tao-prepare-anomalygennext-inputs v0.1.0A

Immutable. This exact content never changes and is served at /api/v1/blob/84e1fb58613680a6.

---
name: tao-prepare-anomalygennext-inputs
description: >-
  Prepare normalized object-detection false negatives, source masks, and clean-image
  embedding inputs for AnomalyGenNext. Use when turning box-level FN gaps into a
  frozen, pair-preserving preparation plan. Do not use for training or generation.
license: Apache-2.0
compatibility: Requires the AnomalyGenNext 1.1 image, pandas, pyarrow, NumPy, Pillow, and PyYAML.
metadata:
  author: NVIDIA Corporation
  version: "0.1.0"
allowed-tools: Read Bash
tags: [tao, data, anomalygen-next, object-detection, input-preparation]
---

# 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.

## Input contract

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

```text
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:

```text
POOL/TEXTURE/clean_image/*
POOL/TEXTURE/mask/DEFECT/*
```

## Action

Run through the selected platform after the common launch review:

```bash
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:

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

This writes `knn_candidates.parquet`, the native AMP request, and
`amp/testcase.jsonl`. Platform skills own staging and cache placement.

## Gates

- Preserve each box-level FN as a distinct `fn_id`.
- Produce exactly two masks per FN: isolated FN and deterministic same-type.
- Embed a repeated source image once while preserving every FN query row.
- Never infer an anomaly type or placement prompt.
- Refuse output reuse and never mutate a training pool.
- Platform skills own transient staging, caches, and copy-back.

Read `references/input-contract.md` when authoring the normalized parquet or
dataset mapping.