Tao Generate Anomalies

TAO Generate Anomalies

Standalone install? If this session was not initialized by the TAO skill bank plugin, run the tao-setup skill first (host preflight, credentials, cross-skill discovery).

Multi-phase pipeline (0–7); the mode flag selects which phases run.

Run every phase through to completion without mid-run pauses. Collect all required parameters up front, and run every command from the repo root.

Shell setup. All ${ANOMALYGEN_SCRIPTS} references resolve to the packaged helper-script directory. Inside the container this is preset (ENV ANOMALYGEN_SCRIPTS=<dir>/scripts/utilities); on the host, export it once per shell:

export ANOMALYGEN_SCRIPTS="$(git rev-parse --show-toplevel)/scripts/utilities"

python3 -m scripts.utilities.<name> invocations work from any CWD inside the container (PYTHONPATH is preset) and from the repo root on the host. When inside a product container (ANOMALYGEN_PRODUCT_MODE=1), invoke anomalygen-guard before any GPU work; if it reports BLOCKED, fix the listed issues before continuing.

Quick Start

The pipeline runs inside the metropolis_sdg.paidf_anomalygen container (declared in versions.yaml) or any host with the cosmos-predict2 conda env active. All phase commands assume that environment, at the repo root, with ANOMALYGEN_SCRIPTS exported.

Minimal end-to-end run (mode=full):

# 1. Set the shared variables (see "Shared variables" for the full set).
export ANOMALYGEN_SCRIPTS="$(git rev-parse --show-toplevel)/scripts/utilities"
MODE=full
NAME=my_exp
DATASET_DIR=/data/uc1
DEFECT_DESC=assets/defect_spec_template.jsonl
NUM_SDG=20
MODEL_SIZE=2b

# 2. Phase 0 — verify / download checkpoints (~40 GB for the 2B default; needs HF_TOKEN).
${ANOMALYGEN_SCRIPTS}/check.sh --model-sizes ${MODEL_SIZE^^} \
    || ${ANOMALYGEN_SCRIPTS}/download_checkpoints.sh --model-sizes ${MODEL_SIZE^^}

# 3. Walk Phases 1→7 in order (see each Phase section).

For mode=inference_only (reuse a checkpoint) also set CKPT/STEP and skip Phase 1. For mode=finetune_only run only Phases 0–1.

Running in Docker — container launch, mounts & permissions

The paidf-anomalygen image runs as a non-root baked-in user (USER anomalygen, uid=10000), independent of your host uid. Docker does not remap uids on bind mounts, so a host directory owned by your uid is not writable by uid 10000 and the container fails the instant it tries to create a file there. Run with the full host identity: --user "$(id -u):$(id -g)", matching USER/LOGNAME, HOME=/tmp, and read-only /etc/passwd+/etc/group mounts, plus the cache redirects. Run the fail-fast write preflight before Phase 0. See references/docker.md for the full docker run command, the load-bearing-flag table, the preflight snippet, and the uid-10000 chown/chmod fallback.

Reference files — read before executing phases

Read references/finetune.md before Phase 0/1 and references/inference.md before any of Phases 2–7; for mode=full read both before starting. The remaining references below are on-demand — read when troubleshooting or needing full detail for a specific phase.

Required parameters

num_SDG allocation depends on prep_testcase.sh --mode: inference (default, Phase 2) is uniform across defect types, override per-defect via --per-defect-counts; validation (Phase 1's validation JSONL) is proportional to training mask counts (largest-remainder rounding) and enforces ≥1 per defect. See references/prep-testcase.md for the full mode table.

Conditionally required

Optional parameters

Mode validation (fail fast before any phase)

• mode unset → halt: "mode is required (full | inference_only | finetune_only)."
• mode=inference_only missing either checkpoint_dir or step → halt: "inference_only requires both checkpoint_dir and step."
• mode=full with checkpoint_dir or step supplied → halt: "full mode runs finetune; use mode=inference_only to reuse an existing checkpoint."

Shared variables

Set once before Phase 0:

MODE=<full|inference_only|finetune_only>
NAME=<exp>
DATASET_DIR=<dataset_dir>
CLEAN_DIR=${clean_dir:-${DATASET_DIR}}
CKPT=<checkpoint_dir>      # required iff MODE=inference_only; auto-derived after Phase 1 when MODE=full
STEP=<iter>                # required iff MODE=inference_only; auto-derived after Phase 1 when MODE=full
NUM_SDG=<N>
DEFECT_DESC=<defect_spec.jsonl>
DEFECTS=(T+A T+B)          # TEXTURE+TYPE names. For mode=inference_only, derive from ${CKPT}/ag_config.yaml → dataloader_train.dataset.anomaly_types (also printed by validate_checkpoint.py in Phase 0). For mode=full, take from DEFECT_DESC entries. See references/inference.md §Phase 0.
NUM_SEARCH_RUN=${num_search_run:-3}
NN_THRESHOLD=${nn_threshold:-0.4}
MODEL_SIZE=<2b|14b>
NUM_GPUS=${num_gpus:-1}
MAX_ITER=${max_iter:-75000}
SAVE_ITER=${save_iter:-5000}
VALIDATION_ITER=${validation_iter:-5000}
LR=${lr:-0.02}
BATCH_SIZE=${batch_size:-2}
IMAGE_SIZE=${image_size:-512}
VALIDATION_JSONL=${validation_jsonl:-}  # optional; set by Phase 1 Step 2 if not user-supplied

BASE=results/${NAME}
JSONL=ag_inference/${NAME}/testcase.jsonl
ORIGINAL=${BASE}/original
SEARCHED=${BASE}/searched
ROUNDS=${BASE}/rounds
REGENS=${BASE}/regens

Guard preflight (product mode only)

When ANOMALYGEN_PRODUCT_MODE=1, run .agents/skills/anomalygen-guard/scripts/preflight.py before any GPU work and fix any BLOCKED issues. --validation-jsonl is forwarded only when the user supplied one; for MODE=finetune_only omit --num-sdg if not supplied. See references/guard-and-custom-counts.md for the full preflight command with all forwarded flags and the validation-JSONL / allocate_samples.py 0-entry checks.

Phase 0 — checkpoints

Read references/finetune.md §Phase 0 for HF_TOKEN requirements and what gets downloaded. Both scripts default to the 2B base + t5-large (~40 GB); pass --model-sizes ${MODEL_SIZE^^} so the chain checks and fetches the base size this run actually uses (2b→2B, 14b→14B) — otherwise a 14b run silently passes the 2B-only check and never downloads its checkpoint. Verify first; download only what is missing.

${ANOMALYGEN_SCRIPTS}/check.sh --model-sizes ${MODEL_SIZE^^} \
    || ${ANOMALYGEN_SCRIPTS}/download_checkpoints.sh --model-sizes ${MODEL_SIZE^^}

Phase 1 — fine-tune (skip when MODE=inference_only)

Read references/finetune.md §Phase 1 for dataset structure, config template details, and best-checkpoint selection. Four steps: (1) validate dataset / derive anomaly types, (2) generate the validation JSONL (skip if user supplied VALIDATION_JSONL), (3) generate the training config — show it to the user and confirm before writing — (4) launch training in the background. Then derive CKPT (path encodes upper-case MODEL_SIZE) and STEP (highest nn_score step from validation logs). If MODE=finetune_only, stop after training. See references/finetune-commands.md for the exact Step 1–4 commands and the CKPT/STEP derivation snippet.

Phase 2 — prep-testcase (skip when MODE=finetune_only)

Read references/inference.md §Phase 2 for AMP routing detail and n_seeds sizing. Do NOT pass --seeds — it is auto-computed and is not a recognized flag. prep_testcase.sh defaults to --mode inference (uniform allocation across defect types, no KPI floor), which Phase 2 always uses.

${ANOMALYGEN_SCRIPTS}/prep_testcase.sh \
    --name ${NAME} --num-sdg ${NUM_SDG} \
    --dataset-dir ${DATASET_DIR} \
    --clean-dir ${CLEAN_DIR} \
    --defect-spec ${DEFECT_DESC} \
    --amp-output-dir ag_inference/${NAME}/amp \
    --output-jsonl ${JSONL}

Custom per-defect counts: when the user specifies counts per defect type, translate to --num-sdg plus a --per-defect-counts JSON dict (types absent from the dict get 0; sum should equal --num-sdg, else the script warns on stderr and uses the override sum). Confirm the allocation when intent is ambiguous. See references/guard-and-custom-counts.md for the full --per-defect-counts command example and the ambiguity-handling detail.

Phase 3 — SDG → original/

Read references/inference.md §Phase 3 for JSONL validation against the checkpoint, multi-GPU caveats, and output verification.

python3 -m scripts.utilities.validate_checkpoint ${CKPT} --step ${STEP}
python3 -m scripts.utilities.validate_jsonl ${CKPT} ${JSONL}

${ANOMALYGEN_SCRIPTS}/run_sdg.sh \
    --checkpoint_dir ${CKPT} --step ${STEP} \
    --input_jsonl ${JSONL} --output_dir ${ORIGINAL} \
    --model_size ${MODEL_SIZE} --num_gpus ${NUM_GPUS}

${ANOMALYGEN_SCRIPTS}/verify_output.sh ${JSONL} ${ORIGINAL}

Phase 4 — eval original/

Read references/inference.md §Eval for score interpretation and feature-count explanation. run_eval.sh writes per_sample.csv and eval.log inside original/ and merges nn_score into SDG_result.csv.

${ANOMALYGEN_SCRIPTS}/run_eval.sh \
    --real-path ${DATASET_DIR} --generated-path ${ORIGINAL} \
    --anomaly-types ${DEFECTS[@]}

Phase 5 — per-sample search rounds

Read references/inference.md §Phase 5 for draw strategy, ranges, and re-AMP guidance. For r in 1..NUM_SEARCH_RUN:

1. Read prior round's per_sample.csv (or ${ORIGINAL}/per_sample.csv for r=1).
2. Write ${ROUNDS}/round_${r}/draws.json with selected (guidance, crop_ratio) per sample.
3. Run round via ${ANOMALYGEN_SCRIPTS}/run_round.sh (SDG + eval; the round dir gets its own sdg/{SDG_result.csv, per_sample.csv, eval.log}). See references/inference-commands.md §Phase 5 for the full command and flags.

NUM_SEARCH_RUN=0 is valid — skip this phase entirely and let Phase 6 clone original/ into searched/.

Phase 6 — assemble searched/ (stitch only)

Always run assemble (works with 0 rounds — searched/ clones original/, so downstream always reads searched/ regardless of num_search_run). Stitch-only: copies winning images per sample-index into searched/ and carries over per-sample nn_score / mnn_score from each pick's source-round per_sample.csv. No eval — Phase 7 emits the canonical searched/eval.log.

mkdir -p ${ROUNDS}
python3 -m scripts.utilities.assemble_searched \
    --original-dir ${ORIGINAL} --original-csv ${ORIGINAL}/per_sample.csv \
    --rounds-dir ${ROUNDS} --searched-dir ${SEARCHED}

Phase 7 — filter + regen + eval (default nn_threshold=0.4)

Phase 7 runs by default (nn_threshold=0.4) on every mode=full and mode=inference_only invocation; pass nn_threshold=0 to skip it. It filters searched/ by nn_threshold, regenerates dropped samples via re-AMP (fresh (clean, submask) pairing in the same defect type) for up to 5 attempts, then falls back to best-scoring non-passing regens and finally to dropped originals, so the final bucket always equals num_SDG.

Run python3 -m scripts.utilities.filter_with_regen. Pass --allocation ag_inference/${NAME}/allocation.json so regen targets the intended per-defect counts — without it a bucket left short (e.g. by an interrupted SDG) cannot be topped back up to num_SDG. It runs the final run_eval.sh internally — the only eval against searched/. Read references/inference.md §Phase 7 for regen mechanics, source-column tracing, and the regens/regen_summary.csv schema; see references/inference-commands.md §Phase 7 for the full command and flags.

Output layout

Every bucket that gets eval'd carries the same triad of files: SDG_result.csv (generation params + nn_score + guardrail_pass), per_sample.csv (per-sample nn + mnn), and eval.log (aggregate FID / per-defect avg). Buckets live under results/<name>/ as original/ (Phase 3+4), searched/ (Phase 6 stitch + Phase 7 filter+regen+eval), rounds/round_NN/ (Phase 5, plus search_summary.csv), and regens/regen_NN/ (Phase 7, plus regen_summary.csv).

Image content guardrail. A SigLIP content-safety check runs on every generated image and records its verdict in SDG_result.csv.guardrail_pass (1 safe / 0 blocked). A blocked image is replaced with an all-black image that still occupies its slot on disk, so counting files cannot detect it — never hand a guardrail_pass=0 sample downstream. Blacked-out samples score near-zero nn_score, so Phase 7 regenerates them like any other sub-threshold sample. Disable with ANOMALYGEN_IMAGE_GUARDRAIL=0.

See references/output-layout.md for the full directory tree with per-file annotations, the guardrail semantics, and the post-run Verification checklist (image counts per bucket, search_summary.csv / regen_summary.csv row checks, the per-type nn_score / mnn_score / fid fields in each eval.log, and the guardrail_pass sweep).

Error handling

Common pipeline failure modes (missing mask dirs, short/empty AMP output and the 0 entries written halt, mid-round SDG failure resume, off-boundary step, validate_dataset.py's non-zero exit on any pairing issue, the 2B-only check.sh default, and guardrail-blocked black images) are covered in references/error-handling.md; see also references/finetune.md and references/inference.md for phase-specific error handling.