executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
modeling-and-inferenceOperate FastReID model registries, safe CPU construction, feature extraction, checkpoint loading, and rank utility imports.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this sub-skill when the task is about FastReID model construction, backbone/head/meta-architecture registries, CPU-safe forward checks, predictor-style feature extraction, checkpoint expectations, or rank/rerank utility imports.
Route elsewhere when the request is primarily about:
../training-and-evaluation/.../data-and-datasets/.../deployment-and-projects/.../setup-and-configuration/.references/model-api.md: use for builder imports, registry names, model family mapping, config keys, tensor contracts, and layer/head/loss vocabulary.references/inference-workflows.md: use for CPU dry-runs, DefaultPredictor-style checkpoint loading, demo-style BGR image preprocessing, feature normalization, and rank/rerank utility usage.references/troubleshooting.md: use to debug missing weights, unwanted ImageNet pretrain downloads, device mismatches, shape errors, rank import mismatches, Cython fallback warnings, and missing OpenCV.scripts/model_forward_smoke.py: run a no-download CPU model construction and random-tensor forward check; accepts an optional config path and explicit repository source path.scripts/feature_extraction_smoke.py: run a no-download feature-extraction preprocessing/model smoke; accepts an optional local image, optional local checkpoint, optional config path, and explicit repository source path.MODEL.DEVICE to cuda; the bundled smoke scripts override it to cpu.MODEL.BACKBONE.PRETRAIN; the bundled smoke scripts override it to False so they do not download backbone weights.MODEL.WEIGHTS; the feature smoke ignores config weights unless --weights is explicitly supplied as a local file.float32 in (B, C, H, W) layout and model outputs are feature tensors in eval mode for standard ReID heads.INPUT.SIZE_TEST, transposes to CHW, adds a batch dimension, and returns L2-normalized features when requested by the caller.