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name: pytorch-training description: "Configure and inspect face.evoLVe PyTorch training, validation, checkpoints, and model components." disable-model-invocation: true metadata: disco-role: operating license: MIT

face.evoLVe PyTorch Training Router

Use this sub-skill when a task is about face.evoLVe PyTorch training configuration, PyTorch model/backbone/head/loss inspection, legacy PyTorch training-source repair, checkpoint naming/resume behavior, validation during training, or GPU/CPU training expectations.

Do not use this sub-skill for identity-folder creation or low-shot pruning; route those to data-preparation. Route MTCNN alignment and crop generation to face-alignment, post-training checkpoint feature extraction or ROC verification to feature-extraction-verification, and PaddlePaddle training/deployment workflows to paddle-workflows.

Read or run these bundled files

• Read references/training-workflows.md when you need the end-to-end PyTorch config, data-loader, train/validate, checkpoint, tiny-smoke, or skip/full-training decision flow.
• Read references/model-api-reference.md when you need supported ResNet/IR/IR-SE backbones, advanced source backbones, head signatures, loss signatures, and stable-versus-experimental API status.
• Read references/configuration.md before editing config.py values such as BACKBONE_NAME, HEAD_NAME, LOSS_NAME, data/checkpoint/log roots, optimizer schedule, MULTI_GPU, and GPU_ID.
• Read references/troubleshooting.md when PyTorch training or component inspection fails with syntax errors, missing validation data, bcolz/numpy issues, head.metrics import errors, GPU placement problems, checkpoint mismatches, or BatchNorm/tiny-batch issues.
• Run scripts/inspect_pytorch_components.py for a safe parser/import/signature/CPU tensor-shape check of PyTorch components without launching full training.

Operating defaults

• Prefer README-supported training combinations first: IR_SE_50 or IR_50 backbone, ArcFace head, and Focal or softmax cross-entropy loss.
• Treat full PyTorch training as a user-checkout operation that needs datasets, validation arrays, checkpoint/log directories, compatible dependencies, and usually CUDA GPUs. This sub-skill does not provide a bundled full-training launcher.
• Use the bundled inspection script before modifying full training code. It imports backbone/head/loss modules directly and intentionally avoids importing or executing the training entrypoint.
• If a task asks for an executable tiny CPU smoke, keep it to model/head/loss construction and one synthetic forward pass unless the user supplies a deliberately tiny ImageFolder fixture and accepts source repair work.