---

name: training-evaluation description: "Routes 3DDFA training recipes, loss selection, checkpoint resume, dataset layout, and benchmark evaluation." disable-model-invocation: true metadata: disco-role: operating license: MIT

training-evaluation

Use this sub-skill when you need to train 3DDFA, adapt a loss recipe, validate the training data layout, resume from a checkpoint, or interpret AFLW / AFLW2000 benchmark results.

Use this route for

• train.py command setup and the bundled training/*.sh recipes.
• Choosing between WPDC, VDC, and PDC.
• Checking filelists, param files, and the expected cropped-image layout.
• Understanding checkpoint naming and resume behavior.
• Running or interpreting benchmark.py, benchmark_aflw.py, and benchmark_aflw2000.py.

Do not use this route for

• Inference-only or demo flows; use the python-inference route instead.
• Geometry rendering / visualization outputs; use the geometry-rendering route instead.
• ONNX or C++ export; use the cpp-onnx-port route instead.

Read first

• references/training-and-losses.md
• references/data-layout.md
• references/evaluation-benchmarks.md
• references/troubleshooting.md

Skill-owned script

• scripts/validate_training_args.py — safe checker for training command templates, filelists, param paths, and GPU device ids.

Operating notes

• Native training is CUDA-centric: train.py calls torch.cuda.set_device(...) and wraps the model with nn.DataParallel(...).cuda().
• The shipped loss modules import in a CPU-only Python environment, but their forward paths still expect CUDA tensors.
• --resume restores the model state dict only; optimizer state is not bundled in the saved checkpoints.
• The bundled shell recipes are templates. Adjust --root, filelists, param paths, and --devices-id to your local layout before running.
• If you already have predicted params, use the benchmark helper flow described in the benchmark reference rather than re-running inference.

Cross-links

• For training command validation, run the skill-owned script.
• For benchmark data requirements and NME meaning, read the evaluation reference.