tao-prepare-anomalygennext-inputs

Prepare AnomalyGenNext Inputs

The first action freezes eligible false-negative identities, isolates each FN mask to its bounding box, selects one deterministic same-type mask, and emits the clean and FN embedding specs. The second action consumes those embedding results, preserves every FN-to-clean pair, and runs native automatic mask placement (AMP) from the AnomalyGenNext container. The final action accepts only pairs for which both mask branches passed AMP, then freezes the exact generation inputs and their hashes.

Input contract

Start from assets/default_filtering.yaml. The gap parquet must contain:

image_id filepath gap_type bbox class split
dataset_id texture_id defect_class anomaly_type fn_mask_source

Identity is explicit: anomaly_type must equal texture_id+defect_class. Preparation intentionally has no filename or directory-layout inference. A caller adapting an unfamiliar dataset must normalize these fields before this boundary.

Each selected dataset maps to an existing fine-tuned checkpoint and recipe. The recipe must declare the exact anomaly type, and defect_spec.jsonl must contain its placement definition. Text-routed definitions require a nonempty roi_prompt_defect_location. The pool layout is:

POOL/TEXTURE/clean_image/*
POOL/TEXTURE/mask/DEFECT/*

Action

Run through the selected platform after the common launch review:

scripts/prepare_anomalygennext_inputs.py \
  --config /path/to/filtering.yaml \
  --output-dir /new/result/root

The output root must not exist. The action emits fn_queries.parquet, selected_fn_queries.parquet, mask_selection.parquet, clean_pool.parquet, two tao-generate-image-embeddings specs, the copied filtering config, and input_contract.json.

Run both emitted specs through tao-generate-image-embeddings, preserving the same encoder. Place their outputs under embeddings/ as named by the specs, then submit the run_amp action:

scripts/run_anomalygennext_amp.py \
  --config /path/to/filtering.yaml \
  --prepared-root /existing/result/root