-preparation/scripts/extract_video_frames.py` - `sub-skills/data-preparation/scripts/extract_video_hdf5.py` - `sub-skills/data-preparation/scripts/build_annotation_json.py` ## Quick runtime helpers - `scripts/check_imports.py` verifies that the core source modules import from a checkout and applies the temporary legacy `Scale` alias when needed. - `scripts/check_main_help.py` prints the full `main.py` CLI help through the same compatibility shim. - `scripts/run_main.py` forwards into the repository CLI after preparing the checkout and compatibility shim. ## Shared environment facts This repo expects a Python environment with PyTorch, torchvision, pandas, h5py, scikit-learn, joblib, and FFmpeg/FFprobe available on PATH. A modern torchvision wheel may not expose `torchvision.transforms.Scale`. If that happens, use the compatibility shim in `scripts/_torchvision_compat.py` or a legacy torchvision release that still ships `Scale`. ## Route selection guidance - If the user already has prepared videos and annotation JSONs, start with `training-and-inference`. - If the user still needs frames, HDF5 files, or split JSONs, start with `data-preparation`. - If the request mentions both, do data preparation first unless the videos and labels are already ready. - If the user only wants command discovery or environment checks, use the root helpers and the sub-skill references rather than reopening the source repo. ## Common handoff sequence 1. Prepare or verify the dataset layout with `data-preparation`. 2. Verify the environment with `scripts/check_imports.py`. 3. Inspect the CLI with `scripts/check_main_help.py`. 4. Run the desired training or inference command with `scripts/run_main.py`. 5. Score or clean outputs with the training-and-inference helpers. ## Don’t do this - Do not point future agents back to the original checkout paths. - Do not use `flow` with JPEG inputs. - Do not assume resume checkpoints can change architecture. - Do not score `--inference_no_average` JSON with the result evaluator before aggregating segment outputs.