fastfill/v2/ops/isambard_analysis.sbatch (runs "all")
+# or one step by hand inside an allocation (Isambard login nodes have no GPU and forbid heavy work), from the checkout:
+# srun --nodes=1 --gpus=4 --time=12:00:00 bash fastfill/v2/ops/run_plan.sh inputs | gpu | cpu | report | all
+# (only report is light enough for a login node).
+# Everything is written under runs/analysis (inputs also: runs/.download-*, runs/baseline-hf, runs/llm-*-300,
+# data/main-20261007b). Every step keeps what a previous run of it finished; resubmitting the job resumes. If the
+# chosen checkpoints (FINAL, CTRL, BASE) differ from the previous run's, the inputs step removes the earlier outputs.
+#
+# inputs (CPU, ~10 min) isambard_inputs.py: refuses unless both arms' STATUS.json phase is done / done-with-failures and
+# their select2 cohorts are the same bytes scored by the same code; final = the arm with the lower select2
+# score, control = the other.
+# Downloads, sha256-pinned: the old baseline (used from a copy whose backbone path is models/Qwen3-8B), the saved
+# LLM answers (prompt, harness; the structured modes only if runs/llm-structured*-300 exist) and the old data
+# main-20261007b; rebuilds the baseline's old selection cohorts (Autopilot.eval_data). Then the 500-row ablation
+# sample (sample_validation.py: the same scenes in new and old validation, eligible for all three checkpoints).
+# gpu one lane per GPU of the job (round robin; GPU 0 is ours here): every forward pass saves its head outputs:
+# final, control and baseline on NEW validation and NEW test (minimal + full; the baseline cannot answer rooms
+# over 128 objects), final on the 300 LLM rows (full); diag_ablation on the sample: final and control (new rows,
+# new train prior), baseline (new rows and old rows, old train prior). Beside, on the CPU: RoomGenBench reference
+# checks of both arms' phase F exports and of the baseline exported again like phase F (not fatal).
+# cpu every head output decoded twice by the same code (spread, argmax); cross-check against phase F's GPU-decoded
+# reports; stratify x24; compare: final vs baseline (<= 128 objects), final vs control, spread vs argmax per
+# model; the saved LLM answers re-scored by the evaluator on disk (no API calls) and llm_compare (final vs LLM).
+# report runs/analysis/final/report.html. Decisions read validation only; the test, LLM, ablation and RoomGenBench
+# sections are report-only.
+# Wall time per step: runs/analysis/walltime.tsv (step, start, end, seconds, exit code, card).
+# DRYRUN=1 (the CPU dry run, isambard_dryrun.sh, only): no downloads, the baseline/data pins are reported instead of
+# enforced, and DEVICE (cpu) and ABLATION_ROWS may be overridden.
+set -euo pipefail
+R=${R:-$PWD}
+cd "$R"
+A=$R/runs/analysis; G=$A/gpu; F=$A/final; CACHE=$A/cache; OPS=$R/fastfill/v2/ops
+mkdir -p $G $F
+export PYTHONPATH=$R PYTHONDONTWRITEBYTECODE=1 TOKENIZERS_PARALLELISM=false OMP_NUM_THREADS=8 \
+ PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True # HF_HOME: the submitting shell's (sbatch exports it)
+P=$R/env/bin/python
+NEW=$R/data/main-20261008a
+OLD=$R/data/main-20261007b
+LLMROWS=$R/runs/llm-prompt-300/rows.jsonl
+GPUS=${CUDA_VISIBLE_DEVICES:-0,1,2,3} # the job's GPUs (Slurm sets CUDA_VISIBLE_DEVICES)
+DEVICE=${DEVICE:-cuda}
+ABL=${ABLATION_ROWS:-500}
+if [ "${DRYRUN:-0}" != 1 ] && { [ "$DEVICE" != cuda ] || [ "$ABL" != 500 ]; }; then
+ echo "refused: DEVICE / ABLATION_ROWS overrides are for the dry run (DRYRUN=1) only"; exit 2
+fi
+SAMPLE=$A/ablation-sample-$ABL
+CPUS=$($P -c "import os; print(len(os.sched_getaffinity(0)) if hasattr(os, 'sched_getaffinity') else os.cpu_count())")
+W=$(( CPUS < 32 ? CPUS : 32 )) # stratify / compare workers and parallel CPU decodes
+
+load_inputs() { # FINAL CTRL BASE (checkpoints), FINAL_NAME CTRL_NAME (runs), *_DIR, COHORT_*, LLM_MODES
+ [ -f $A/inputs.env ] || { echo "refused: run the inputs step first"; exit 1; }
+ . $A/inputs.env
+}
+
+label() { case $1 in base) echo 基线 ;; final) echo 最终 ;; ctrl) echo 对照 ;; esac; }
+
+finished() { # finished DIR: a complete evaluate output. evaluate writes report.json before the outcomes files, so the
+ # mark is every outcomes file holding as many complete lines as its projection's requests
+ $P -c 'import json, sys
+from pathlib import Path
+d = Path(sys.argv[1])
+try:
+ r = json.loads((d / "report.json").read_text())
+ parts = [("outcomes.jsonl", r)] + [(f"outcomes-{p}.jsonl", x) for p, x in r["projections"].items()]
+ ok = all(sum(line.endswith(b"\n") for line in open(d / f, "rb")) == x["requests"] for f, x in parts)
+except (OSError, ValueError, KeyError):
+ ok = False
+sys.exit(0 if ok else 1)' "$1"
+}
+
+# Functions run by timed (or after ||) run with errexit ignored (bash rule): each propagates failures with || return 1.
+timed() { # timed STEP CARD LOG CMD...: runs CMD > LOG, appends its wall time to walltime.tsv, returns its exit code
+ local step=$1 card=$2 log=$3 start t0 rc=0; shift 3; start=$(date -u +%FT%TZ); t0=$(date +%s)
+ "$@" > "$log" 2>&1 || rc=$?
+ printf '%s\t%s\t%s\t%s\t%s\t%s\n' "$step" "$start" "$(date -u +%FT%TZ)" $(( $(date +%s) - t0 )) $rc "$card" >> $A/walltime.tsv
+ echo "$step (card $card): exit $rc after $(( $(date +%s) - t0 )) s"
+ return $rc
+}
+
+parallel() { # parallel N < COMMANDS: each line run by bash, at most N at a time; fails if any fails
+ local n=$1 line fail=0 p pids=()
+ while IFS= read -r line; do
+ bash -c "$line" & pids+=($!)
+ if [ ${#pids[@]} -ge $n ]; then wait ${pids[0]} || fail=1; pids=(${pids[@]+"${pids[@]:1}"}); fi
+ done
+ for p in ${pids[@]+"${pids[@]}"}; do wait $p || fail=1; done
+ return $fail
+}
+
+step_inputs() {
+ local dry= before; [ "${DRYRUN:-0}" != 1 ] || dry=--dry-run
+ before=$(grep -E '^(FINAL|CTRL|BASE)=' $A/inputs.env 2>/dev/null || true)
+ $P $OPS/isambard_inputs.py --repo $R --out $A $dry
+ load_inputs
+ if [ -n "$before" ] && [ "$before" != "$(grep -E '^(FINAL|CTRL|BASE)=' $A/inputs.env)" ]; then
+ echo "the chosen checkpoints changed since the previous run: its outputs are removed"
+ rm -rf $G $F $A/ablation-sample-* $A/reference-final $A/reference-ctrl; mkdir -p $G $F
+ fi
+ if [ ! -f $SAMPLE/sample.json ]; then # drawn on the CPU; eligible for all three checkpoints (min max_objects 128)
+ rm -rf $SAMPLE
+ $P $OPS/sample_validation.py --new $NEW/validation.jsonl --old $OLD/validation.jsonl --checkpoint $FINAL \
+ --checkpoint $CTRL --checkpoint $BASE --rows $ABL --seed 0 --out-dir $SAMPLE
+ fi
+ echo INPUTS-DONE
+}
+
+gpu_step() { # gpu_step STEP CARD LOG CMD...: CMD on GPU CARD (no GPU in the CPU dry run)
+ local step=$1 card=$2 log=$3 visible=$2; shift 3
+ [ $DEVICE = cuda ] || visible=
+ timed $step $card $log env CUDA_VISIBLE_DEVICES=$visible "$@"
+}
+
+heads() { # heads NAME CHECKPOINT DATA CARD PROJECTION...: one forward pass per request, head outputs saved
+ local name=$1 ckpt=$2 data=$3 card=$4; shift 4
+ if finished $G/$name-online; then echo "kept: heads-$name"; return 0; fi
+ rm -rf $G/$name-online $G/$name-heads $G/$name-spread $G/$name-argmax # the decodes of the old head outputs too
+ gpu_step heads-$name $card $G/$name-online.log $P -m fastfill.v2.evaluate --checkpoint $ckpt --data $data \
+ --projection "$@" --device $DEVICE --grid-decode spread --output $G/$name-online --save-head-outputs $G/$name-heads
+}
+
+ablation() { # ablation NAME CHECKPOINT ROWS TRAIN CARD; the sample holds exactly $ABL eligible rows in a seeded order
+ local check="import json,sys; r=json.load(open('$G/$1/report.json')); sys.exit(0 if r['rows'] == $ABL else f'$1 scored {r[\"rows\"]} rows')"
+ if [ ! -f $G/$1/report.json ]; then
+ rm -rf $G/$1
+ gpu_step $1 $5 $G/$1.log $P $OPS/diag_ablation.py --checkpoint $2 --validation $3 --train $4 --rows $ABL \
+ --device $DEVICE --out $G/$1 || return 1
+ fi
+ $P -c "$check"
+}
+
+TASKS=(final-test ctrl-test final-val ctrl-val base-val base-test final-llm abl-ctrl-new abl-final-new abl-base-new abl-base-old)
+task() { # task NAME CARD
+ case $1 in
+ final-test) heads final-test $FINAL $NEW/test.jsonl $2 minimal full ;;
+ ctrl-test) heads ctrl-test $CTRL $NEW/test.jsonl $2 minimal full ;;
+ final-val) heads final-val $FINAL $NEW/validation.jsonl $2 minimal full ;;
+ ctrl-val) heads ctrl-val $CTRL $NEW/validation.jsonl $2 minimal full ;;
+ base-val) heads base-val $BASE $NEW/validation.jsonl $2 minimal full ;;
+ base-test) heads base-test $BASE $NEW/test.jsonl $2 minimal full ;;
+ final-llm) heads final-llm $FINAL $LLMROWS $2 full ;;
+ abl-final-new) ablation ablation-final-new $FINAL $SAMPLE/new.jsonl $NEW/train.jsonl $2 ;; # each checkpoint's own train prior
+ abl-ctrl-new) ablation ablation-ctrl-new $CTRL $SAMPLE/new.jsonl $NEW/train.jsonl $2 ;;
+ abl-base-new) ablation ablation-base-new $BASE $SAMPLE/new.jsonl $OLD/train.jsonl $2 ;;
+ abl-base-old) ablation ablation-base-old $BASE $SAMPLE/old.jsonl $OLD/train.jsonl $2 ;;
+ esac
+}
+
+rgb_export() { # the baseline exported on the CPU like phase F's run_checkpoint.sh; every usable export reference-checked
+ local req key side
+ mkdir -p $A/rgb-base
+ for req in $R/runs/roomgenbench/requests/*.json; do
+ key=$(basename $req .json)
+ if [ ! -f $A/rgb-base/$key.prediction.json ] || [ ! -d $A/rgb-base/$key.handoff ]; then
+ rm -rf $A/rgb-base/$key.prediction.json $A/rgb-base/$key.handoff
+ OMP_NUM_THREADS=48 CUDA_VISIBLE_DEVICES= $P -m fastfill.v2.predict --checkpoint $BASE --request $req --device cpu \
+ --output $A/rgb-base/$key.prediction.json --export-dir $A/rgb-base/$key.handoff || return 1
+ fi
+ for side in $RGB_SIDES base:$A/rgb-base; do
+ mkdir -p $A/reference-${side%%:*}
+ [ -f $A/reference-${side%%:*}/$key.json ] || CUDA_VISIBLE_DEVICES= $P -m fastfill.v2.roomgenbench --reference-check \
+ ${side#*:}/$key.handoff --scene $R/RoomGenBench/bench/inputs/scenes/$key.json --output $A/reference-${side%%:*}/$key.json || return 1
+ done
+ done
+}
+
+step_rgb() { # an arm's phase F export (run_checkpoint.sh) counts only when complete and made by the code on disk
+ local code m ckpt export
+ code=$($P -c "from fastfill.v2.autorun import implementation_sha256; print(implementation_sha256('$R'))")
+ RGB_SIDES=
+ for m in final ctrl; do
+ if [ $m = final ]; then ckpt=$FINAL; export=$R/runs/roomgenbench/best-$FINAL_NAME; else ckpt=$CTRL; export=$R/runs/roomgenbench/best-$CTRL_NAME; fi
+ if [ $(find $export -path '*.layout_boxes/receipt.json' 2>/dev/null | wc -l) -ne 5 ]; then
+ echo "skipped $m: $export lacks 5 receipts"
+ elif [ "$(sed -n 1p $export/checkpoint.txt)" != "$ckpt" ] || [ "$(sed -n 2p $export/checkpoint.txt)" != "$code" ]; then
+ echo "skipped $m: $export holds another checkpoint or was made by other fastfill/v2 code than is on disk now"
+ else
+ RGB_SIDES="$RGB_SIDES $m:$export"
+ fi
+ done
+ timed rgb-export-and-check cpu $A/rgb.log rgb_export
+}
+
+step_gpu() {
+ load_inputs
+ local cards pids=() n k i fail=0
+ IFS=, read -r -a cards <<< "$GPUS"
+ n=${#cards[@]}
+ echo "lanes on $DEVICE: ${cards[*]}"
+ step_rgb > $A/rgb-step.log 2>&1 & local rgb=$!
+ for k in $(seq 0 $((n - 1))); do
+ ( rc=0; for i in "${!TASKS[@]}"; do if [ $((i % n)) = $k ]; then task ${TASKS[i]} ${cards[k]} || rc=1; fi; done; exit $rc ) &
+ pids+=($!)
+ done
+ for i in "${pids[@]}"; do wait $i || fail=1; done # every lane's own exit code
+ wait $rgb || echo "RoomGenBench step failed (report-only, not fatal): see $A/rgb-step.log and $A/rgb.log"
+ [ $fail = 0 ] || { echo "GPU-STEP FAILURE: see $A/walltime.tsv and the logs in $G"; exit 1; }
+ echo GPU-DONE
+}
+
+crosscheck() { # identical layouts expected: phase F's GPU-decoded reports and our online runs vs the CPU decodes
+ local pairs=() m n o
+ for m in final ctrl; do
+ if [ $m = final ]; then n=$FINAL_NAME; else n=$CTRL_NAME; fi
+ pairs+=(--pair "phase F $(label $m) 测试·三字段·spread=$R/runs/test-$n/outcomes.jsonl,$G/$m-test-spread/outcomes.jsonl")
+ pairs+=(--pair "phase F $(label $m) 测试·完整条件·spread=$R/runs/test-$n/outcomes-full.jsonl,$G/$m-test-spread/outcomes-full.jsonl")
+ pairs+=(--pair "phase F $(label $m) 测试·三字段·argmax=$R/runs/test-$n-argmax/outcomes.jsonl,$G/$m-test-argmax/outcomes.jsonl")
+ done
+ pairs+=(--pair "phase F 最终 LLM 行·spread=$R/runs/llmrows-$FINAL_NAME/outcomes.jsonl,$G/final-llm-spread/outcomes.jsonl")
+ pairs+=(--pair "phase F 最终 LLM 行·argmax=$R/runs/llmrows-$FINAL_NAME-argmax/outcomes.jsonl,$G/final-llm-argmax/outcomes.jsonl")
+ for m in base-val final-val ctrl-val base-test final-test ctrl-test final-llm; do for o in outcomes.jsonl outcomes-full.jsonl; do
+ if [ -f $G/$m-online/$o ]; then pairs+=(--pair "本计划在线运行 ${m}（${o}）=$G/$m-online/$o,$G/$m-spread/$o"); fi
+ done; done
+ $P $OPS/crosscheck.py "${pairs[@]}" > $F/crosscheck.md
+}
+
+S() { $P $OPS/stratify.py --workers $W --cache $CACHE "$@" > /dev/null; }
+C() { $P $OPS/compare.py --workers $W --cache $CACHE "$@" > /dev/null; }
+
+stratify_all() {
+ local m s d
+ for m in base final ctrl; do for s in val test; do for d in spread argmax; do
+ S --eval-dir $G/$m-$s-$d --out $F/$m-$s-minimal-$d.json || return 1
+ S --eval-dir $G/$m-$s-$d --outcomes outcomes-full.jsonl --out $F/$m-$s-full-$d.json || return 1
+ done; done; done
+}
+
+compare_all() { # final minus baseline on rooms both can answer (<= 128 objects); final minus control; spread minus argmax
+ local s proj o d sel m
+ for s in val test; do for proj in minimal full; do
+ o=outcomes.jsonl; [ $proj = minimal ] || o=outcomes-full.jsonl
+ for d in spread argmax; do
+ for sel in all "old rule=frozen_prep" "new rule=wall_anchor,support_inside_parent,support_on_added_parent"; do
+ set -- $sel
+ C --a $G/final-$s-$d --a-outcomes $o --b $G/base-$s-$d --b-outcomes $o --label-a 最终 --label-b 基线 --max-objects 128 \
+ ${2:+--where $2} --out $F/compare-$s-$proj-$d-$1.json || return 1
+ C --a $G/final-$s-$d --a-outcomes $o --b $G/ctrl-$s-$d --b-outcomes $o --label-a 最终 --label-b 对照 \
+ ${2:+--where $2} --out $F/control-$s-$proj-$d-$1.json || return 1
+ done
+ done
+ for m in final ctrl base; do
+ C --a $G/$m-$s-spread --a-outcomes $o --b $G/$m-$s-argmax --b-outcomes $o --label-a spread --label-b argmax \
+ --out $F/decode-$s-$m-$proj.json || return 1
+ done
+ done; done
+}
+
+llm_all() { # the saved LLM answers re-scored by the evaluator on disk; FastFill final (CPU decodes; latency from the online run)
+ local methods=() cohorts=() m out
+ mkdir -p $A/llm
+ for m in $LLM_MODES; do
+ out=$A/llm/llm-$m-300-eval
+ if ! finished $out; then
+ rm -rf $out
+ $P -m fastfill.v2.evaluate --data $R/runs/llm-$m-300/rows.jsonl --predictions $R/runs/llm-$m-300/predictions.jsonl \
+ --output $out > $out.log 2>&1 || return 1
+ fi
+ methods+=(--method "LLM $m=$out,$R/runs/llm-$m-300/summary.json")
+ done
+ for m in $COHORT_NEW $COHORT_OLD $COHORT_OLD1; do cohorts+=(--selection-cohort $m); done
+ $P $OPS/llm_compare.py --method "FastFill 最终·spread（防碰撞后处理）=$G/final-llm-spread,$G/final-llm-online" \
+ --method "FastFill 最终·argmax（模型原始输出）=$G/final-llm-argmax,$G/final-llm-online" "${methods[@]}" "${cohorts[@]}" \
+ --out $F/llm-compare.json --workers $W
+}
+
+step_cpu() {
+ load_inputs
+ export OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=
+ local x name data proj d
+ # every saved head output decoded twice by the same code
+ for x in base-val:$NEW/validation.jsonl final-val:$NEW/validation.jsonl ctrl-val:$NEW/validation.jsonl \
+ base-test:$NEW/test.jsonl final-test:$NEW/test.jsonl ctrl-test:$NEW/test.jsonl final-llm:$LLMROWS; do
+ name=${x%%:*}; data=${x#*:}; proj="minimal full"; [ $name != final-llm ] || proj=full
+ for d in spread argmax; do
+ finished $G/$name-$d || echo "rm -rf $G/$name-$d && $P -m fastfill.v2.evaluate --from-head-outputs $G/$name-heads --data $data --projection $proj --grid-decode $d --output $G/$name-$d > $G/$name-$d.log 2>&1"
+ done
+ done > $G/decode-commands.txt
+ timed decode cpu $G/decode.log parallel $(( W < 14 ? W : 14 )) < $G/decode-commands.txt
+ timed crosscheck cpu $F/crosscheck.log crosscheck
+ timed stratify cpu $F/stratify.log stratify_all
+ timed compare cpu $F/compare.log compare_all
+ timed llm-compare cpu $F/llm-compare.log llm_all
+ echo CPU-DONE
+}
+
+pj() { case $1 in minimal) echo 三字段 ;; full) echo 完整条件 ;; esac; }
+sel() { case $1 in all) echo 全部对象 ;; old) echo "旧对象 frozen_prep" ;; new) echo 新对象 ;; esac; }
+
+step_report() {
+ load_inputs
+ local args=() m s p d x side
+ for s in val test; do
+ for m in final ctrl base; do for p in minimal full; do for d in spread argmax; do
+ args+=(--$s-run "$(label $m)·$(pj $p)·$d=$F/$m-$s-$p-$d.json"); done; done; done
+ for p in minimal full; do for d in spread argmax; do for x in all old new; do
+ args+=(--$s-compare "最终 vs 基线·$(pj $p)·${d}·$(sel $x)（≤128 物体）=$F/compare-$s-$p-$d-$x.json")
+ args+=(--$s-control "最终 vs 对照·$(pj $p)·${d}·$(sel $x)=$F/control-$s-$p-$d-$x.json"); done; done
+ for m in final ctrl base; do args+=(--$s-decode "$(label $m)·$(pj $p)：spread vs argmax=$F/decode-$s-$m-$p.json"); done; done
+ done
+ for side in final:"最终（phase F 的 CPU 导出）" ctrl:"对照（phase F 的 CPU 导出）" base:"基线（当前代码 CPU 重新导出）"; do
+ if [ $(ls $A/reference-${side%%:*}/*.json 2>/dev/null | wc -l) -gt 0 ]; then args+=(--reference "${side#*:}=$A/reference-${side%%:*}"); fi
+ done
+ $P $OPS/report.py --out $F/report.html --inputs $A/inputs.json \
+ --title "FastFill v2 评测报告（Isambard）：最终 $FINAL_NAME vs 对照 $CTRL_NAME vs 基线" \
+ --summary "最终臂 $FINAL_DIR=$R/runs/$FINAL_DIR/SUMMARY.md" --summary "对照臂 $CTRL_DIR=$R/runs/$CTRL_DIR/SUMMARY.md" \
+ "${args[@]}" --llm $F/llm-compare.json \
+ --selection-cohort "最终（select2，新验证集全集；对照的相同）=$COHORT_NEW" \
+ --selection-cohort "基线（select2，旧验证集全集）=$COHORT_OLD" --selection-cohort "基线（select1，旧验证集 3000 行）=$COHORT_OLD1" \
+ --ablation "最终·新行（$ABL 行分层样本）=$G/ablation-final-new/report.json" \
+ --ablation "对照·新行（同一样本）=$G/ablation-ctrl-new/report.json" \
+ --ablation "基线·新行（同一样本，相同输入）=$G/ablation-base-new/report.json" \
+ --ablation "基线·旧行（同一批场景）=$G/ablation-base-old/report.json" --ablation-sample $SAMPLE/sample.json \
+ --walltime $A/walltime.tsv --notes $F/crosscheck.md
+}
+
+case "${1:-}" in
+ inputs) step_inputs ;; gpu) step_gpu ;; cpu) step_cpu ;; report) step_report ;;
+ all) step_inputs; step_gpu; step_cpu; step_report ;;
+ *) sed -n '2,31p' "$0"; exit 2 ;;
+esac
diff --git a/code/fastfill/v2/ops/run_reference.sh b/code/fastfill/v2/ops/run_reference.sh
new file mode 100644
index 0000000000000000000000000000000000000000..4022d1a494aa4b9d996701b16e6a021b060efa5d
--- /dev/null
+++ b/code/fastfill/v2/ops/run_reference.sh
@@ -0,0 +1,9 @@
+#!/bin/bash
+set -euo pipefail
+cd /home/jovyan/shanliantian/fastfill
+OUT=runs/roomgenbench/reference-best-main7; mkdir -p $OUT
+for room in bathroom bedroom gym livingroom restaurant; do
+ PYTHONDONTWRITEBYTECODE=1 ./env/bin/python -m fastfill.v2.roomgenbench --reference-check runs/roomgenbench/best-main7-cell05-main-20261007b-e5/$room.handoff \
+ --scene RoomGenBench/bench/inputs/scenes/$room.json --output $OUT/$room.json
+done
+echo REFERENCE-DONE
diff --git a/code/fastfill/v2/ops/run_structured.sh b/code/fastfill/v2/ops/run_structured.sh
new file mode 100644
index 0000000000000000000000000000000000000000..6be6978e01e475438f474c6a3ce1bfbe538cc937
--- /dev/null
+++ b/code/fastfill/v2/ops/run_structured.sh
@@ -0,0 +1,15 @@
+#!/bin/bash
+# Two OptiScene-style LLM agent modes on the frozen 300 rows, then the same scorer as every other method.
+set -uo pipefail
+cd /home/jovyan/shanliantian/fastfill
+export PYTHONDONTWRITEBYTECODE=1 TOKENIZERS_PARALLELISM=false
+ROWS=runs/llm-prompt-300/rows.jsonl; ENV=/home/jovyan/shanliantian/.fastfill_api.env
+for mode in structured structured-harness; do
+ ( ./env/bin/python -m fastfill.v2.llm_structured --data $ROWS --env $ENV --mode $mode --repairs 2 --workers 4 --max-samples 300 \
+ --output runs/llm-$mode-300 > runs/llm-$mode-300.log 2>&1
+ cmp -s runs/llm-$mode-300/rows.jsonl $ROWS && echo "$mode rows identical" || echo "$mode ROWS DIFFER"
+ ./env/bin/python -m fastfill.v2.evaluate --data runs/llm-$mode-300/rows.jsonl --predictions runs/llm-$mode-300/predictions.jsonl \
+ --output runs/llm-$mode-300-eval > runs/llm-$mode-300-eval.log 2>&1 && echo "$mode scored" ) &
+done
+wait
+echo ALL-DONE
diff --git a/code/fastfill/v2/ops/sample_validation.py b/code/fastfill/v2/ops/sample_validation.py
new file mode 100644
index 0000000000000000000000000000000000000000..a343f0e5c08c310e866357a32ca6adb3e2068567
--- /dev/null
+++ b/code/fastfill/v2/ops/sample_validation.py
@@ -0,0 +1,116 @@
+"""Source-stratified random validation sample for diag_ablation.py, the same scenes in two data versions.
+
+ python sample_validation.py --new data/main-20261008a/validation.jsonl --old data/main-20261007b/validation.jsonl \
+ --checkpoint RUN_A/model-step-N --checkpoint RUN_B/model-step-M --rows 500 --seed 0 --out-dir DIR
+
+diag_ablation.py takes the first --rows eligible rows in file order (the file is grouped by source). This writes
+DIR/new.jsonl and DIR/old.jsonl holding the same sampled scenes in the same order, each row eligible exactly as
+diag_ablation checks it for EVERY given checkpoint: none of the runs' validation exclude_flags, a three-field projection
+exists (evaluate.project_minimal), at most max_objects objects, and the projection collates with the run's tokenizer
+and max_length; so ``diag_ablation.py --validation DIR/new.jsonl --rows 500`` scores exactly these rows in this order.
+Allocation: proportional to each source's eligible-scene count (largest remainder, at least one per source); within a
+source, a seeded random order. DIR/sample.json records the allocation, scene ids and file hashes.
+"""
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+import random
+
+from fastfill.v2.batch import collate_samples, load_tokenizer
+from fastfill.v2.data import filter_rows_by_flags
+from fastfill.v2.evaluate import project_minimal
+from fastfill.v2.io import fingerprint, load_checkpoint_config
+
+
+def allocate(sizes, total):
+ """Largest-remainder proportional allocation with a floor of one per non-empty stratum."""
+ keys = sorted(k for k in sizes if sizes[k])
+ if total < len(keys):
+ raise ValueError("fewer rows than sources")
+ base = {k: 1 for k in keys}
+ rest, pool = total - len(keys), sum(sizes[k] - 1 for k in keys)
+ quota = {k: rest * (sizes[k] - 1) / pool if pool else 0 for k in keys}
+ for k in keys:
+ base[k] += int(quota[k])
+ for k in sorted(keys, key=lambda k: (int(quota[k]) - quota[k], k))[:total - sum(base.values())]:
+ base[k] += 1
+ return {k: min(base[k], sizes[k]) for k in keys}
+
+
+def main(argv=None):
+ p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+ p.add_argument("--new", required=True, type=Path)
+ p.add_argument("--old", required=True, type=Path)
+ p.add_argument("--checkpoint", action="append", required=True, type=Path)
+ p.add_argument("--rows", type=int, default=500)
+ p.add_argument("--seed", type=int, default=0)
+ p.add_argument("--out-dir", required=True, type=Path)
+ a = p.parse_args(argv)
+ load = lambda path: [json.loads(line) for line in open(path) if line.strip()]
+ new, old = load(a.new), load(a.old)
+ old_by_scene = {r["provenance"]["scene_id"]: r for r in old}
+ if len(old_by_scene) != len(old) or {r["provenance"]["scene_id"] for r in new} != set(old_by_scene):
+ raise SystemExit("refused: the two files do not hold the same scenes")
+ runs = []
+ for ckpt in a.checkpoint:
+ start = json.loads((ckpt.parent / "run_manifest_start.json").read_text())["resolved_config"]
+ model = json.loads((ckpt / "model_config.json").read_text())
+ runs.append({"checkpoint": str(ckpt), "exclude_flags": start.get("validation", {}).get("exclude_flags", []),
+ "max_objects": model["max_objects"], "max_length": load_checkpoint_config(ckpt)["max_length"] or 4096,
+ "tokenizer": load_tokenizer("tiny" if model["backbone"] == "tiny" else str(ckpt.parent / "tokenizer"),
+ local_files_only=True)})
+ flags = sorted({f for run in runs for f in run["exclude_flags"]})
+ pool = {}
+ for row in filter_rows_by_flags(new, flags):
+ if row["provenance"].get("split") != "validation":
+ raise SystemExit("refused: a non-validation row")
+ twin = old_by_scene[row["provenance"]["scene_id"]]
+ if filter_rows_by_flags([twin], flags) and project_minimal(row) is not None and project_minimal(twin) is not None \
+ and len(row["condition"]["objects"]) <= min(run["max_objects"] for run in runs):
+ pool.setdefault(str(row["provenance"]["source"]), []).append((row, twin))
+ quota = allocate({k: len(v) for k, v in pool.items()}, a.rows)
+ rng, chosen, skipped = random.Random(a.seed), {}, {}
+
+ def collates(row):
+ m = project_minimal(row)
+ try:
+ for run in runs:
+ collate_samples([m], run["tokenizer"], max_length=run["max_length"], max_objects=run["max_objects"])
+ except ValueError:
+ return False
+ return True
+
+ for source in sorted(quota):
+ candidates = pool[source][:]
+ rng.shuffle(candidates)
+ chosen[source] = []
+ for row, twin in candidates:
+ if len(chosen[source]) == quota[source]:
+ break
+ if collates(row) and collates(twin):
+ chosen[source].append((row, twin))
+ else:
+ skipped[source] = skipped.get(source, 0) + 1
+ picked = [pair for source in sorted(chosen) for pair in chosen[source]]
+ random.Random(a.seed + 1).shuffle(picked) # file order mixes sources
+ a.out_dir.mkdir(parents=True, exist_ok=False)
+ for name, index in (("new.jsonl", 0), ("old.jsonl", 1)):
+ with open(a.out_dir / name, "w") as stream:
+ stream.writelines(json.dumps(pair[index]) + "\n" for pair in picked)
+ record = {"rows": len(picked), "seed": a.seed, "new": str(a.new.resolve()), "new_sha256": fingerprint(a.new),
+ "old": str(a.old.resolve()), "old_sha256": fingerprint(a.old), "exclude_flags": flags,
+ "checkpoints": [{k: v for k, v in run.items() if k != "tokenizer"} for run in runs],
+ "eligible_scenes_by_source": {k: len(v) for k, v in sorted(pool.items())}, "allocation": quota,
+ "collate_skipped_by_source": skipped, "chosen_by_source": {k: len(v) for k, v in chosen.items()},
+ "scene_ids": [pair[0]["provenance"]["scene_id"] for pair in picked],
+ "new_sample_sha256": fingerprint(a.out_dir / "new.jsonl"), "old_sample_sha256": fingerprint(a.out_dir / "old.jsonl")}
+ (a.out_dir / "sample.json").write_text(json.dumps(record, indent=1) + "\n")
+ print(json.dumps({k: record[k] for k in ("rows", "allocation", "collate_skipped_by_source", "new_sample_sha256")}, indent=1))
+ assert len(picked) == a.rows, "a source ran out of collatable rows"
+
+
+if __name__ == "__main__":
+ assert allocate({"a": 10, "b": 1, "c": 89}, 10) == {"a": 2, "b": 1, "c": 7}
+ main()
diff --git a/code/fastfill/v2/ops/selftest_shift.py b/code/fastfill/v2/ops/selftest_shift.py
new file mode 100644
index 0000000000000000000000000000000000000000..e277aeeb2361201fe3d71ca06c207a074caf33fc
--- /dev/null
+++ b/code/fastfill/v2/ops/selftest_shift.py
@@ -0,0 +1,55 @@
+"""Self-check of stratify's selection-rule attribution on new-data rows (CPU only).
+
+ python selftest_shift.py --rows runs/analysis/ablation-sample-500/new.jsonl --work runs/analysis/test/shift
+
+Writes the labels as predictions with +0.5 m along x on every target whose selection_rule is not frozen_prep, scores
+them with ``evaluate --predictions`` (full condition) and checks via stratify: report reproduced exactly; position error
+0 on frozen_prep and 0.5 on each added rule; size and yaw error 0 everywhere.
+"""
+import argparse
+import json
+from pathlib import Path
+import subprocess
+import sys
+
+import stratify as S
+
+SHIFT = .5
+
+
+def main():
+ p = argparse.ArgumentParser(description=__doc__)
+ p.add_argument("--rows", required=True, type=Path)
+ p.add_argument("--work", required=True, type=Path)
+ a = p.parse_args()
+ a.work.mkdir(parents=True, exist_ok=False)
+ with open(a.rows) as src, open(a.work / "predictions.jsonl", "w") as out:
+ for line in src:
+ row = json.loads(line)
+ rules = [(e or {}).get("selection_rule") or "frozen_prep" for e in row["provenance"]["field_evidence"]]
+ objects = []
+ for t, rule in zip(row["target"]["objects"], rules):
+ position = [v if v is not None else 0. for v in t["bottom_center_m"]]
+ size = [v if v else 1. for v in t["target_size_local_m"]]
+ objects.append({"id": t["id"], "bottom_center_m": [position[0] + (rule != "frozen_prep") * SHIFT, *position[1:]],
+ "target_size_local_m": size, "yaw_rad": t["yaw_rad"] if t["yaw_rad"] is not None else 0.})
+ out.write(json.dumps({"layout": {"schema_version": "fastfill.v2", "objects": objects}}) + "\n")
+ subprocess.run([sys.executable, "-m", "fastfill.v2.evaluate", "--data", str(a.rows), "--predictions",
+ str(a.work / "predictions.jsonl"), "--output", str(a.work / "eval")], check=True, stdout=subprocess.DEVNULL)
+ meta, rooms, checks = S.collect(a.work / "eval", "outcomes.jsonl", a.rows, 6)
+ strata = S.aggregate(rooms, boot=200)
+ assert checks["reproduces_report_exactly"], checks
+ seen = {}
+ for rule, entry in strata["rule"].items():
+ m = entry["metrics"]
+ seen[rule] = (m["bottom_center_error_m"]["mean"], m["bottom_center_error_m"]["n"])
+ assert abs(m["bottom_center_error_m"]["mean"] - (0. if rule == "frozen_prep" else SHIFT)) < 1e-9, (rule, m)
+ assert m["yaw_error_rad"]["mean"] < 1e-9 and m.get("log_size_error", {"mean": 0.})["mean"] < 1e-9, (rule, m)
+ assert set(seen) == set(S.RULES), seen
+ print(json.dumps({"ok": True, "rooms": len(rooms), "layouts": checks["requests_with_layout"],
+ "position_error_mean_and_objects_by_rule": seen,
+ "places": {k: v["objects"] for k, v in strata["place"].items()}}, indent=1))
+
+
+if __name__ == "__main__":
+ main()
diff --git a/code/fastfill/v2/ops/start_autorun_retrain.sh b/code/fastfill/v2/ops/start_autorun_retrain.sh
new file mode 100644
index 0000000000000000000000000000000000000000..e189408fc09b7c68e8de4ce00b3bd00560b072c0
--- /dev/null
+++ b/code/fastfill/v2/ops/start_autorun_retrain.sh
@@ -0,0 +1,12 @@
+#!/bin/bash
+# Retrain on the round-10 data: adopt the finished baseline run, select its best checkpoint, wait for NEXT_DATA
+# (main-20261008a), then train data fix + max_objects 256 + yaw_cls 0.5. No LLM calls (the env path does not exist).
+cd /home/jovyan/shanliantian/fastfill
+mkdir -p runs/autorun
+nohup ./env/bin/python -m fastfill.v2.autorun \
+ --current main7-cell05-main-20261007b-e5 runs/autorun/main7-cell05-main-20261007b-e5.json 1,2,3,4,5,6,7 \
+ --current-data data/main-20261007b \
+ --llm-env /home/jovyan/shanliantian/.no-llm-for-retrain \
+ --data-wait-h 8 \
+ --set model.max_objects=256 --set loss.yaw_cls=0.5 --name-suffix=-yawcls05 >> runs/autorun/nohup.log 2>&1 &
+echo "autopilot pid $!"
diff --git a/code/fastfill/v2/ops/stratify.py b/code/fastfill/v2/ops/stratify.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5b4be51db4b470e6810413239198bbc19cbe041
--- /dev/null
+++ b/code/fastfill/v2/ops/stratify.py
@@ -0,0 +1,415 @@
+"""Per-object errors of one evaluate output, split by target selection rule, declared place, source family and
+object-count bin, with room-level bootstrap 95% CIs and validator collision / clean-room rates per stratum.
+
+ python stratify.py --eval-dir runs/test-X [--outcomes outcomes.jsonl] --rows data/D/test.jsonl --out S.json
+
+Inputs: an ``evaluate`` output directory (report.json + outcomes*.jsonl) and the rows file it scored (its sha256 must
+equal report.json ``data_sha256``; ``subset_limit`` is honoured). ``outcomes.jsonl`` is the report's first projection,
+``outcomes-.jsonl`` projection p; minimal rows are rebuilt with ``evaluate.project_minimal``. For a
+``--from-head-outputs`` run, meta ``checkpoint`` / ``forward_implementation_sha256`` come from its head-output manifest
+(the saving run), ``implementation_sha256`` is the code that decoded and scored.
+
+Metrics (no new definitions): per object, ``evaluate.reference_metrics``' own loop on its own helpers (``_matched``,
+``_log_size_error``, ``_yaw_error``, ``_box_equivalent_errors``, ``bev_iou``), kept per label; the label-only baselines
+are ``evaluate.baseline_metrics``' loop per object on ``_label_rows`` / ``_estimate`` / ``fit_baselines`` (the run's
+leave-one-out fit). Both are checked per request against the stored outcome (exact float equality) and the pooled
+reference against report.json via ``evaluate._model_summary`` (exact). Validator: ``validation.validate_scene`` (code
+on disk) on the layout and on the labels, as evaluate does; ``evaluate._room_validation`` per room, compared with the
+stored ``target_validation`` (rooms that differ = validator drift since the evaluation).
+
+Attribution: errors and baselines belong to the LABEL object (the legal matching's target); validator flags to the
+PREDICTED object id that a check names. Strata: ``rule`` = target ``provenance.field_evidence[i].selection_rule``
+(zipped with target.objects; missing = frozen_prep, so old data is all frozen_prep); ``place`` = the full row's
+effective support declaration (floor / wall / on_object / undeclared; the minimal projection does not show it to the
+model); ``family`` = provenance.source up to its first "_"; ``objects`` = requested-object count bin.
+Means are object-weighted (rates: per object or per room as named) over rooms with a layout, except
+interface_pass_rate / strict_ok_rate / clean_room_rate_incl_failures (every room). CI: percentile bootstrap over
+rooms (multinomial room weights), ``--boot`` resamples.
+"""
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import multiprocessing as mp
+import os
+from pathlib import Path
+import pickle
+import warnings
+
+os.environ.setdefault("OMP_NUM_THREADS", "1")
+import numpy as np # noqa: E402
+import torch # noqa: E402
+
+from fastfill.v2 import evaluate as E # noqa: E402
+from fastfill.v2.io import fingerprint, read_samples # noqa: E402
+from fastfill.v2.validation import effective_support_requests, validate_scene # noqa: E402
+
+RULES = ("frozen_prep", "wall_anchor", "support_inside_parent", "support_on_added_parent")
+PLACES = ("floor", "wall", "on_object", "undeclared")
+BINS = ((1, 5), (6, 10), (11, 20), (21, 40), (41, 80), (81, 128), (129, 10**9)) # 129+: beyond the baseline's max_objects
+BASELINES = tuple(name for name, _ in E.BASELINE_METRICS)
+OBJECT_KINDS, ROOM_KINDS = ("rule", "place"), ("family", "objects")
+METRICS = (E.REFERENCE_METRICS + tuple(f"baseline:{b}" for b in BASELINES)
+ + ("object_collision_rate", "object_hard_violation_rate", "gt_object_collision_rate",
+ "room_clean_rate", "room_collision_rate", "gt_room_clean_rate", "gt_room_collision_rate",
+ "interface_pass_rate", "strict_ok_rate", "clean_room_rate_incl_failures"))
+COLLISIONS = ("collision", "fixed_collision")
+
+
+def bin_name(n):
+ return next(f"{lo}-{hi}" if hi < 10**9 else f"{lo}+" for lo, hi in BINS if lo <= n <= hi)
+
+
+def object_errors(layout, sample):
+ """reference_metrics' loop kept per label: [(label id, {metric: value or None})] in slot order, plus the
+ per-request summary that reference_metrics would return for the pooled keys."""
+ batch, assignment = E._matched(layout, sample, True)
+ objects = batch["objects"][0]
+ predicted = {o["id"]: o for o in layout["objects"]}
+ labels = {o["id"]: o for o in sample["target"]["objects"]}
+ valid = batch["validity"]
+ records, by_order, box_equivalent = [], {}, 0
+ for i, j in enumerate(assignment[:len(objects)]):
+ p, t = predicted[objects[i]["id"]], labels.get(objects[j]["id"], {})
+ pv, sv, yv = bool(valid["position"][0, j].all()), bool(valid["size"][0, j].all()), bool(valid["yaw"][0, j])
+ e = dict.fromkeys(E.REFERENCE_METRICS)
+ if pv:
+ e["bottom_center_error_m"] = float(np.linalg.norm(np.subtract(p["bottom_center_m"], t["bottom_center_m"])))
+ order, swap = int(batch["yaw_symmetry_order"][0, j]), bool(batch["size_axis_swap_allowed"][0, j])
+ size_error = E._log_size_error(p["target_size_local_m"], t["target_size_local_m"]) if sv else None
+ yaw_error = E._yaw_error(p["yaw_rad"], t["yaw_rad"], 2 if swap else order) if yv else None
+ e["log_size_error_plain_convention"], e["yaw_error_rad_plain_convention"] = size_error, yaw_error
+ if (sv or yv) and swap:
+ size_error, yaw_error = E._box_equivalent_errors(p, t, sv, yv)
+ box_equivalent += 1
+ e["log_size_error"], e["yaw_error_rad"] = size_error, yaw_error
+ if yv:
+ by_order.setdefault(order, []).append(yaw_error)
+ if pv and sv and yv:
+ e["bev_iou"] = float(E.bev_iou(*(torch.tensor(v, dtype=torch.float64) for v in (
+ p["bottom_center_m"], p["target_size_local_m"], p["yaw_rad"],
+ t["bottom_center_m"], t["target_size_local_m"], t["yaw_rad"]))))
+ records.append((objects[j]["id"], e))
+ summary = {**{key: E._stat([e[key] for _, e in records if e[key] is not None]) for key in E.REFERENCE_METRICS},
+ "yaw_error_rad_by_symmetry_order": {str(o): E._stat(v) for o, v in sorted(by_order.items())},
+ "box_equivalent_objects": box_equivalent}
+ return records, summary
+
+
+def object_baselines(sample, fit, exclude_row):
+ """baseline_metrics' loop kept per object: {id: {baseline name: value, pv, sv, yv}} plus the per-request summary."""
+ objects, geometry, origin, scale = E._label_rows(sample)
+ labels = {o["id"]: o for o in sample["target"]["objects"]}
+ center = np.array([origin[0] + scale[0] / 2, origin[1] + scale[1] / 2, origin[2]])
+ own = {fit["keys"][(exclude_row, o["id"])] for o in objects if (exclude_row, o["id"]) in fit["keys"]}
+ out, lists = {}, {name: [] for name in BASELINES}
+ for i, obj in enumerate(objects):
+ target = labels.get(obj["id"], {})
+ b = {"pv": all(geometry["position_valid"][i]), "sv": all(geometry["size_valid"][i]), "yv": bool(geometry["yaw_valid"][i])}
+ if b["pv"]:
+ gt = np.asarray(target["bottom_center_m"], dtype=np.float64)
+ b["room_center_position"] = float(np.linalg.norm(center - gt))
+ estimate, _ = E._estimate(fit, "position", obj["category"], own, np.mean)
+ if estimate is not None:
+ b["category_mean_position"] = float(np.linalg.norm(np.asarray(origin) + np.asarray(scale) * estimate - gt))
+ if b["sv"]:
+ estimate, _ = E._estimate(fit, "size", obj["category"], own, np.median)
+ if estimate is not None:
+ label = target["target_size_local_m"]
+ error = E._log_size_error(estimate, label)
+ if geometry["swap"][i]:
+ error = min(error, E._log_size_error(estimate, (label[1], label[0], label[2])))
+ b["category_median_size"] = error
+ if b["yv"]:
+ b["uniform_yaw"] = E._uniform_yaw_error(geometry["symmetry"][i], geometry["swap"][i],
+ target["target_size_local_m"] if b["sv"] else None)
+ for name in BASELINES:
+ if name in b:
+ lists[name].append(b[name])
+ out[obj["id"]] = b
+ return out, {name: {metric: E._stat(lists[name])} for name, metric in E.BASELINE_METRICS}
+
+
+def _flags(validation, ids):
+ collision, hard = dict.fromkeys(ids, False), dict.fromkeys(ids, False)
+ for c in validation["checks"]:
+ for i in c["object_ids"]:
+ if i in collision and c["status"] == "violation":
+ collision[i] |= c["code"] in COLLISIONS
+ hard[i] |= c["hard"]
+ return collision, hard
+
+
+_STATE = {}
+
+
+def _room(line):
+ """One outcome line -> its room record (worker)."""
+ o = json.loads(line)
+ row, projection, fit, exclude = o["row"], _STATE["projection"], _STATE["fit"], _STATE["exclude_row"]
+ full = _STATE["rows"][row]
+ if o.get("provenance", {}).get("scene_id") != full["provenance"].get("scene_id"):
+ raise ValueError(f"outcome row {row} is scene {o.get('provenance', {}).get('scene_id')}, rows file has another")
+ sample = E.project_minimal(full) if projection == "minimal" else full
+ evidence = full["provenance"].get("field_evidence") or [None] * len(full["target"]["objects"])
+ if len(evidence) != len(full["target"]["objects"]): # zip would silently attribute rules to the wrong objects
+ raise ValueError(f"row {row}: {len(evidence)} field_evidence entries for {len(full['target']['objects'])} target objects")
+ rules = {t["id"]: (e or {}).get("selection_rule") or "frozen_prep" for t, e in zip(full["target"]["objects"], evidence)}
+ parents = {r["id"]: r.get("support_parent") for r in effective_support_requests(full["condition"])}
+ ids = [obj["id"] for obj in sample["condition"]["objects"]]
+ objects = {i: {"rule": rules.get(i, "frozen_prep"),
+ "place": {None: "undeclared", "floor": "floor", "wall": "wall"}.get(parents[i], "on_object"),
+ "err": {}, "base": {}} for i in ids}
+ base, base_summary = object_baselines(sample, fit, None if exclude is None else row)
+ for i, b in base.items():
+ objects[i]["base"] = b
+ gt = validate_scene(sample["condition"], sample["target"]["objects"])
+ gt_collision, gt_hard = _flags(gt, ids)
+ room = {"row": row, "scene_id": full["provenance"].get("scene_id"), "source": str(full["provenance"].get("source")),
+ "n_objects": len(ids), "layout": "error" not in o, "gt_room": E._room_validation(gt),
+ "over_capacity": E.OBJECT_BUDGET_ERROR in o.get("error", {}).get("message", ""),
+ "latency_ms": o.get("fastfill_latency_ms"), "objects": objects}
+ room["family"], room["objects_bin"] = room["source"].split("_")[0], bin_name(len(ids))
+ stored_base = {name: o["baselines"][name] for name in BASELINES} if o.get("baselines") else None
+ check = {"baselines_exact": stored_base == base_summary if stored_base else None, "reference_exact": None,
+ "validation_same": None,
+ "gt_validation_same": (o["ground_truth_validation"] == room["gt_room"]) if o.get("ground_truth_validation") else None}
+ for i in ids:
+ objects[i]["gt_collision"], objects[i]["gt_hard"] = gt_collision[i], gt_hard[i]
+ if room["layout"]:
+ layout = o["raw_prediction"]
+ records, summary = object_errors(layout, sample)
+ for label, e in records:
+ objects[label]["err"] = e
+ validation = validate_scene(sample["condition"], layout["objects"])
+ collision, hard = _flags(validation, ids)
+ for i in ids:
+ objects[i]["collision"], objects[i]["hard"] = collision[i], hard[i]
+ room["model_room"], room["strict_ok"] = E._room_validation(validation), validation["ok"]
+ stored = o["model"]["reference"]
+ check["reference_exact"] = stored is not None and all(stored[k] == summary[k] for k in summary)
+ check["validation_same"] = E._room_validation(o["target_validation"]) == room["model_room"]
+ room["summary"] = summary
+ else:
+ room["model_room"], room["strict_ok"], room["summary"] = None, False, None
+ room["model_flags"] = {k: o["model"].get(k) for k in ("schema_success", "requested_ids_exactly_once",
+ "positive_valid_size", "target_geometry_valid")}
+ room["base_summary"], room["check"] = base_summary, check
+ return room
+
+
+def _init(state):
+ _STATE.update(state)
+ torch.set_num_threads(1)
+
+
+def run_info(eval_dir, outcomes="outcomes.jsonl"):
+ """(report.json, projection of the outcomes file, the report section that summarizes it)."""
+ report = json.loads((Path(eval_dir) / "report.json").read_text())
+ projection = report["projection"] if outcomes == "outcomes.jsonl" else outcomes[len("outcomes-"):-len(".jsonl")]
+ return report, projection, report if outcomes == "outcomes.jsonl" else report["projections"][projection]
+
+
+def collect(eval_dir, outcomes="outcomes.jsonl", rows_path=None, workers=6, cache=None):
+ """Room records of one evaluate output plus the reproduction checks. Refuses rows the run did not score.
+ ``cache`` (a directory): reuse the result of the same outcomes file, rows and code (this script, fastfill/v2)."""
+ eval_dir = Path(eval_dir)
+ report, projection, section = run_info(eval_dir, outcomes)
+ rows_path = Path(rows_path or report["data_path"])
+ sha = fingerprint(rows_path)
+ if sha != report["data_sha256"]:
+ raise SystemExit(f"refused: {rows_path} sha256 {sha[:12]} is not the scored data {report['data_sha256'][:12]}")
+ if cache:
+ code = [fingerprint(p) for p in (Path(__file__), *sorted(Path(E.__file__).parent.glob("*.py")))]
+ key = json.dumps([str(eval_dir.resolve()), outcomes, fingerprint(eval_dir / outcomes), sha, code])
+ cached = Path(cache) / f"{hashlib.sha256(key.encode()).hexdigest()[:32]}.pkl"
+ if cached.is_file():
+ return pickle.loads(cached.read_bytes())
+ result = collect(eval_dir, outcomes, rows_path, workers)
+ cached.parent.mkdir(parents=True, exist_ok=True)
+ cached.with_suffix(".tmp").write_bytes(pickle.dumps(result))
+ cached.with_suffix(".tmp").rename(cached)
+ return result
+ rows = read_samples(rows_path, max_samples=report.get("subset_limit"))
+ fit_source = (section.get("baselines") or {}).get("fit") or "evaluation_set_leave_one_out"
+ if fit_source.startswith("baseline_fit_file:"):
+ with open(fit_source.split(":", 1)[1]) as stream:
+ fit = E.fit_baselines(json.loads(line) for line in stream if line.strip())
+ exclude = None
+ else:
+ fit, exclude = E.fit_baselines(rows), "row"
+ with open(eval_dir / outcomes) as stream:
+ lines = [line for line in stream if line.strip()]
+ state = {"rows": rows, "fit": fit, "exclude_row": exclude, "projection": projection}
+ if workers > 1:
+ _STATE.update(state) # forked workers inherit the rows and the fit
+ with mp.get_context("fork").Pool(workers, initializer=_init, initargs=({},)) as pool:
+ rooms = pool.map(_room, lines, chunksize=8)
+ else:
+ _init(state)
+ rooms = [_room(line) for line in lines]
+ # pooled reference and baselines rebuilt from the per-object values with evaluate's own summarizers
+ pseudo = [{"model": {**r["model_flags"], "reference": r["summary"]}} for r in rooms]
+ summary = E._model_summary(pseudo)["reference"]
+ stored = section["model"]["reference"]
+ baselines = E._baseline_summary([{"baselines": {**r["base_summary"], "category_fallback_objects": 0}} for r in rooms])
+ stored_b = section.get("baselines") or {}
+ checks = {
+ "requests": len(rooms), "requests_with_layout": sum(r["layout"] for r in rooms),
+ "over_capacity_requests": sum(r["over_capacity"] for r in rooms),
+ "request_reference_mismatches": sum(r["check"]["reference_exact"] is False for r in rooms),
+ "request_baseline_mismatches": sum(r["check"]["baselines_exact"] is False for r in rooms),
+ "rooms_validation_differs_from_stored": sum(r["check"]["validation_same"] is False for r in rooms),
+ "rooms_gt_validation_differs_from_stored": sum(r["check"]["gt_validation_same"] is False for r in rooms),
+ "rooms_gt_validation_stored": sum(r["check"]["gt_validation_same"] is not None for r in rooms),
+ "report_reference_exact": {k: summary[k] == stored[k] for k in stored},
+ "report_baselines_exact": {name: baselines[name] == stored_b.get(name) for name in BASELINES},
+ "recomputed_reference": {k: summary[k] for k in E.REFERENCE_METRICS}}
+ checks["reproduces_report_exactly"] = (all(checks["report_reference_exact"].values())
+ and all(checks["report_baselines_exact"].values())
+ and not checks["request_reference_mismatches"] and not checks["request_baseline_mismatches"])
+ manifest = report.get("head_outputs_manifest") or {} # --from-head-outputs: the forward pass ran in the saving run
+ meta = {"eval_dir": str(eval_dir.resolve()), "outcomes": outcomes, "projection": projection, "rows": str(rows_path.resolve()),
+ "data_sha256": sha, "subset_limit": report.get("subset_limit"),
+ **{k: report.get(k) for k in ("grid_decode", "baseline", "code_commit", "implementation_sha256", "head_outputs")},
+ "checkpoint": report.get("checkpoint") or manifest.get("checkpoint"),
+ "forward_implementation_sha256": manifest.get("implementation_sha256") if report.get("head_outputs")
+ else report.get("implementation_sha256") if report.get("checkpoint") else None,
+ "baseline_fit": fit_source, "script_sha256": hashlib.sha256(Path(__file__).read_bytes()).hexdigest()}
+ return meta, rooms, checks
+
+
+def _rate(values):
+ return sum(map(float, values)), len(values)
+
+
+def room_values(room, metric, objs, *, fallback=False):
+ """(sum, count) of one metric over the kept objects ``objs`` of one room, or None if the room does not count.
+ ``fallback`` (llm_compare): a room without a layout scores its label-only baselines (position: room centre,
+ size: category median, yaw: uniform-random expectation, IoU 0; clean room 0) instead of being left out."""
+ if not objs:
+ return None
+ layout = room["layout"]
+ if metric in ("interface_pass_rate", "strict_ok_rate", "clean_room_rate_incl_failures"):
+ value = {"interface_pass_rate": layout, "strict_ok_rate": room["strict_ok"],
+ "clean_room_rate_incl_failures": layout and not room["model_room"]["hard_violation"]}[metric]
+ return float(value), 1
+ if not layout and not fallback:
+ return None
+ if not layout: # fallback
+ if metric == "room_clean_rate":
+ return 0., 1
+ key = {"bottom_center_error_m": "room_center_position", "log_size_error": "category_median_size",
+ "yaw_error_rad": "uniform_yaw"}.get(metric)
+ if key:
+ return _rate([o["base"][key] for o in objs if key in o["base"]])
+ if metric == "bev_iou":
+ return _rate([0. for o in objs if o["base"]["pv"] and o["base"]["sv"] and o["base"]["yv"]])
+ return None
+ if metric in E.REFERENCE_METRICS:
+ return _rate([o["err"][metric] for o in objs if o["err"].get(metric) is not None])
+ if metric.startswith("baseline:"):
+ key = metric.split(":", 1)[1]
+ return _rate([o["base"][key] for o in objs if key in o["base"]])
+ if metric in ("object_collision_rate", "object_hard_violation_rate", "gt_object_collision_rate"):
+ key = {"object_collision_rate": "collision", "object_hard_violation_rate": "hard", "gt_object_collision_rate": "gt_collision"}[metric]
+ return _rate([o[key] for o in objs])
+ validation = room["gt_room"] if metric.startswith("gt_") else room["model_room"]
+ if metric.endswith("clean_rate"):
+ return float(not validation["hard_violation"]), 1
+ return float(validation["collision_pairs"] + validation["fixed_collision_pairs"] > 0), 1 # *_room_collision_rate
+
+
+def bootstrap(num, den, boot, seed, chunk=200):
+ """Point ratios sum(num)/sum(den) per column and ``boot`` resampled ratios (rooms resampled with replacement)."""
+ num, den = np.asarray(num, float), np.asarray(den, float)
+ rooms = len(num)
+ rng = np.random.default_rng(seed)
+ samples = []
+ with np.errstate(invalid="ignore", divide="ignore"):
+ point = num.sum(0) / den.sum(0)
+ for start in range(0, boot, chunk):
+ w = rng.multinomial(rooms, np.full(rooms, 1 / rooms), size=min(chunk, boot - start)).astype(float)
+ samples.append((w @ num) / (w @ den))
+ return point, np.concatenate(samples)
+
+
+def ci(samples):
+ with warnings.catch_warnings(): # a stratum absent from a resample is NaN there; all-NaN columns stay NaN
+ warnings.simplefilter("ignore", RuntimeWarning)
+ return np.nanpercentile(samples, [2.5, 97.5], axis=0)
+
+
+def strata(room):
+ """(kind, value, kept objects) for every stratum the room belongs to."""
+ objs = list(room["objects"].values())
+ yield "all", "all", objs
+ for kind in OBJECT_KINDS:
+ groups = {}
+ for o in objs:
+ groups.setdefault(o[kind], []).append(o)
+ yield from ((kind, value, kept) for value, kept in groups.items())
+ yield "family", room["family"], objs
+ yield "objects", room["objects_bin"], objs
+
+
+def aggregate(rooms, boot=2000, seed=0):
+ columns, cells = {}, [] # (kind, value, metric) -> column index; per room: [(column, num, den)]
+ counts = {}
+ for r, room in enumerate(rooms):
+ for kind, value, objs in strata(room):
+ c = counts.setdefault((kind, value), {"rooms": 0, "objects": 0, "rooms_with_layout": 0})
+ c["rooms"] += 1
+ c["objects"] += len(objs)
+ c["rooms_with_layout"] += room["layout"]
+ for metric in METRICS:
+ value_pair = room_values(room, metric, objs)
+ if value_pair is not None:
+ cells.append((r, columns.setdefault((kind, value, metric), len(columns)), *value_pair))
+ num, den = np.zeros((len(rooms), len(columns))), np.zeros((len(rooms), len(columns)))
+ for r, col, n, d in cells:
+ num[r, col] += n
+ den[r, col] += d
+ point, samples = bootstrap(num, den, boot, seed)
+ lo, hi = ci(samples)
+ order = {"rule": RULES, "place": PLACES, "objects": tuple(bin_name(lo_) for lo_, _ in BINS)}
+ rank = lambda kind, value: (order[kind].index(value) if value in order.get(kind, ()) else len(order.get(kind, ())), value)
+ finite = lambda x: float(x) if np.isfinite(x) else None
+ out = {}
+ for kind, value in sorted(counts, key=lambda kv: (kv[0], *rank(*kv))):
+ entry = out.setdefault(kind, {}).setdefault(value, {**counts[kind, value], "metrics": {}})
+ for metric in METRICS:
+ col = columns.get((kind, value, metric))
+ if col is not None and den[:, col].sum():
+ entry["metrics"][metric] = {"mean": float(point[col]), "ci95": [finite(lo[col]), finite(hi[col])],
+ "n": int(den[:, col].sum())}
+ return out
+
+
+def main(argv=None):
+ p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+ p.add_argument("--eval-dir", required=True, type=Path)
+ p.add_argument("--outcomes", default="outcomes.jsonl", help="outcomes.jsonl (first projection) or outcomes-
.jsonl")
+ p.add_argument("--rows", type=Path, help="the scored rows file (default: report.json data_path)")
+ p.add_argument("--out", required=True, type=Path)
+ p.add_argument("--boot", type=int, default=2000)
+ p.add_argument("--seed", type=int, default=0)
+ p.add_argument("--workers", type=int, default=6)
+ p.add_argument("--cache", type=Path, help="directory of collected room records, reused by compare.py --cache")
+ a = p.parse_args(argv)
+ meta, rooms, checks = collect(a.eval_dir, a.outcomes, a.rows, a.workers, a.cache)
+ result = {"meta": {**meta, "boot": a.boot, "seed": a.seed, "metrics": METRICS,
+ "attribution": "errors/baselines -> label object; validator flags -> predicted object id; "
+ "place = full row's declaration; rule = target field_evidence selection_rule"},
+ "checks": checks, "strata": aggregate(rooms, a.boot, a.seed),
+ "latency_ms": {"observed": sum(r["latency_ms"] is not None for r in rooms),
+ **({"mean": float(np.mean(v)), "p50": float(np.percentile(v, 50)), "p95": float(np.percentile(v, 95))}
+ if (v := [r["latency_ms"] for r in rooms if r["latency_ms"] is not None]) else {})}}
+ a.out.parent.mkdir(parents=True, exist_ok=True)
+ a.out.write_text(json.dumps(result, indent=1, allow_nan=False) + "\n")
+ print(json.dumps({"out": str(a.out), **{k: checks[k] for k in checks if k != "recomputed_reference"}}, indent=1))
+
+
+if __name__ == "__main__":
+ main()
diff --git a/code/fastfill/v2/ops/test300/MANIFEST.json b/code/fastfill/v2/ops/test300/MANIFEST.json
new file mode 100644
index 0000000000000000000000000000000000000000..bbb23d2dea6880e8f90c4638a832fa6f55f19efe
--- /dev/null
+++ b/code/fastfill/v2/ops/test300/MANIFEST.json
@@ -0,0 +1,692 @@
+{
+ "rows": 300,
+ "seed": 0,
+ "max_objects": 256,
+ "over_capacity_excluded": 3,
+ "test_file": "test.jsonl",
+ "test_rows": 8700,
+ "test_sha256": "bfaef332e40226660fd6663e9ad05b3454035e825d22236ed821aae3409e865b",
+ "rows_sha256": "c2a6afb4cb783e59f279367311c153a9a8acffc4dcd275f574d72af2f1458a8c",
+ "per_source": {
+ "ASE": {
+ "test": 65,
+ "chosen": 2
+ },
+ "HSSD200": {
+ "test": 136,
+ "chosen": 5
+ },
+ "IL3D_3dfront": {
+ "test": 862,
+ "chosen": 30
+ },
+ "IL3D_synthetic": {
+ "test": 316,
+ "chosen": 11
+ },
+ "InteriorGS": {
+ "test": 310,
+ "chosen": 11
+ },
+ "InternScenes_3rscan": {
+ "test": 70,
+ "chosen": 2
+ },
+ "InternScenes_arkit": {
+ "test": 229,
+ "chosen": 8
+ },
+ "InternScenes_gen": {
+ "test": 660,
+ "chosen": 23
+ },
+ "InternScenes_mp3d": {
+ "test": 18,
+ "chosen": 1
+ },
+ "InternScenes_scannet": {
+ "test": 104,
+ "chosen": 4
+ },
+ "MansionWorld": {
+ "test": 1313,
+ "chosen": 45
+ },
+ "MultiScan": {
+ "test": 6,
+ "chosen": 0
+ },
+ "OptiScene_holodeck": {
+ "test": 503,
+ "chosen": 17
+ },
+ "SAGE-10k": {
+ "test": 488,
+ "chosen": 17
+ },
+ "Scan2CAD": {
+ "test": 1,
+ "chosen": 0
+ },
+ "SceneSmith": {
+ "test": 364,
+ "chosen": 12
+ },
+ "SpatialGen": {
+ "test": 44,
+ "chosen": 1
+ },
+ "SpatialLM": {
+ "test": 2461,
+ "chosen": 85
+ },
+ "Structured3D": {
+ "test": 747,
+ "chosen": 26
+ }
+ },
+ "test_line_indices": [
+ 49,
+ 53,
+ 75,
+ 131,
+ 169,
+ 190,
+ 196,
+ 277,
+ 299,
+ 303,
+ 305,
+ 344,
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+ ],
+ "scene_ids": [
+ "ase_train_0085853::room_0",
+ "ase_train_0086252::room_0",
+ "HSSD200:103997562_171030642:4",
+ "HSSD200:107733912_175999623:6",
+ "HSSD200:108294846_176710506:7",
+ "HSSD200:108736851_177263586:1",
+ "HSSD200:108736851_177263586:7",
+ "il3d:b6085418-86d4-11f0-a478-60cf84ae2082",
+ "il3d:b66c588c-86d4-11f0-a478-60cf84ae2082",
+ "il3d:b66c7452-86d4-11f0-a478-60cf84ae2082",
+ "il3d:b67afad6-86d4-11f0-a478-60cf84ae2082",
+ "il3d:b6dee8c0-86d4-11f0-a478-60cf84ae2082::KidsRoom-193078",
+ "il3d:b6def220-86d4-11f0-a478-60cf84ae2082",
+ "il3d:b6f3056c-86d4-11f0-a478-60cf84ae2082",
+ "il3d:b77bda2c-86d4-11f0-a478-60cf84ae2082",
+ "il3d:b7d4c768-86d4-11f0-a478-60cf84ae2082",
+ "il3d:b816ed8c-86d4-11f0-a478-60cf84ae2082",
+ "il3d:b8430e80-86d4-11f0-a478-60cf84ae2082",
+ "il3d:b867031c-86d4-11f0-a478-60cf84ae2082",
+ "il3d:b88218d2-86d4-11f0-a478-60cf84ae2082",
+ "il3d:b8ed4b84-86d4-11f0-a478-60cf84ae2082",
+ "il3d:ba42bdac-86d4-11f0-a478-60cf84ae2082",
+ "il3d:ba5b57ae-86d4-11f0-a478-60cf84ae2082",
+ "il3d:ba8e6a54-86d4-11f0-a478-60cf84ae2082::Bedroom-451",
+ "il3d:babfea5c-86d4-11f0-a478-60cf84ae2082",
+ "il3d:baf3365a-86d4-11f0-a478-60cf84ae2082",
+ "il3d:bb2516ac-86d4-11f0-a478-60cf84ae2082::SecondBedroom-4710",
+ "il3d:bb397f7a-86d4-11f0-a478-60cf84ae2082",
+ "il3d:bb5b5ec4-86d4-11f0-a478-60cf84ae2082",
+ "il3d:bc000c44-86d4-11f0-a478-60cf84ae2082",
+ "il3d:bc363008-86d4-11f0-a478-60cf84ae2082::MasterBedroom-5063",
+ "il3d:bca1dc7c-86d4-11f0-a478-60cf84ae2082::LivingRoom-4561",
+ "il3d:bcbf5464-86d4-11f0-a478-60cf84ae2082::OtherRoom-8641",
+ "il3d:bcebfae6-86d4-11f0-a478-60cf84ae2082",
+ "il3d:bd201010-86d4-11f0-a478-60cf84ae2082",
+ "il3d:bd5cd892-86d4-11f0-a478-60cf84ae2082::OtherRoom-3082",
+ "il3d:bd84e56c-86d4-11f0-a478-60cf84ae2082",
+ "il3d:74a0fd70-94ff-11f0-9d02-60cf84ae2082",
+ "il3d:74fddc98-94ff-11f0-9d02-60cf84ae2082",
+ "il3d:751706c8-94ff-11f0-9d02-60cf84ae2082",
+ "il3d:752f201e-94ff-11f0-9d02-60cf84ae2082",
+ "il3d:7541bb48-94ff-11f0-9d02-60cf84ae2082",
+ "il3d:756d9eca-94ff-11f0-9d02-60cf84ae2082",
+ "il3d:75703a2c-94ff-11f0-9d02-60cf84ae2082",
+ "il3d:7594cf18-94ff-11f0-9d02-60cf84ae2082",
+ "il3d:75a66d4a-94ff-11f0-9d02-60cf84ae2082",
+ "il3d:75b19ba2-94ff-11f0-9d02-60cf84ae2082",
+ "il3d:75ce9a2c-94ff-11f0-9d02-60cf84ae2082",
+ "interiorgs:0051_839903::room_0",
+ "interiorgs:0146_840063::room_0",
+ "interiorgs:0283_840775::room_1",
+ "interiorgs:0297_840578::room_0",
+ "interiorgs:0384_840201::room_7",
+ "interiorgs:0471_840870::room_6",
+ "interiorgs:0649_841461::room_1",
+ "interiorgs:0649_841461::room_5",
+ "interiorgs:0735_841587::room_0",
+ "interiorgs:0832_840249::room_4",
+ "interiorgs:0935_841851::room_6",
+ "InternScenes_3rscan::3RScan:3rscan/4138582f-a238-2435-8332-6902542c2823",
+ "InternScenes_3rscan::3RScan:3rscan/422885e0-192d-25fc-844a-62e395291839",
+ "InternScenes_arkit::ARKitScenes:arkitscenes/Training/42444845",
+ "InternScenes_arkit::ARKitScenes:arkitscenes/Training/42444855",
+ "InternScenes_arkit::ARKitScenes:arkitscenes/Training/42897467",
+ "InternScenes_arkit::ARKitScenes:arkitscenes/Training/43828552",
+ "InternScenes_arkit::ARKitScenes:arkitscenes/Training/45261332",
+ "InternScenes_arkit::ARKitScenes:arkitscenes/Training/47331573",
+ "InternScenes_arkit::ARKitScenes:arkitscenes/Training/47895962",
+ "InternScenes_arkit::ARKitScenes:arkitscenes/Validation/45662975",
+ "InternScenes_gen::bathroom/6207",
+ "InternScenes_gen::bathroom/6700",
+ "InternScenes_gen::bathroom/7224",
+ "InternScenes_gen::bathroom/8487",
+ "InternScenes_gen::bathroom/9130",
+ "InternScenes_gen::bedroom/12021",
+ "InternScenes_gen::bedroom/6651",
+ "InternScenes_gen::bedroom/6731",
+ "InternScenes_gen::bedroom/6754",
+ "InternScenes_gen::bedroom/7413",
+ "InternScenes_gen::bedroom/8478",
+ "InternScenes_gen::bedroom/9282",
+ "InternScenes_gen::diningroom/5494",
+ "InternScenes_gen::diningroom/7612",
+ "InternScenes_gen::diningroom/8759",
+ "InternScenes_gen::diningroom/9014",
+ "InternScenes_gen::kitchen/10300",
+ "InternScenes_gen::kitchen/10660",
+ "InternScenes_gen::kitchen/10866",
+ "InternScenes_gen::kitchen/6422",
+ "InternScenes_gen::kitchen/7423",
+ "InternScenes_gen::kitchen/7900",
+ "InternScenes_gen::kitchen/7977",
+ "InternScenes_mp3d::Matterport3D:matterport3d/S9hNv5qa7GM/region14",
+ "InternScenes_scannet::ScanNet:scannet/scene0046_02",
+ "InternScenes_scannet::ScanNet:scannet/scene0102_01",
+ "InternScenes_scannet::ScanNet:scannet/scene0450_00",
+ "InternScenes_scannet::ScanNet:scannet/scene0580_00",
+ "MansionWorld::mansionworld/office_corporate_hq_6f_400_fp005#0/F4_restroom_male",
+ "MansionWorld::mansionworld/office_corporate_hq_6f_400_fp005#1/F4_open_plan_workstations",
+ "MansionWorld::mansionworld/office_corporate_hq_6f_400_fp005#1/F5_huddle_room_2",
+ "MansionWorld::mansionworld/office_corporate_hq_6f_400_fp005#2/F3_restroom_male",
+ "MansionWorld::mansionworld/office_corporate_hq_6f_400_fp005#2/F5_pantry_break_area",
+ "MansionWorld::mansionworld/office_corporate_hq_8f_400_fp002#0/F2_meeting_room_medium_1",
+ "MansionWorld::mansionworld/office_corporate_hq_8f_400_fp002#0/F3_meeting_room_medium_1",
+ "MansionWorld::mansionworld/office_corporate_hq_8f_400_fp002#0/F5_hub_room",
+ "MansionWorld::mansionworld/office_corporate_hq_8f_400_fp002#0/F6_pantry_1",
+ "MansionWorld::mansionworld/office_corporate_hq_8f_400_fp002#1/F4_private_office_2",
+ "MansionWorld::mansionworld/office_corporate_hq_8f_400_fp002#1/F4_private_office_5",
+ "MansionWorld::mansionworld/office_corporate_hq_8f_400_fp002#1/F6_manager_office_2",
+ "MansionWorld::mansionworld/office_corporate_hq_8f_400_fp002#2/F4_private_office_2",
+ "MansionWorld::mansionworld/office_corporate_hq_8f_400_fp002#2/F4_department_meeting_room_1",
+ "MansionWorld::mansionworld/office_corporate_hq_8f_400_fp002#3/F1_reception_desk_area_1",
+ "MansionWorld::mansionworld/office_corporate_hq_8f_400_fp002#3/F6_private_office_5",
+ "MansionWorld::mansionworld/office_lab_studio_office_3f_150_fp005#2/F1_lab_support",
+ "MansionWorld::mansionworld/office_lab_studio_office_3f_150_fp005#2/F3_creative_studio",
+ "MansionWorld::mansionworld/office_lab_studio_office_3f_150_fp005#2/F3_restroom_f3",
+ "MansionWorld::mansionworld/office_open_plan_office_4f_150_fp001#1/F4_pantry_restroom_4",
+ "MansionWorld::mansionworld/office_open_plan_office_4f_150_fp001#2/F4_lounge_collaboration_2",
+ "MansionWorld::mansionworld/office_open_plan_office_4f_200_fp001#1/F1_open_office_space",
+ "MansionWorld::mansionworld/office_open_plan_office_4f_200_fp001#2/F2_meeting_room",
+ "MansionWorld::mansionworld/office_open_plan_office_4f_200_fp001#2/F4_executive_office",
+ "MansionWorld::mansionworld/office_open_plan_office_4f_200_fp001#2/F4_board_room",
+ "MansionWorld::mansionworld/office_open_plan_office_4f_200_fp003#3/F3_open_office_area_2",
+ "MansionWorld::mansionworld/public_healthcare_2f_300_fp002#0/F1_clean_utility_room",
+ "MansionWorld::mansionworld/public_healthcare_2f_300_fp002#0/F2_consultation_room_2",
+ "MansionWorld::mansionworld/public_healthcare_2f_300_fp002#2/F2_consultation_room_3",
+ "MansionWorld::mansionworld/public_healthcare_4f_300_fp001#0/F3_doctor_office_3",
+ "MansionWorld::mansionworld/public_hotel_dormitory_6f_300_fp001#0/F4_standard_room_4",
+ "MansionWorld::mansionworld/public_hotel_dormitory_6f_300_fp001#1/F5_standard_room_2",
+ "MansionWorld::mansionworld/public_hotel_dormitory_6f_300_fp001#1/F6_dorm_room_3",
+ "MansionWorld::mansionworld/public_hotel_dormitory_6f_300_fp001#2/F6_dorm_room_3",
+ "MansionWorld::mansionworld/residential_duplex_townhouse_2f_200_fp001#0/F2_bedroom_3",
+ "MansionWorld::mansionworld/residential_duplex_townhouse_2f_200_fp001#2/F1_kitchen",
+ "MansionWorld::mansionworld/residential_duplex_townhouse_2f_200_fp001#3/F1_entrance_hall",
+ "MansionWorld::mansionworld/residential_duplex_townhouse_3f_200_fp002#0/F2_shared_bathroom",
+ "MansionWorld::mansionworld/residential_duplex_townhouse_3f_200_fp002#1/F1_dining_room",
+ "MansionWorld::mansionworld/residential_duplex_townhouse_3f_200_fp002#3/F2_bedroom_1",
+ "MansionWorld::mansionworld/residential_family_apartment_6f_100_fp001#0/F4_bathroom",
+ "MansionWorld::mansionworld/residential_family_apartment_6f_100_fp001#0/F6_balcony",
+ "MansionWorld::mansionworld/residential_family_apartment_6f_100_fp001#1/F1_bathroom",
+ "MansionWorld::mansionworld/residential_family_apartment_6f_100_fp001#2/F2_secondary_bedroom",
+ "MansionWorld::mansionworld/residential_studio_small_flat_2f_60_fp005#0/F2_bathroom",
+ "optiscene:HOLODECK-bathroom-122",
+ "optiscene:HOLODECK-bathroom-1646",
+ "optiscene:HOLODECK-bathroom-1981",
+ "optiscene:HOLODECK-bathroom-1994",
+ "optiscene:HOLODECK-bathroom-369",
+ "optiscene:HOLODECK-kitchen-1493",
+ "optiscene:HOLODECK-kitchen-1772",
+ "optiscene:HOLODECK-kitchen-180",
+ "optiscene:HOLODECK-kitchen-2100",
+ "optiscene:HOLODECK-kitchen-2163",
+ "optiscene:HOLODECK-kitchen-2435",
+ "optiscene:HOLODECK-kitchen-2441",
+ "optiscene:HOLODECK-kitchen-436",
+ "optiscene:HOLODECK-living_room-4944",
+ "optiscene:HOLODECK-living_room-5025",
+ "optiscene:HOLODECK-living_room-5846",
+ "optiscene:HOLODECK-living_room-6117",
+ "SAGE-10k::layout_f10752d3::0",
+ "SAGE-10k::layout_3ed54326::0",
+ "SAGE-10k::layout_1f08bd77::0",
+ "SAGE-10k::layout_cdfd1ecd::0",
+ "SAGE-10k::layout_b72ccd9a::0",
+ "SAGE-10k::layout_7b86c101::0",
+ "SAGE-10k::layout_00717fe8::0",
+ "SAGE-10k::layout_bfa239a6::0",
+ "SAGE-10k::layout_de6b6260::0",
+ "SAGE-10k::layout_1d0b44f5::0",
+ "SAGE-10k::layout_ba01b7cb::0",
+ "SAGE-10k::layout_f969f32a::0",
+ "SAGE-10k::layout_172543fb::0",
+ "SAGE-10k::layout_24dbc5c4::0",
+ "SAGE-10k::layout_1be46289::0",
+ "SAGE-10k::layout_a8b2d64d::0",
+ "SAGE-10k::layout_6d8c4e7f::0",
+ "SceneSmith::scene_011",
+ "SceneSmith::scene_035",
+ "SceneSmith::scene_036",
+ "SceneSmith::scene_054",
+ "SceneSmith::scene_119",
+ "SceneSmith::scene_139",
+ "SceneSmith::scene_160",
+ "SceneSmith::scene_190::treatment_room",
+ "SceneSmith::scene_201::bathroom",
+ "SceneSmith::scene_206::bathroom_2",
+ "SceneSmith::scene_207::office5",
+ "SceneSmith::scene_208::activity_room_pingpong",
+ "spatialgen_scene_00023::room_0",
+ "spatiallm:scene_000044::room_0",
+ "spatiallm:scene_000078::room_3",
+ "spatiallm:scene_000467::room_1",
+ "spatiallm:scene_000476::room_2",
+ "spatiallm:scene_001097::room_7",
+ "spatiallm:scene_001277::room_1",
+ "spatiallm:scene_001300::room_0",
+ "spatiallm:scene_001374::room_1",
+ "spatiallm:scene_001408::room_1",
+ "spatiallm:scene_001618::room_4",
+ "spatiallm:scene_001782::room_0",
+ "spatiallm:scene_001810::room_6",
+ "spatiallm:scene_002069::room_4",
+ "spatiallm:scene_002091::room_3",
+ "spatiallm:scene_002414::room_0",
+ "spatiallm:scene_002680::room_0",
+ "spatiallm:scene_003001::room_2",
+ "spatiallm:scene_003030::room_1",
+ "spatiallm:scene_003361::room_0",
+ "spatiallm:scene_003531::room_4",
+ "spatiallm:scene_003531::room_7",
+ "spatiallm:scene_003581::room_2",
+ "spatiallm:scene_003608::room_2",
+ "spatiallm:scene_003709::room_0",
+ "spatiallm:scene_003823::room_0",
+ "spatiallm:scene_003862::room_1",
+ "spatiallm:scene_003907::room_4",
+ "spatiallm:scene_004145::room_0",
+ "spatiallm:scene_004295::room_3",
+ "spatiallm:scene_004313::room_3",
+ "spatiallm:scene_004751::room_2",
+ "spatiallm:scene_005012::room_1",
+ "spatiallm:scene_005015::room_0",
+ "spatiallm:scene_005203::room_2",
+ "spatiallm:scene_005203::room_3",
+ "spatiallm:scene_005343::room_2",
+ "spatiallm:scene_005354::room_0",
+ "spatiallm:scene_005652::room_1",
+ "spatiallm:scene_005729::room_4",
+ "spatiallm:scene_006261::room_4",
+ "spatiallm:scene_006263::room_3",
+ "spatiallm:scene_006267::room_0",
+ "spatiallm:scene_006568::room_1",
+ "spatiallm:scene_006799::room_0",
+ "spatiallm:scene_006828::room_1",
+ "spatiallm:scene_006941::room_4",
+ "spatiallm:scene_007153::room_1",
+ "spatiallm:scene_007224::room_1",
+ "spatiallm:scene_007229::room_1",
+ "spatiallm:scene_007229::room_4",
+ "spatiallm:scene_007746::room_4",
+ "spatiallm:scene_007896::room_1",
+ "spatiallm:scene_007896::room_3",
+ "spatiallm:scene_007940::room_3",
+ "spatiallm:scene_008109::room_0",
+ "spatiallm:scene_008198::room_2",
+ "spatiallm:scene_008293::room_0",
+ "spatiallm:scene_008440::room_0",
+ "spatiallm:scene_008915::room_3",
+ "spatiallm:scene_009021::room_2",
+ "spatiallm:scene_009146::room_2",
+ "spatiallm:scene_009149::room_1",
+ "spatiallm:scene_009157::room_5",
+ "spatiallm:scene_009345::room_2",
+ "spatiallm:scene_009487::room_0",
+ "spatiallm:scene_009531::room_3",
+ "spatiallm:scene_009567::room_4",
+ "spatiallm:scene_009739::room_2",
+ "spatiallm:scene_009890::room_0",
+ "spatiallm:scene_010049::room_1",
+ "spatiallm:scene_010288::room_0",
+ "spatiallm:scene_010302::room_4",
+ "spatiallm:scene_010821::room_2",
+ "spatiallm:scene_010900::room_4",
+ "spatiallm:scene_011046::room_4",
+ "spatiallm:scene_011422::room_1",
+ "spatiallm:scene_011430::room_1",
+ "spatiallm:scene_011636::room_0",
+ "spatiallm:scene_011637::room_0",
+ "spatiallm:scene_011648::room_3",
+ "spatiallm:scene_011710::room_1",
+ "spatiallm:scene_012109::room_5",
+ "spatiallm:scene_012212::room_1",
+ "spatiallm:scene_012233::room_1",
+ "spatiallm:scene_012233::room_2",
+ "structured3d_00020_room_662",
+ "structured3d_00020_room_281",
+ "structured3d_00060_room_2030",
+ "structured3d_00060_room_1265",
+ "structured3d_00245_room_163527",
+ "structured3d_00418_room_436",
+ "structured3d_00490_room_955711",
+ "structured3d_00561_room_843593",
+ "structured3d_00631_room_276",
+ "structured3d_00859_room_2393",
+ "structured3d_00862_room_702",
+ "structured3d_01036_room_233",
+ "structured3d_01464_room_7555",
+ "structured3d_02164_room_145",
+ "structured3d_02251_room_472",
+ "structured3d_02266_room_41",
+ "structured3d_02488_room_625171",
+ "structured3d_02659_room_580811",
+ "structured3d_02796_room_831",
+ "structured3d_02979_room_2517",
+ "structured3d_02981_room_3266",
+ "structured3d_03057_room_159",
+ "structured3d_03191_room_951",
+ "structured3d_03214_room_34280",
+ "structured3d_03457_room_141479",
+ "structured3d_03475_room_695351"
+ ]
+}
diff --git a/code/fastfill/v2/ops/test300/README.md b/code/fastfill/v2/ops/test300/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..6c2092bd6c446540a374726974dcb0e839f69d19
--- /dev/null
+++ b/code/fastfill/v2/ops/test300/README.md
@@ -0,0 +1,41 @@
+# test300：FastFill v4 与 LLM 对比用的固定测试子集
+
+从 `data_v4_v2_fixed/test.jsonl`（8,700 个房间）中抽出的 300 个房间，用于在**同一批房间、同一套评测代码**上
+对比 FastFill 各组模型与 GPT-5.6（以及后续的"FastFill 草稿 + GPT 纠正"等方法）。
+
+## 抽样方式
+
+- 生成脚本：`fastfill/v2/ops/make_test300.py --test test.jsonl --output test300 --rows 300 --seed 0`，可完全复现（`rows_sha256` 一致）。
+- 先排除家具超过 256 件的房间（模型容量上限，共 3 个），保证每种方法都能回答每个房间。
+- 按数据来源分层，各来源的份额与测试集中的比例一致（最大余数法）；MultiScan（6 个）和 Scan2CAD（1 个）占比太小，分不到名额。
+- 每个来源内部用同一个随机数生成器（seed 0）不放回抽取；`rows.jsonl` 按原测试集顺序排列。
+- `MANIFEST.json` 记录了种子、各来源数量、原测试集 sha256、每个房间在 test.jsonl 中的行号和 scene_id。
+
+## 文件
+
+| 文件 | 内容 |
+|---|---|
+| `rows.jsonl` | 300 个房间，格式与 test.jsonl 相同（condition / target / validity / provenance） |
+| `MANIFEST.json` | 抽样记录 |
+| `gpt56/predictions.jsonl` | 纯 GPT-5.6（gpt-5.6-sol）的布局，与 `rows.jsonl` 逐行对应，可直接用于 `evaluate --predictions` |
+| `gpt56/room_*.json` | 每个房间的原始请求、原始回答、耗时和 token 用量 |
+| `gpt56_eval/report.json` | 上面这份答案的评测报告（`fastfill.v2.evaluate`） |
+
+GPT-5.6 的协议：完整条件（房间多边形、门窗、固定障碍物、家具清单、约束），每个房间调用一次，不做任何修补
+（`fastfill/v2/ops/llm_probe.py`，提示词来自 `llm_workflow.instructions` / `request_text`）。
+
+## 评测
+
+```bash
+python -m fastfill.v2.evaluate --data test300/rows.jsonl --predictions test300/gpt56/predictions.jsonl --output <目录>
+python -m fastfill.v2.ops.remask_ablation --checkpoint /model-step-N --data test300/rows.jsonl --output <目录> \
+ --modes argmax spread remask4L remask4LX --device cuda
+```
+
+## GPT-5.6 运行记录（2026-10-17）
+
+- 模型 `gpt-5.6-sol`，8 个并行请求（重试时 3 个），限速 30 次/分钟；共约 116 万 token。
+- 300 个房间中 284 个得到布局；**16 个失败**，都是 67–189 件家具的大房间（SAGE-10k 12 个、MansionWorld 2 个、InteriorGS 1 个、SceneSmith 1 个），
+ 原因是 API 网关超时（HTTP 524）、连接中断或返回内容缺少答案。失败房间重试过一次，仍失败的记为 GPT-5.6 失败，
+ `predictions.jsonl` 里 `layout` 为 null，评测时计入失败，不从样本中去掉。
+- 成功房间的耗时中位数 44 秒，平均 63 秒。
diff --git a/code/fastfill/v2/ops/test300/gpt56_predictions.jsonl b/code/fastfill/v2/ops/test300/gpt56_predictions.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..826f7d94f23aca49f7be170f5f2ae9fbc4276671
--- /dev/null
+++ b/code/fastfill/v2/ops/test300/gpt56_predictions.jsonl
@@ -0,0 +1,300 @@
+{"layout": {"schema_version": "fastfill.v2", "objects": [{"id": "obj_0000", "target_size_local_m": [0.55, 0.5, 0.82], "bottom_center_m": [4.6, 2.72, 0.0], "yaw_rad": -1.5707963267948966}, {"id": "obj_0001", "target_size_local_m": [0.06, 0.9, 1.1], "bottom_center_m": [8.25, 3.718, 1.0], "yaw_rad": -1.5707963267948966}, {"id": "obj_0002", "target_size_local_m": [0.4, 0.4, 0.12], "bottom_center_m": [3.7, 2.72, 0.82], "yaw_rad": -1.5707963267948966}, {"id": "obj_0003", "target_size_local_m": [0.55, 0.5, 0.82], "bottom_center_m": [5.5, 2.72, 0.0], "yaw_rad": -1.5707963267948966}, {"id": "obj_0004", "target_size_local_m": [0.25, 0.25, 0.5], "bottom_center_m": [8.65, 3.42, 0.78], "yaw_rad": -1.5707963267948966}, {"id": "obj_0005", "target_size_local_m": [0.9, 2.7, 0.76], "bottom_center_m": [4.6, 1.87, 0.0], "yaw_rad": 1.5707963267948966}, {"id": "obj_0006", "target_size_local_m": [0.5, 1.4, 0.78], "bottom_center_m": [8.25, 3.43, 0.0], "yaw_rad": -1.5707963267948966}, {"id": "obj_0007", "target_size_local_m": [0.55, 0.5, 0.82], "bottom_center_m": [3.7, 1.02, 0.0], "yaw_rad": 1.5707963267948966}, {"id": "obj_0008", "target_size_local_m": [0.55, 0.5, 0.82], "bottom_center_m": [3.7, 2.72, 0.0], "yaw_rad": -1.5707963267948966}]}, "error": null, "latency_s": 42.462392332963645}
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+{"layout": {"schema_version": "fastfill.v2", "objects": [{"id": "obj_0000", "target_size_local_m": [0.48, 0.45, 0.82], "bottom_center_m": [1.72, 0.95, 0.0], "yaw_rad": 0.0}, {"id": "obj_0001", "target_size_local_m": [2.0, 0.9, 0.55], "bottom_center_m": [0.48, 1.15, 0.0], "yaw_rad": 1.5707963267948966}, {"id": "obj_0002", "target_size_local_m": [0.48, 0.45, 0.82], "bottom_center_m": [1.72, 1.55, 0.0], "yaw_rad": 0.0}, {"id": "obj_0003", "target_size_local_m": [0.45, 1.1, 2.0], "bottom_center_m": [0.6, 3.618, 0.0], "yaw_rad": -1.5707963267948966}, {"id": "obj_0004", "target_size_local_m": [0.6, 1.2, 0.75], "bottom_center_m": [2.375, 1.25, 0.0], "yaw_rad": -3.141592653589793}]}, "error": null, "latency_s": 28.548451999900863}
+{"layout": {"schema_version": "fastfill.v2", "objects": [{"id": "obj_0000", "target_size_local_m": [0.55, 0.5, 0.85], "bottom_center_m": [2.2, 0.3, 0.0], "yaw_rad": 1.5707963267948966}]}, "error": null, "latency_s": 11.565844417084008}
diff --git a/code/fastfill/v2/ops/test300/relation_templates.json b/code/fastfill/v2/ops/test300/relation_templates.json
new file mode 100644
index 0000000000000000000000000000000000000000..2dcb21a5de4ac1350397cf803d3f070150d01170
--- /dev/null
+++ b/code/fastfill/v2/ops/test300/relation_templates.json
@@ -0,0 +1,478 @@
+{
+ "train": "../data_v4_v2_fixed/train.jsonl",
+ "rooms_sampled": 18986,
+ "seed": 0,
+ "fraction": 0.15,
+ "min_rooms": 150,
+ "templates": [
+ {
+ "a": "nightstand",
+ "b": "bed",
+ "kind": "near",
+ "max_gap_m": 0.023,
+ "rooms": 2305
+ },
+ {
+ "a": "bed",
+ "b": "nightstand",
+ "kind": "near",
+ "max_gap_m": 0.008,
+ "rooms": 2305
+ },
+ {
+ "a": "wardrobe",
+ "b": "bed",
+ "kind": "near",
+ "max_gap_m": 1.0,
+ "rooms": 1823
+ },
+ {
+ "a": "wardrobe",
+ "b": "bed",
+ "kind": "faces",
+ "share": 0.857,
+ "rooms": 1823
+ },
+ {
+ "a": "bed",
+ "b": "wardrobe",
+ "kind": "near",
+ "max_gap_m": 0.9,
+ "rooms": 1823
+ },
+ {
+ "a": "chair",
+ "b": "table",
+ "kind": "near",
+ "max_gap_m": 0.538,
+ "rooms": 1565
+ },
+ {
+ "a": "chair",
+ "b": "table",
+ "kind": "faces",
+ "share": 0.735,
+ "rooms": 1565
+ },
+ {
+ "a": "bed",
+ "b": "chair",
+ "kind": "near",
+ "max_gap_m": 0.954,
+ "rooms": 819
+ },
+ {
+ "a": "dining chair",
+ "b": "dining table",
+ "kind": "near",
+ "max_gap_m": 0.075,
+ "rooms": 795
+ },
+ {
+ "a": "dining chair",
+ "b": "dining table",
+ "kind": "faces",
+ "share": 0.85,
+ "rooms": 795
+ },
+ {
+ "a": "dining table",
+ "b": "dining chair",
+ "kind": "near",
+ "max_gap_m": 0.108,
+ "rooms": 795
+ },
+ {
+ "a": "coffee table",
+ "b": "sofa",
+ "kind": "near",
+ "max_gap_m": 0.397,
+ "rooms": 712
+ },
+ {
+ "a": "sofa",
+ "b": "coffee table",
+ "kind": "near",
+ "max_gap_m": 0.498,
+ "rooms": 712
+ },
+ {
+ "a": "sofa",
+ "b": "coffee table",
+ "kind": "faces",
+ "share": 0.901,
+ "rooms": 712
+ },
+ {
+ "a": "tv stand",
+ "b": "coffee table",
+ "kind": "faces",
+ "share": 0.95,
+ "rooms": 638
+ },
+ {
+ "a": "king size bed",
+ "b": "nightstand",
+ "kind": "near",
+ "max_gap_m": 0.063,
+ "rooms": 634
+ },
+ {
+ "a": "nightstand",
+ "b": "king size bed",
+ "kind": "near",
+ "max_gap_m": 0.081,
+ "rooms": 634
+ },
+ {
+ "a": "desk",
+ "b": "chair",
+ "kind": "near",
+ "max_gap_m": 0.208,
+ "rooms": 621
+ },
+ {
+ "a": "wardrobe",
+ "b": "king size bed",
+ "kind": "faces",
+ "share": 0.934,
+ "rooms": 578
+ },
+ {
+ "a": "king size bed",
+ "b": "wardrobe",
+ "kind": "near",
+ "max_gap_m": 0.973,
+ "rooms": 578
+ },
+ {
+ "a": "wardrobe",
+ "b": "desk",
+ "kind": "faces",
+ "share": 0.709,
+ "rooms": 424
+ },
+ {
+ "a": "tv stand",
+ "b": "side table",
+ "kind": "faces",
+ "share": 0.788,
+ "rooms": 414
+ },
+ {
+ "a": "armchair",
+ "b": "coffee table",
+ "kind": "faces",
+ "share": 0.823,
+ "rooms": 407
+ },
+ {
+ "a": "coffee table",
+ "b": "armchair",
+ "kind": "near",
+ "max_gap_m": 0.75,
+ "rooms": 407
+ },
+ {
+ "a": "sofa",
+ "b": "side table",
+ "kind": "near",
+ "max_gap_m": 0.944,
+ "rooms": 379
+ },
+ {
+ "a": "side table",
+ "b": "sofa",
+ "kind": "near",
+ "max_gap_m": 0.908,
+ "rooms": 379
+ },
+ {
+ "a": "tv stand",
+ "b": "armchair",
+ "kind": "faces",
+ "share": 0.751,
+ "rooms": 352
+ },
+ {
+ "a": "bed",
+ "b": "stool",
+ "kind": "near",
+ "max_gap_m": 0.932,
+ "rooms": 342
+ },
+ {
+ "a": "tv cabinet",
+ "b": "coffee table",
+ "kind": "faces",
+ "share": 0.946,
+ "rooms": 341
+ },
+ {
+ "a": "tv cabinet",
+ "b": "sofa",
+ "kind": "faces",
+ "share": 0.785,
+ "rooms": 334
+ },
+ {
+ "a": "sofa",
+ "b": "tv cabinet",
+ "kind": "faces",
+ "share": 0.755,
+ "rooms": 334
+ },
+ {
+ "a": "coffee table",
+ "b": "three seat sofa",
+ "kind": "near",
+ "max_gap_m": 0.575,
+ "rooms": 330
+ },
+ {
+ "a": "three seat sofa",
+ "b": "coffee table",
+ "kind": "near",
+ "max_gap_m": 0.585,
+ "rooms": 330
+ },
+ {
+ "a": "three seat sofa",
+ "b": "coffee table",
+ "kind": "faces",
+ "share": 0.908,
+ "rooms": 330
+ },
+ {
+ "a": "wardrobe",
+ "b": "chair",
+ "kind": "faces",
+ "share": 0.737,
+ "rooms": 316
+ },
+ {
+ "a": "wardrobe",
+ "b": "dressing table",
+ "kind": "faces",
+ "share": 0.745,
+ "rooms": 307
+ },
+ {
+ "a": "tv stand",
+ "b": "three seat sofa",
+ "kind": "faces",
+ "share": 0.95,
+ "rooms": 285
+ },
+ {
+ "a": "three seat sofa",
+ "b": "tv stand",
+ "kind": "faces",
+ "share": 0.941,
+ "rooms": 285
+ },
+ {
+ "a": "dressing table",
+ "b": "bed",
+ "kind": "near",
+ "max_gap_m": 0.758,
+ "rooms": 269
+ },
+ {
+ "a": "bed",
+ "b": "dressing table",
+ "kind": "near",
+ "max_gap_m": 0.764,
+ "rooms": 269
+ },
+ {
+ "a": "toilet",
+ "b": "shower room",
+ "kind": "near",
+ "max_gap_m": 0.366,
+ "rooms": 264
+ },
+ {
+ "a": "shower room",
+ "b": "toilet",
+ "kind": "near",
+ "max_gap_m": 0.364,
+ "rooms": 264
+ },
+ {
+ "a": "sectional sofa",
+ "b": "coffee table",
+ "kind": "near",
+ "max_gap_m": 0.27,
+ "rooms": 250
+ },
+ {
+ "a": "sectional sofa",
+ "b": "coffee table",
+ "kind": "faces",
+ "share": 0.952,
+ "rooms": 250
+ },
+ {
+ "a": "coffee table",
+ "b": "sectional sofa",
+ "kind": "near",
+ "max_gap_m": 0.274,
+ "rooms": 250
+ },
+ {
+ "a": "wine cabinet",
+ "b": "dining table combination",
+ "kind": "faces",
+ "share": 0.86,
+ "rooms": 223
+ },
+ {
+ "a": "tv stand",
+ "b": "nightstand",
+ "kind": "faces",
+ "share": 0.942,
+ "rooms": 222
+ },
+ {
+ "a": "nightstand",
+ "b": "tv stand",
+ "kind": "faces",
+ "share": 0.947,
+ "rooms": 222
+ },
+ {
+ "a": "sofa",
+ "b": "armchair",
+ "kind": "near",
+ "max_gap_m": 0.806,
+ "rooms": 219
+ },
+ {
+ "a": "three seat sofa",
+ "b": "side table",
+ "kind": "near",
+ "max_gap_m": 0.289,
+ "rooms": 200
+ },
+ {
+ "a": "side table",
+ "b": "three seat sofa",
+ "kind": "near",
+ "max_gap_m": 0.359,
+ "rooms": 200
+ },
+ {
+ "a": "bookcase",
+ "b": "desk",
+ "kind": "faces",
+ "share": 0.703,
+ "rooms": 197
+ },
+ {
+ "a": "tv cabinet",
+ "b": "side table",
+ "kind": "faces",
+ "share": 0.902,
+ "rooms": 189
+ },
+ {
+ "a": "king size bed",
+ "b": "tv stand",
+ "kind": "faces",
+ "share": 0.957,
+ "rooms": 186
+ },
+ {
+ "a": "tv stand",
+ "b": "king size bed",
+ "kind": "faces",
+ "share": 0.979,
+ "rooms": 186
+ },
+ {
+ "a": "wardrobe",
+ "b": "dining table combination",
+ "kind": "faces",
+ "share": 0.716,
+ "rooms": 182
+ },
+ {
+ "a": "seat",
+ "b": "table",
+ "kind": "near",
+ "max_gap_m": 0.493,
+ "rooms": 179
+ },
+ {
+ "a": "dining table combination",
+ "b": "bed",
+ "kind": "near",
+ "max_gap_m": 0.484,
+ "rooms": 168
+ },
+ {
+ "a": "bed",
+ "b": "dining table combination",
+ "kind": "near",
+ "max_gap_m": 0.488,
+ "rooms": 168
+ },
+ {
+ "a": "box",
+ "b": "bottle",
+ "kind": "near",
+ "max_gap_m": 0.443,
+ "rooms": 166
+ },
+ {
+ "a": "bottle",
+ "b": "box",
+ "kind": "near",
+ "max_gap_m": 0.752,
+ "rooms": 166
+ },
+ {
+ "a": "armchair",
+ "b": "three seat sofa",
+ "kind": "near",
+ "max_gap_m": 0.674,
+ "rooms": 159
+ },
+ {
+ "a": "three seat sofa",
+ "b": "armchair",
+ "kind": "near",
+ "max_gap_m": 0.53,
+ "rooms": 159
+ },
+ {
+ "a": "bed",
+ "b": "bedside table",
+ "kind": "near",
+ "max_gap_m": 0.153,
+ "rooms": 157
+ },
+ {
+ "a": "bedside table",
+ "b": "bed",
+ "kind": "near",
+ "max_gap_m": 0.19,
+ "rooms": 157
+ },
+ {
+ "a": "tv stand",
+ "b": "sectional sofa",
+ "kind": "faces",
+ "share": 0.987,
+ "rooms": 151
+ },
+ {
+ "a": "sectional sofa",
+ "b": "tv stand",
+ "kind": "faces",
+ "share": 0.967,
+ "rooms": 151
+ }
+ ]
+}
diff --git a/code/fastfill/v2/ops/test300/rows.jsonl b/code/fastfill/v2/ops/test300/rows.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..d306e9541638f3807426b48bad994b114c1576b0
--- /dev/null
+++ b/code/fastfill/v2/ops/test300/rows.jsonl
@@ -0,0 +1,300 @@
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diff --git a/code/fastfill/v2/ops/v3_metrics.py b/code/fastfill/v2/ops/v3_metrics.py
new file mode 100644
index 0000000000000000000000000000000000000000..a62c836408a18fd620dbcc331699e4f699fba41e
--- /dev/null
+++ b/code/fastfill/v2/ops/v3_metrics.py
@@ -0,0 +1,202 @@
+"""V3.1's metrics for v2 layouts: the rooms and layouts of fastfill.v2 rows are converted to V3.1's IR and scored by
+V3.1's own code (``fastfill.evaluate.judge``: ``validate.check`` raw, then the interface's <= 10 cm ``snap_inside``
+repair, acceptance as serve.py), every metric next to the reference layout of the same rooms scored the same way, as
+in the V3.1 report (Parse / Complete / Valid / 1 mm Snap / Accepted rate, per-object OOB / support / ceiling rates,
+rooms with each collision kind, mean OOR).
+
+Conversion (the same for every method and for the reference):
+ boundary floor_polygon_xy_m (boundary_type polygon when boundary_known, else unknown: no OOB check, as V3.1)
+ height height_m; z measured from floor_z_m
+ objects category, target_size_local_m -> size, bottom_center_m -> pos, yaw_rad -> yaw
+ fixed room fixed_objects (size_local_m, bottom_center_m, yaw_rad)
+ parent the condition's declared support_parent (an object id; floor -> none); an object declared on a wall is left
+ out of the support check (V3.1's IR has no wall support); an undeclared object's parent is found from the
+ layout itself: none when its bottom is within 5 cm of the floor, else the object whose top is within 5 cm of
+ its bottom and whose footprint holds its centre (the largest such), else none (a support failure)
+ constraints none: V3.1's reported test metrics had no constraints (constraint_satisfaction {}); v2's constraint
+ pass rate is reported by fastfill.v2.evaluate
+A request without a layout (or missing ids) fails, as an unparsable V3.1 answer.
+
+ python -m fastfill.v2.ops.v3_metrics --rows fastfill/v2/ops/test300/rows.jsonl --output DIR \\
+ --method "GPT-5.6=fastfill/v2/ops/test300/gpt56_predictions.jsonl" --method "E remask4L=.../predictions_remask4L.jsonl"
+writes DIR/v3_metrics.json and DIR/v3_core_metrics.png (V3.1's eval_core_metrics chart, one bar per method + GT).
+"""
+from __future__ import annotations
+
+import argparse
+import collections
+import copy
+import json
+from pathlib import Path
+
+from shapely.geometry import Polygon
+
+from fastfill.interface import snap_inside
+from fastfill.scene import apply, footprint
+from fastfill.validate import check, support_contains
+from fastfill.v2.schema import migrate_legacy_row
+
+TOL = .05
+BUCKETS = ("main", "severe", "tuck", "contained", "fixed")
+CORE = (("parse_rate", "Parse\nRate"), ("complete_rate", "Complete\nRate"), ("valid_rate", "Valid\nRate"),
+ ("inside_1mm_rate_repaired", "1mm Snap\nRate"), ("accepted_rate", "Accepted\nRate"))
+
+
+def ir_room(row):
+ c = row["condition"]
+ room = c["room"]
+ z0 = room.get("floor_z_m") or 0.
+ declared = {o["id"]: o.get("support_parent") for o in c["objects"]}
+ gt = {o["id"]: o for o in row["target"]["objects"]}
+ objects = [{"id": o["id"], "category": o.get("category") or "object", "size": list(gt[o["id"]]["target_size_local_m"]),
+ "pos": [*gt[o["id"]]["bottom_center_m"][:2], gt[o["id"]]["bottom_center_m"][2] - z0],
+ "yaw": gt[o["id"]]["yaw_rad"]} for o in c["objects"]]
+ fixed = [{"id": f["id"], "category": f.get("category") or "fixed", "size": list(f["size_local_m"]),
+ "pos": [*f["bottom_center_m"][:2], f["bottom_center_m"][2] - z0], "yaw": f["yaw_rad"]}
+ for f in room.get("fixed_objects") or []]
+ return {"uid": row["provenance"].get("scene_id"), "source": row["provenance"]["source"],
+ "group": row["provenance"].get("group"), "objects": objects, "fixed": fixed, "constraints": [],
+ "boundary": [list(p) for p in room["floor_polygon_xy_m"]],
+ "boundary_type": "polygon" if room.get("boundary_known", True) else "unknown",
+ "height": room.get("height_m"), "meta": {}}, declared, z0
+
+
+def placements(room, declared, layout, z0):
+ """{id: {pos, yaw, on}} in V3.1's form; None when the layout is missing or incomplete."""
+ if layout is None:
+ return None
+ by_id = {o["id"]: o for o in layout["objects"]}
+ if set(by_id) != {o["id"] for o in room["objects"]}:
+ return None
+ size = {o["id"]: o["size"] for o in room["objects"]}
+ pl = {i: {"pos": [*o["bottom_center_m"][:2], o["bottom_center_m"][2] - z0], "yaw": o["yaw_rad"], "on": None}
+ for i, o in by_id.items()}
+ for i in by_id:
+ size[i] = list(by_id[i]["target_size_local_m"])
+ boxes = {i: {"pos": p["pos"], "yaw": p["yaw"], "size": size[i]} for i, p in pl.items()}
+ for i, p in pl.items():
+ d = declared.get(i)
+ if d in boxes and d != i:
+ p["on"] = d
+ elif d in ("floor", "wall"):
+ continue
+ elif abs(p["pos"][2]) > TOL:
+ under = [j for j, b in boxes.items() if j != i and abs(b["pos"][2] + b["size"][2] - p["pos"][2]) <= TOL
+ and support_contains(b, p["pos"], TOL)]
+ if under:
+ p["on"] = max(under, key=lambda j: Polygon(footprint(boxes[j])).area)
+ return pl, size
+
+
+def judge(room, pl, size, wall):
+ """fastfill.evaluate.judge on the predicted sizes, wall-declared objects left out of the support check."""
+ room = {**room, "objects": [{**o, "size": size[o["id"]]} for o in room["objects"]]}
+ pred = apply(room, pl)
+ c = check(pred)
+ fails = [i for i in c["support_fail"] if i not in wall]
+ r = {"valid": not (c["oob"] or fails or c["ceiling"]), "oob_objects": len(c["oob"]), "support_fail": len(fails),
+ "ceiling_objects": len(c["ceiling"]), "oor": c["oor"], "coll_fixed": len(c["fixed_collisions"]),
+ **{f"coll_{k}": len(c["collisions"][k]) for k in BUCKETS[:4]}}
+ floor = Polygon(room["boundary"])
+ rep = copy.deepcopy(pl)
+ snap_inside(rep, room, floor, (), room["boundary"])
+ fixed = apply(room, rep)
+ c2 = check(fixed)
+ grown = floor.buffer(1e-3)
+ inside = lambda scene: all(grown.covers(Polygon(footprint(o))) for o in scene["objects"])
+ r["inside_1mm"], r["inside_1mm_repaired"] = inside(pred), inside(fixed)
+ r["valid_repaired"] = not (c2["oob"] or [i for i in c2["support_fail"] if i not in wall] or c2["ceiling"])
+ r["repaired_objects"] = sum(rep[i]["pos"] != pl[i]["pos"] for i in pl)
+ r["accepted"] = r["valid_repaired"] and r["inside_1mm_repaired"] and not c2["fixed_blocking"]
+ return r
+
+
+def score(rows, layouts):
+ out = []
+ for row, layout in zip(rows, layouts):
+ room, declared, z0 = ir_room(row)
+ wall = {i for i, d in declared.items() if d == "wall"}
+ gt_pl, gt_size = placements(room, declared, row["target"], z0)
+ r = {"source": room["source"], "group": room["group"], "n": len(room["objects"]),
+ "gt": judge(room, gt_pl, gt_size, wall)}
+ got = placements(room, declared, layout, z0)
+ r["parsed"] = layout is not None
+ r["error"] = None if got else ("no_layout" if layout is None else "missing_ids")
+ out.append({**r, **(judge(room, *got, wall) if got else {"valid": False})})
+ return out
+
+
+def summarize(rs):
+ """fastfill.evaluate.summarize's pooled figures."""
+ n, ok = len(rs), [r for r in rs if r["error"] is None]
+ objs = sum(r["n"] for r in ok)
+ rate = lambda num, den: num / den if den else None
+ s = {"rooms": n, "parse_rate": sum(r["parsed"] for r in rs) / n, "complete_rate": len(ok) / n,
+ "errors": dict(collections.Counter(r["error"] for r in rs if r["error"]))}
+ for k, key in (("valid_rate", "valid"), ("valid_rate_repaired", "valid_repaired"), ("inside_1mm_rate", "inside_1mm"),
+ ("inside_1mm_rate_repaired", "inside_1mm_repaired"), ("accepted_rate", "accepted")):
+ s[k] = sum(r.get(key, False) for r in rs) / n
+ s[f"{k}_gt"] = sum(r["gt"][key] for r in rs) / n
+ for k, key in (("repaired_object_rate", "repaired_objects"), ("oob_object_rate", "oob_objects"),
+ ("support_fail_rate", "support_fail"), ("ceiling_object_rate", "ceiling_objects")):
+ s[k], s[f"{k}_gt"] = rate(sum(r[key] for r in ok), objs), rate(sum(r["gt"][key] for r in ok), objs)
+ for k in BUCKETS:
+ name = "fixed_collision" if k == "fixed" else k
+ s[f"rooms_with_{name}"] = rate(sum(r[f"coll_{k}"] > 0 for r in ok), len(ok))
+ s[f"rooms_with_{name}_gt"] = rate(sum(r["gt"][f"coll_{k}"] > 0 for r in ok), len(ok))
+ s["mean_oor"], s["mean_oor_gt"] = rate(sum(r["oor"] for r in ok), len(ok)), rate(sum(r["gt"]["oor"] for r in ok), len(ok))
+ return s
+
+
+def figure(results, path):
+ import matplotlib
+ matplotlib.use("Agg")
+ import matplotlib.pyplot as plt
+ names = list(results)
+ fig, ax = plt.subplots(figsize=(max(12, 2.2 * len(CORE) * (len(names) + 1) / 2), 6))
+ width = .8 / (len(names) + 1)
+ colors = plt.cm.tab10.colors
+ for k, name in enumerate(names + ["Ground Truth"]):
+ s = results[names[0]] if name == "Ground Truth" else results[name]
+ vals = [100 * (s.get(f"{key}_gt", 1.) if name == "Ground Truth" else s[key]) for key, _ in CORE] # GT always parses
+ bars = ax.bar([i + k * width for i in range(len(CORE))], vals, width, label=name,
+ color="#a9b1bf" if name == "Ground Truth" else colors[k % 10])
+ for b, v in zip(bars, vals):
+ ax.text(b.get_x() + b.get_width() / 2, v + .8, f"{v:.1f}", ha="center", fontsize=7, rotation=90)
+ ax.set_xticks([i + .4 - width / 2 for i in range(len(CORE))], [label for _, label in CORE])
+ ax.set(ylabel="Percentage (%)", ylim=(0, 118))
+ ax.set_title(f"V3.1 metrics on the {results[names[0]]['rooms']} test300 rooms (V3.1 evaluator, same rooms for every method)",
+ fontweight="bold")
+ ax.grid(axis="y", ls="--", alpha=.5)
+ ax.legend(fontsize=8, ncol=3, loc="lower left")
+ fig.tight_layout()
+ fig.savefig(path, dpi=160)
+ plt.close(fig)
+
+
+def main(argv=None):
+ p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+ p.add_argument("--rows", type=Path, required=True)
+ p.add_argument("--method", action="append", required=True, help="NAME=predictions.jsonl (one {layout} per row)")
+ p.add_argument("--output", type=Path, required=True)
+ a = p.parse_args(argv)
+ rows = [migrate_legacy_row(json.loads(l)) for l in a.rows.read_text().splitlines() if l.strip()]
+ results = {}
+ for spec in a.method:
+ name, path = spec.split("=", 1)
+ layouts = [json.loads(l).get("layout") for l in Path(path).read_text().splitlines() if l.strip()]
+ if len(layouts) != len(rows):
+ raise SystemExit(f"{path}: {len(layouts)} predictions for {len(rows)} rows")
+ results[name] = summarize(score(rows, layouts))
+ s = results[name]
+ print(f"{name:28s} parse {s['parse_rate']:.3f} complete {s['complete_rate']:.3f} valid {s['valid_rate']:.3f} "
+ f"(GT {s['valid_rate_gt']:.3f}) 1mm {s['inside_1mm_rate_repaired']:.3f} (GT {s['inside_1mm_rate_repaired_gt']:.3f}) "
+ f"accepted {s['accepted_rate']:.3f} (GT {s['accepted_rate_gt']:.3f}) collision rooms {s['rooms_with_main']:.3f} "
+ f"(GT {s['rooms_with_main_gt']:.3f})", flush=True)
+ a.output.mkdir(parents=True, exist_ok=True)
+ (a.output / "v3_metrics.json").write_text(json.dumps(results, indent=1) + "\n")
+ figure(results, a.output / "v3_core_metrics.png")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/code/fastfill/v2/ops/v4_plots.py b/code/fastfill/v2/ops/v4_plots.py
new file mode 100644
index 0000000000000000000000000000000000000000..48d3a7611115befb0a2f705e79fc399eb7ea65d7
--- /dev/null
+++ b/code/fastfill/v2/ops/v4_plots.py
@@ -0,0 +1,352 @@
+"""Figures for the v4 runs, from files the runs already write (no model, no GPU, no network):
+
+ curves training_log.json of each arm: train loss (smoothed) with the validation points, every loss term
+ (position cell / residual / z, size, yaw, support), the validation quality checks, learning rate and
+ gradient norms
+ results runs/ablation-v4: every arm x decoding mode next to GPT-5.6 on the 300 test rooms, one panel per metric
+ with the trivial baselines as dashed lines; the validation sample; per-source position and size error;
+ the support head's accuracy
+ rooms a few test rooms drawn side by side: ground truth, GPT-5.6 and each arm (vlm_judge's top-down plan and
+ two corner views)
+
+ python -m fastfill.v2.ops.v4_plots all --out runs/plots # every figure whose inputs exist
+ python -m fastfill.v2.ops.v4_plots curves --runs base=runs/main4-...-base D=... --out runs/plots
+ python -m fastfill.v2.ops.v4_plots rooms --rows 3,40,77 --out runs/plots
+
+Arms default to the newest runs/main4-*-e5-{base,D,E}* directory with a training_log.json. Needs matplotlib.
+"""
+from __future__ import annotations
+
+import argparse
+import glob
+import json
+import math
+from pathlib import Path
+import tempfile
+
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt # noqa: E402
+
+REPO = Path(__file__).resolve().parents[3]
+ARMS = ("base", "D", "E")
+COLOR = {"base": "#7f8c8d", "D": "#2e86de", "E": "#e67e22", "E_nosupport": "#f5b041", "GPT-5.6": "#8e44ad"}
+TERMS = (("position_cell", "position cell (CE)"), ("position_residual", "position residual"), ("position_z", "height z"),
+ ("size", "size"), ("yaw_cls", "yaw class (CE)"), ("support", "support (CE)"))
+METRICS = (("position_m", "Position error (m) ↓", 1, "room_center_position", "bottom_center_error_m", "room centre"),
+ ("log_size", "Size error (log) ↓", 1, "category_median_size", "log_size_error", "category median"),
+ ("yaw_rad", "Yaw error (rad) ↓", 1, "uniform_yaw", "yaw_error_rad", "uniform yaw"),
+ ("footprint_outside", "Outside room (%) ↓", 100, None, None, None),
+ ("door_blocking", "Blocking a door (%) ↓", 100, None, None, None),
+ ("collision_pairs_per_room", "Collisions per room ↓", 1, None, None, None),
+ ("constraint_pass", "Constraints satisfied (%) ↑", 100, None, None, None),
+ ("clean_room_rate", "Clean rooms (%) ↑", 100, None, None, None))
+
+
+def color(name):
+ return COLOR.get(name.split(" ")[0], "#34495e")
+
+
+def smooth(values, alpha=.98):
+ out, level = [], None
+ for v in values:
+ level = v if level is None else alpha * level + (1 - alpha) * v
+ out.append(level)
+ return out
+
+
+def save(fig, path):
+ path.parent.mkdir(parents=True, exist_ok=True)
+ fig.savefig(path, dpi=150, bbox_inches="tight")
+ plt.close(fig)
+ print(f"wrote {path}")
+
+
+def find_runs(specs):
+ if specs:
+ return {k: Path(v) for k, v in (s.split("=", 1) for s in specs)}
+ runs = {}
+ for arm in ARMS:
+ found = sorted((Path(p).parent for p in glob.glob(str(REPO / f"runs/main4-*-e5-{arm}*/training_log.json"))),
+ key=lambda p: p.stat().st_mtime)
+ if found:
+ runs[arm] = found[-1]
+ return runs
+
+
+# ---------------------------------------------------------------------------------------------------------- curves
+def curves(runs, out):
+ logs = {arm: json.loads((path / "training_log.json").read_text()) for arm, path in runs.items()
+ if (path / "training_log.json").is_file()}
+ if not logs:
+ print("curves: no training_log.json found")
+ return
+ fig, ax = plt.subplots(figsize=(11, 5))
+ for arm, log in logs.items():
+ steps, loss = [r["step"] for r in log], [r["loss"] for r in log]
+ ax.plot(steps, loss, color=color(arm), alpha=.15, lw=.6)
+ ax.plot(steps, smooth(loss), color=color(arm), lw=2, label=f"{arm} train (final {smooth(loss)[-1]:.3f})")
+ val = [(r["step"], r["validation"]["geometry_objective_mean_of_batches"]) for r in log if r.get("validation")]
+ if val:
+ ax.plot(*zip(*val), "o--", color=color(arm), ms=5, lw=1, label=f"{arm} validation")
+ ax.set(xlabel="step", ylabel="loss", title="Training loss (each arm's own objective: D/E add masked and support terms)")
+ ax.grid(ls=":", alpha=.6)
+ ax.legend(ncol=2, fontsize=9)
+ save(fig, out / "loss_curves.png")
+
+ fig, axes = plt.subplots(2, 3, figsize=(15, 8))
+ for ax, (key, title) in zip(axes.flat, TERMS):
+ for arm, log in logs.items():
+ pts = [(r["step"], r["unweighted"][key]) for r in log if (r.get("unweighted") or {}).get(key) is not None]
+ if pts:
+ ax.plot([p[0] for p in pts], smooth([p[1] for p in pts]), color=color(arm), lw=1.6, label=f"{arm} train")
+ val = [(r["step"], r["validation"]["unweighted"].get(key)) for r in log
+ if r.get("validation") and r["validation"]["unweighted"].get(key) is not None]
+ if val:
+ ax.plot(*zip(*val), "o--", color=color(arm), ms=4, lw=1, label=f"{arm} val")
+ ax.set_title(title)
+ ax.grid(ls=":", alpha=.6)
+ axes.flat[0].legend(fontsize=8, ncol=2)
+ fig.suptitle("Loss terms (unweighted; lines: training, smoothed; dots: validation)")
+ fig.tight_layout()
+ save(fig, out / "loss_terms.png")
+
+ checks = (("footprint_outside_objects", "objects", "Outside room (%)"),
+ ("door_blocking_objects", "floor_objects", "Blocking a door (%)"),
+ ("bev_overlap_pairs", "pairs", "Overlapping pairs (%)"))
+ fig, axes = plt.subplots(1, 4, figsize=(18, 4.2))
+ for arm, log in logs.items():
+ val = [r for r in log if r.get("validation")]
+ if not val:
+ continue
+ steps = [r["step"] for r in val]
+ sel = [(r["step"], r["validation"]["selection_metric"]) for r in val
+ if isinstance(r["validation"].get("selection_metric"), (int, float))]
+ if sel:
+ axes[0].plot(*zip(*sel), "o-", color=color(arm), label=arm)
+ for ax, (num, den, _) in zip(axes[1:], checks):
+ c = [r["validation"].get("collapse_matched") or r["validation"]["collapse"] for r in val]
+ ax.plot(steps, [100 * x[num] / max(x[den], 1) for x in c], "o-", color=color(arm), label=arm)
+ for ax, title in zip(axes, ["Selection metric ↓ (best checkpoint)"] + [c[2] for c in checks]):
+ ax.set(title=title, xlabel="step")
+ ax.grid(ls=":", alpha=.6)
+ axes[0].legend()
+ fig.suptitle("Validation during training")
+ fig.tight_layout()
+ save(fig, out / "validation_during_training.png")
+
+ fig, (a, b) = plt.subplots(1, 2, figsize=(14, 4.2))
+ for arm, log in logs.items():
+ steps = [r["step"] for r in log]
+ for group, ls in (("backbone", "-"), ("decoder", "--")):
+ lr = [(r.get("learning_rates") or {}).get(group) for r in log]
+ if any(v is not None for v in lr):
+ a.plot(steps, lr, ls, color=color(arm), label=f"{arm} {group}")
+ for part, ls in (("backbone", "-"), ("decoder", "--"), ("position_head", ":")):
+ g = [(s, (r.get("gradient_norms") or {}).get(part)) for s, r in zip(steps, log)]
+ g = [(s, v) for s, v in g if v is not None]
+ if g:
+ b.plot([p[0] for p in g], smooth([p[1] for p in g]), ls, color=color(arm), label=f"{arm} {part}")
+ a.set(title="Learning rate", xlabel="step")
+ b.set(title="Gradient norm (smoothed)", xlabel="step", yscale="log")
+ for ax in (a, b):
+ ax.grid(ls=":", alpha=.6)
+ ax.legend(fontsize=8)
+ fig.tight_layout()
+ save(fig, out / "lr_and_gradients.png")
+
+
+# --------------------------------------------------------------------------------------------------------- results
+def summarize(report):
+ from fastfill.v2.ops.remask_ablation import summarize as base
+ return base(report)
+
+
+def results_table(ablation, prefix):
+ """[(label, metrics, report)] for every arm x mode under ablation/, plus GPT-5.6 when scored."""
+ table = []
+ for arm in (*ARMS, "E_nosupport"):
+ path = ablation / f"{prefix}{arm}" / "ablation.json"
+ if not path.is_file():
+ continue
+ for mode, metrics in json.loads(path.read_text()).items():
+ if mode == "ground_truth":
+ continue
+ report = ablation / f"{prefix}{arm}" / f"eval_{mode}" / "report.json"
+ table.append((f"{arm} {mode}", metrics, json.loads(report.read_text()) if report.is_file() else None))
+ gpt = ablation / f"{prefix}gpt56" / "report.json"
+ if gpt.is_file():
+ report = json.loads(gpt.read_text())
+ table.append(("GPT-5.6", summarize(report), report))
+ return table
+
+
+def metric_panels(table, title, path):
+ fig, axes = plt.subplots(2, 4, figsize=(20, max(6, .55 * len(table) + 3)))
+ labels = [t[0] for t in table]
+ reference = next((r for _, _, r in table if r and r.get("baselines_paired")), None)
+ for ax, (key, name, scale, base, base_key, base_name) in zip(axes.flat, METRICS):
+ values = [m.get(key) for _, m, _ in table]
+ shown = [scale * v if v is not None else 0 for v in values]
+ bars = ax.barh(range(len(table)), shown, color=[color(l) for l in labels])
+ for bar, v in zip(bars, values):
+ ax.text(bar.get_width(), bar.get_y() + bar.get_height() / 2,
+ " –" if v is None else f" {scale * v:.{1 if scale == 100 else 3}f}", va="center", fontsize=8)
+ b = reference["baselines_paired"].get(base, {}).get(base_key, {}).get("mean") if base and reference else None
+ if b is not None:
+ ax.axvline(b, color="k", ls="--", lw=1)
+ name += f"\n(dashed: {base_name} {b:.3f})"
+ ax.set_yticks(range(len(table)), labels if ax in axes[:, 0] else [""] * len(table), fontsize=9)
+ ax.invert_yaxis()
+ ax.set_title(name, fontsize=11)
+ ax.margins(x=.18)
+ ax.grid(axis="x", ls=":", alpha=.6)
+ fig.suptitle(title, fontsize=14, weight="bold")
+ fig.tight_layout()
+ save(fig, path)
+
+
+def by_source(table, path):
+ picked = [(l, r) for l, _, r in table if r and r.get("by_source")]
+ if not picked:
+ return
+ sources = sorted({s for _, r in picked for s in r["by_source"]})
+ n = {s: max(r["by_source"].get(s, {}).get("requests", 0) for _, r in picked) for s in sources}
+ fig, axes = plt.subplots(1, 2, figsize=(18, max(6, .5 * len(sources) * max(1, len(picked) / 3) + 2)))
+ height = .8 / len(picked)
+ for ax, (key, name) in zip(axes, (("bottom_center_error_m", "Position error (m) ↓"), ("log_size_error", "Size error (log) ↓"))):
+ for k, (label, report) in enumerate(picked):
+ vals = [(((report["by_source"].get(s) or {}).get("model") or {}).get("reference") or {}).get(key, {}).get("mean")
+ for s in sources]
+ ax.barh([i + k * height for i in range(len(sources))], [v or 0 for v in vals], height, color=color(label),
+ alpha=.6 + .4 * (k % 2), label=label)
+ ax.set_yticks([i + .4 - height / 2 for i in range(len(sources))], [f"{s} (N={n[s]})" for s in sources], fontsize=9)
+ ax.invert_yaxis()
+ ax.set_title(name)
+ ax.grid(axis="x", ls=":", alpha=.6)
+ axes[1].legend(fontsize=8, loc="lower right")
+ fig.suptitle("Test 300 by data source", fontsize=14, weight="bold")
+ fig.tight_layout()
+ save(fig, path)
+
+
+def support_figure(table, path):
+ rows = [(l, m["support_accuracy"]) for l, m, _ in table if (m.get("support_accuracy") or {}).get("all") is not None]
+ if not rows:
+ return
+ keys = ("all", "floor", "wall", "object")
+ fig, ax = plt.subplots(figsize=(10, 4.5))
+ w = .8 / len(rows)
+ for k, (label, s) in enumerate(rows):
+ ax.bar([i + k * w for i in range(len(keys))], [100 * (s.get(x) or 0) for x in keys], w, label=label,
+ color=color(label), alpha=.5 + .5 * (k % 2))
+ ax.set_xticks([i + .4 - w / 2 for i in range(len(keys))], ["all", "floor", "wall", "on furniture"])
+ ax.set(ylabel="accuracy (%)", ylim=(0, 105), title="Support prediction accuracy (no support declared in the input)")
+ ax.legend(fontsize=8)
+ ax.grid(axis="y", ls=":", alpha=.6)
+ save(fig, path)
+
+
+def results(ablation, out):
+ test = results_table(ablation, "test300_")
+ if test:
+ metric_panels(test, "Test 300 rooms: FastFill v4 arms vs GPT-5.6 (same rooms, same evaluator)", out / "test300_metrics.png")
+ best = [t for t in test if t[0] == "GPT-5.6"] + [max((t for t in test if t[0].startswith(arm + " ") and t[2]),
+ key=lambda t: -t[1]["position_m"], default=None) for arm in ARMS]
+ by_source([t for t in best if t], out / "test300_by_source.png")
+ val = results_table(ablation, "")
+ if val:
+ metric_panels(val, "Validation sample: every arm x decoding mode", out / "validation_metrics.png")
+ support_figure(val, out / "support_accuracy.png")
+ probe = results_table(ablation, "probe20_")
+ if probe:
+ metric_panels(probe, "The 20 probe rooms", out / "probe20_metrics.png")
+ if not (test or val or probe):
+ print(f"results: nothing under {ablation}")
+
+
+# ----------------------------------------------------------------------------------------------------------- rooms
+def jsonl(path):
+ return [json.loads(l) for l in Path(path).read_text().splitlines() if l.strip()]
+
+
+def room_layouts(ablation, rows_path, gpt_path, mode):
+ rows = jsonl(rows_path)
+ methods = {"Ground truth": [{"schema_version": r["target"]["schema_version"], "objects": r["target"]["objects"]} for r in rows]}
+ if Path(gpt_path).is_file():
+ methods["GPT-5.6"] = [p.get("layout") for p in jsonl(gpt_path)]
+ for arm in ARMS:
+ path = ablation / f"test300_{arm}" / f"predictions_{mode}.jsonl"
+ if not path.is_file() and arm == "base":
+ path = ablation / f"test300_{arm}" / "predictions_spread.jsonl"
+ if path.is_file():
+ methods[f"{arm} {path.stem.split('_', 1)[1]}"] = [p.get("layout") for p in jsonl(path)]
+ return rows, methods
+
+
+def default_rows(rows, methods, count=6):
+ """Rooms every method answered, 6-30 objects, one per source while possible, in test order."""
+ ok = [i for i, r in enumerate(rows) if 6 <= len(r["condition"]["objects"]) <= 30
+ and all(m[i] is not None for m in methods.values())]
+ picked, seen = [], set()
+ for i in ok:
+ if rows[i]["provenance"]["source"] not in seen:
+ picked.append(i)
+ seen.add(rows[i]["provenance"]["source"])
+ return (picked + [i for i in ok if i not in picked])[:count]
+
+
+def rooms(ablation, rows_path, gpt_path, mode, chosen, out):
+ from fastfill.v2.ops.vlm_judge import items, render_panel
+ rows, methods = room_layouts(ablation, rows_path, gpt_path, mode)
+ chosen = chosen or default_rows(rows, methods)
+ with tempfile.TemporaryDirectory() as tmp:
+ for r in chosen:
+ row = rows[r]
+ cols = min(2, len(methods))
+ fig, axes = plt.subplots(math.ceil(len(methods) / cols), cols, figsize=(12 * cols, 8.2 * math.ceil(len(methods) / cols)),
+ squeeze=False)
+ for ax in axes.flat:
+ ax.axis("off")
+ for ax, (name, layouts) in zip(axes.flat, methods.items()):
+ ax.axis("off")
+ layout = layouts[r]
+ try:
+ if layout is None:
+ raise ValueError("no layout")
+ its, _ = items(row, layout)
+ except (ValueError, KeyError) as error:
+ ax.set_title(f"{name}: {error}", fontsize=14)
+ continue
+ png = Path(tmp) / f"{r}-{len(name)}.png"
+ render_panel(png, row, its)
+ ax.imshow(plt.imread(png))
+ ax.set_title(name, fontsize=16, weight="bold", color=color(name))
+ room = row["condition"]["room"]
+ fig.suptitle(f"test300 row {r}: {row['provenance']['source']}, {room.get('room_type') or 'room'}, "
+ f"{len(row['condition']['objects'])} objects", fontsize=18, weight="bold")
+ fig.tight_layout()
+ save(fig, out / "rooms" / f"row{r:03d}.png")
+
+
+def main(argv=None):
+ p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+ p.add_argument("command", choices=("all", "curves", "results", "rooms"))
+ p.add_argument("--out", type=Path, default=REPO / "runs/plots")
+ p.add_argument("--runs", nargs="*", help="arm=run_dir (default: newest runs/main4-*-e5-*)")
+ p.add_argument("--ablation", type=Path, default=REPO / "runs/ablation-v4")
+ p.add_argument("--test-rows", type=Path, default=REPO / "fastfill/v2/ops/test300/rows.jsonl")
+ p.add_argument("--gpt", type=Path, default=REPO / "fastfill/v2/ops/test300/gpt56_predictions.jsonl")
+ p.add_argument("--mode", default="remask4LX", help="decoding mode drawn in rooms (base falls back to spread)")
+ p.add_argument("--rows", type=lambda s: [int(x) for x in s.split(",")], help="test300 row numbers for rooms")
+ a = p.parse_args(argv)
+ if a.command in ("all", "curves"):
+ curves(find_runs(a.runs), a.out)
+ if a.command in ("all", "results"):
+ results(a.ablation, a.out)
+ if a.command in ("all", "rooms"):
+ rooms(a.ablation, a.test_rows, a.gpt, a.mode, a.rows, a.out)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/code/fastfill/v2/ops/v4_report.py b/code/fastfill/v2/ops/v4_report.py
new file mode 100644
index 0000000000000000000000000000000000000000..dbabfcc9e1cb2cc54d2bc57aecfc754dcdf59445
--- /dev/null
+++ b/code/fastfill/v2/ops/v4_report.py
@@ -0,0 +1,77 @@
+"""One Markdown page from the v4 ablation outputs: every arm x decoding mode on the validation sample, the same on
+the 300 test rooms and the first 20 rooms GPT-5.6 answered (with GPT-5.6's own layouts), the support head's accuracy and the anchor probe.
+Whatever is missing (an arm that did not finish) is left out, not an error.
+
+ python fastfill/v2/ops/v4_report.py runs/ablation-v4 # writes runs/ablation-v4/REPORT.md
+"""
+from __future__ import annotations
+
+import json
+from pathlib import Path
+import sys
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[3]))
+from fastfill.v2.ops.remask_ablation import summarize # noqa: E402
+
+COLUMNS = [("position_m", "位置误差 m", 2), ("log_size", "尺寸 log", 3), ("yaw_rad", "朝向 rad", 3),
+ ("footprint_outside", "出界", "%"), ("door_blocking", "挡门", "%"), ("overlap_iou_0.3", "重叠", "%"),
+ ("collision_pairs_per_room", "碰撞/房间", 2), ("constraint_pass", "约束通过", "%"),
+ ("clean_room_rate", "干净房间", "%"), ("decode_ms", "解码 ms", 0)]
+
+
+def cell(value, digits):
+ if value is None:
+ return "–"
+ return f"{100 * value:.1f}%" if digits == "%" else f"{value:.{digits}f}"
+
+
+def table(rows):
+ head = "| 模型 | 解码 | " + " | ".join(name for _, name, _ in COLUMNS) + " |"
+ lines = [head, "|" + "---|" * (len(COLUMNS) + 2)]
+ lines += ["| " + " | ".join([arm, mode] + [cell(metrics.get(key), d) for key, _, d in COLUMNS]) + " |"
+ for arm, mode, metrics in rows]
+ return "\n".join(lines)
+
+
+def ablation_rows(root, prefix=""):
+ rows = []
+ for arm in ("base", "D", "E", "E_nosupport"):
+ path = root / f"{prefix}{arm}" / "ablation.json"
+ if path.is_file():
+ rows += [(arm, mode, m) for mode, m in json.loads(path.read_text()).items() if mode != "ground_truth"]
+ return rows
+
+
+def main(root):
+ root, out = Path(root), []
+ out.append("# FastFill v4 结果汇总\n")
+ rows = ablation_rows(root)
+ if rows:
+ out += ["## 验证集抽样（同一批房间）\n", table(rows), ""]
+ for prefix, title in (("test300_", "测试集 300 个房间（按来源分层抽样），和 GPT-5.6 同一批房间、同一评测代码"),):
+ held = ablation_rows(root, prefix)
+ gpt = root / f"{prefix}gpt56" / "report.json"
+ if gpt.is_file():
+ held.append(("GPT-5.6", "单次调用", summarize(json.loads(gpt.read_text()))))
+ if held:
+ out += [f"## {title}\n", table(held), ""]
+ probe = ablation_rows(root, "probe20_")
+ gpt = root / "probe20_gpt56" / "report.json"
+ if gpt.is_file():
+ probe.append(("GPT-5.6", "单次调用", summarize(json.loads(gpt.read_text()))))
+ if probe:
+ out += ["## GPT-5.6 回答过的 20 个房间（同一评测代码）\n", table(probe), ""]
+ support = [(arm, mode, m["support_accuracy"]) for arm, mode, m in rows if (m.get("support_accuracy") or {}).get("all") is not None]
+ if support:
+ out += ["## 支撑预测准确率（去掉全部支撑声明）\n", "| 模型 | 解码 | 全部 | 地面 | 墙 | 放在家具上 | 个数 |", "|---|---|---|---|---|---|---|"]
+ out += [f"| {arm} | {mode} | " + " | ".join(cell(s.get(k), "%") for k in ("all", "floor", "wall", "object"))
+ + f" | {s.get('objects')} |" for arm, mode, s in support]
+ out.append("")
+ anchor = root / "anchor_probe_D.json"
+ if anchor.is_file():
+ out += ["## anchor_probe（D）\n", "```", json.dumps(json.loads(anchor.read_text()), indent=1, ensure_ascii=False)[:4000], "```", ""]
+ (root / "REPORT.md").write_text("\n".join(out) + "\n")
+
+
+if __name__ == "__main__":
+ main(sys.argv[1] if len(sys.argv) > 1 else "runs/ablation-v4")
diff --git a/code/fastfill/v2/ops/vlm_judge.py b/code/fastfill/v2/ops/vlm_judge.py
new file mode 100644
index 0000000000000000000000000000000000000000..921c4a06aeb77b17e9a57fcdef7ab05ca81fa653
--- /dev/null
+++ b/code/fastfill/v2/ops/vlm_judge.py
@@ -0,0 +1,533 @@
+"""Box renders, anchors, RoomGen hand-offs and a contact sheet for the VLM-judge pilot (render-and-judge design,
+sections B "Rendering" and C "Implement first"). CPU only, deterministic, no network and no model calls; the saved
+comparison runs are read only.
+
+ python -m fastfill.v2.ops.vlm_judge render --comparison-dir B --out DIR [--rows 111,114] [--methods gt,random]
+ python -m fastfill.v2.ops.vlm_judge anchors --comparison-dir B --out DIR
+ python -m fastfill.v2.ops.vlm_judge handoff --comparison-dir B --out DIR --roomgenbench-root .../RoomGenBench
+ python -m fastfill.v2.ops.vlm_judge contact --out DIR
+
+render: DIR/panels/.png, one 1536x1024 panel per (room, method): a 1024 px top-down plan (black walls, 0.5 m
+grid, 1 m scale bar, boxes filled by RoomGen place and drawn in order of their top height, the front edge dark with an
+arrow, "n category" labels, numbers only above 25 objects) and two 512 px views from opposite upper corners (numpy
+z-buffer, near walls removed, flat shading, front faces darker). The image names no method; DIR/manifest.json maps
+hash -> (row, method) with the programmatic checks, and keeps each room's numbered legend for the judge prompt.
+anchors: DIR/anchors.jsonl. handoff: DIR/handoff/row-/{export,assembly} and DIR/handoff/results.json
+(export_handoff, then ``fastfill.v2.roomgenbench --method layout_boxes --require-placement``). contact:
+DIR/contact.html, panels by relative path with method names and checks. contact.html, handoff/ and manifest.json name
+the methods: for the operator, never for the judge (the judge gets panels/.png and the legend only).
+
+Conventions (fastfill.v2): target_size_local_m [sx, sy, sz] with local +X the front, bottom_center_m in metres, yaw_rad
+counter-clockwise from world +X; footprints as validation.footprint; place (floor / on_object / wall / unknown) from
+direct_layout.layout_to_roomgenbench, coloured as RoomGenBench assemble.PLACE_RGBA (grey for unknown).
+"""
+from __future__ import annotations
+
+import argparse
+from collections import Counter
+from copy import deepcopy
+import hashlib
+import html
+import json
+import math
+from pathlib import Path
+import subprocess
+import sys
+
+import numpy as np
+
+from fastfill.v2.direct_layout import export_handoff, layout_to_roomgenbench
+from fastfill.v2.geometry import wrap_yaw
+from fastfill.v2.validation import validate_scene
+
+
+REPO = Path(__file__).resolve().parents[3]
+COMPARISON = REPO / "outputs/fastfill_v2/comparison-20261007b"
+ROWS_FILE = "llm-harness-300/rows.jsonl" # byte-identical to llm-prompt-300/rows.jsonl
+SOURCES = {"ff-spread": "llmrows-main7-cell05-main-20261007b-e5/outcomes.jsonl", # old step-6357 FastFill; the
+ "ff-argmax": "llmrows-main7-cell05-main-20261007b-e5-argmax/outcomes.jsonl", # decoded layout is raw_prediction
+ "llm-prompt": "llm-prompt-300/predictions.jsonl", "llm-harness": "llm-harness-300/predictions.jsonl"}
+METHODS = ("gt", *SOURCES)
+ANCHORS = ("room-centre", "random", "shuffled", "yaw-flipped", "mirrored", "identical")
+NAMES = {"gt": "Ground truth", "ff-spread": "FastFill spread (old step-6357)", "ff-argmax": "FastFill argmax (old step-6357)",
+ "llm-prompt": "LLM prompt", "llm-harness": "LLM harness", **{a: f"Anchor: {a}" for a in ANCHORS}}
+# Pilot (every method returned a layout; ground truth without boundary violations unless noted): bedroom 119
+# (MansionWorld, 14 objects), bathroom 104 (InternScenes, 8, height unknown), kitchen 156 (SAGE-10k, 14), living room
+# 59 (Holodeck, 12, height unknown; its ground truth overshoots the walls by centimetres), dining room 133 (3D-FRONT,
+# 10, chairs tucked under the table), small meeting room 16 (MansionWorld, 14), bedroom 197 (Structured3D, 5) and
+# gym 249 (SAGE-10k, 46 objects, ground truth clean).
+PILOT_ROWS = (119, 104, 156, 59, 133, 16, 197, 249)
+KEYS = ("id", "target_size_local_m", "bottom_center_m", "yaw_rad")
+SALT = "fastfill-vlm-judge-v1"
+
+DEFAULT_HEIGHT_M = 2.8
+PLACE_RGB = {"floor": (77, 128, 204), "on_object": (90, 170, 100), "wall": (214, 120, 60), "unknown": (150, 150, 150)}
+FLOOR_RGB, WALL_RGB = (168, 150, 126), (236, 233, 226) # RoomGenBench assemble.FLOOR_RGBA / WALL_RGBA
+FRONT = "#1a1a1a"
+NUMBERS_ONLY_ABOVE = 25
+PANEL_W, PANEL_H, PLAN_PX, VIEW_PX, DPI = 1536, 1024, 1024, 512, 100
+PLAN_MARGIN_M = .9
+VIEW_FOV_DEG, NEAR_M = 50., .05
+LIGHT = np.array((.3, .5, .8)) / np.linalg.norm((.3, .5, .8))
+
+
+def _jsonl(path):
+ with Path(path).open() as stream:
+ return [json.loads(line) for line in stream if line.strip()]
+
+
+def load(comparison):
+ """The 300 frozen rows and, per method, each row's layout (None when the method returned none)."""
+ base = Path(comparison)
+ rows = _jsonl(base / ROWS_FILE)
+ layouts = {"gt": [{"schema_version": r["target"]["schema_version"],
+ "objects": [{k: o[k] for k in KEYS} for o in r["target"]["objects"]]} for r in rows]}
+ for method, name in SOURCES.items():
+ records = _jsonl(base / name)
+ if method.startswith("ff-"):
+ by_row = {r["row"]: r.get("raw_prediction") for r in records}
+ records = [by_row.get(i) for i in range(len(rows))]
+ else:
+ records = [r.get("layout") for r in records]
+ if len(records) != len(rows):
+ raise ValueError(f"{name}: {len(records)} records for {len(rows)} rows")
+ layouts[method] = [x if isinstance(x, dict) else None for x in records]
+ return rows, layouts
+
+
+def _room(row):
+ room = row["condition"]["room"]
+ polygon = np.asarray(room["floor_polygon_xy_m"], float)
+ return polygon, polygon.min(0), polygon.max(0), room.get("floor_z_m") or 0., room.get("height_m") or DEFAULT_HEIGHT_M
+
+
+def anchors(row, index, gt, seed=0):
+ """Layouts derived from the ground truth (its sizes kept); the same (seed, row) gives the same layouts.
+
+ room-centre: every bottom centre at the floor-level room centre. random: uniform XY whose rotated footprint fits
+ the room's XY bounds, uniform yaw, ground-truth height above the floor. shuffled: the objects pass their bottom
+ centres and yaws along one random cycle (nobody keeps its own). yaw-flipped: yaw + pi. mirrored: x -> xmin + xmax - x,
+ yaw -> pi - yaw. identical: the ground truth itself.
+ """
+ _, lo, hi, floor, _ = _room(row)
+ objs = gt["objects"]
+ centre = [float(v) for v in (lo + hi) / 2]
+ rng = np.random.default_rng([seed, index, ANCHORS.index("random")])
+ random = []
+ for o in objs:
+ yaw = float(rng.uniform(-math.pi, math.pi))
+ c, s = abs(math.cos(yaw)), abs(math.sin(yaw))
+ sx, sy = o["target_size_local_m"][:2]
+ half = (.5 * (c * sx + s * sy), .5 * (s * sx + c * sy))
+ xy = [float(rng.uniform(a + h, b - h)) if b - a > 2 * h else float((a + b) / 2) for a, b, h in zip(lo, hi, half)]
+ random.append({**o, "bottom_center_m": [*xy, o["bottom_center_m"][2]], "yaw_rad": yaw})
+ order = np.random.default_rng([seed, index, ANCHORS.index("shuffled")]).permutation(len(objs))
+ donor = {int(a): objs[int(b)] for a, b in zip(order, np.roll(order, -1))}
+ layouts = {
+ "room-centre": [{**o, "bottom_center_m": [*centre, floor]} for o in objs],
+ "random": random,
+ "shuffled": [{**o, "bottom_center_m": list(donor[i]["bottom_center_m"]), "yaw_rad": donor[i]["yaw_rad"]}
+ for i, o in enumerate(objs)],
+ "yaw-flipped": [{**o, "yaw_rad": wrap_yaw(o["yaw_rad"] + math.pi)} for o in objs],
+ "mirrored": [{**o, "bottom_center_m": [float(lo[0] + hi[0]) - o["bottom_center_m"][0], *o["bottom_center_m"][1:]],
+ "yaw_rad": wrap_yaw(math.pi - o["yaw_rad"])} for o in objs],
+ "identical": deepcopy(objs)}
+ return {name: {"schema_version": gt["schema_version"], "objects": objects} for name, objects in layouts.items()}
+
+
+def method_layout(rows, layouts, index, method, seed):
+ if method in layouts:
+ return layouts[method][index]
+ return anchors(rows[index], index, layouts["gt"][index], seed)[method]
+
+
+def corners_xy(size, pos, yaw):
+ """validation.footprint's corners: local (-sx/2,-sy/2), (sx/2,-sy/2), (sx/2,sy/2), (-sx/2,sy/2) rotated by yaw
+ about the bottom centre; corners 1 -> 2 are the front edge (local +X)."""
+ c, s = math.cos(yaw), math.sin(yaw)
+ hx, hy = size[0] / 2, size[1] / 2
+ return np.array([(pos[0] + c * px - s * py, pos[1] + s * px + c * py)
+ for px, py in ((-hx, -hy), (hx, -hy), (hx, hy), (-hx, hy))])
+
+
+def front_arrow(size, pos, yaw):
+ """(tail, head): from the footprint centre to the middle of the front edge, i.e. along local +X."""
+ tail = np.asarray(pos[:2], float)
+ return tail, tail + size[0] / 2 * np.array((math.cos(yaw), math.sin(yaw)))
+
+
+def by_height(its):
+ """Drawing order: by top height, then request number."""
+ return sorted(its, key=lambda it: (it["pos"][2] + it["size"][2], it["n"]))
+
+
+def items(row, layout):
+ """Request-ordered boxes (n from 1) with their RoomGen place, and the RoomGen scene; invalid layouts raise."""
+ scene = layout_to_roomgenbench(row["condition"], layout)
+ place = {o["id"]: o["place"] for o in scene["objects"]}
+ by_id = {o["id"]: o for o in layout["objects"]}
+ return [{"n": n, "category": r["category"], "place": place[r["id"]], "size": by_id[r["id"]]["target_size_local_m"],
+ "pos": by_id[r["id"]]["bottom_center_m"], "yaw": by_id[r["id"]]["yaw_rad"]}
+ for n, r in enumerate(row["condition"]["objects"], 1)], scene
+
+
+def facing(row, layout):
+ """The wall-facing statistic of the investigation's facing.py: floor objects (bottom within 5 cm of the floor)
+ whose axis-aligned footprint bound lies within 0.15 m of exactly one wall of the room's XY bounds and whose front
+ is within ~45 deg of that wall's normal; returns (front into the room, scored)."""
+ _, lo, hi, floor, _ = _room(row)
+ into = total = 0
+ for o in layout["objects"]:
+ (sx, sy, _), (x, y, z), t = o["target_size_local_m"], o["bottom_center_m"], o["yaw_rad"]
+ if z - floor > .05:
+ continue
+ c, s = abs(math.cos(t)), abs(math.sin(t))
+ hx, hy = .5 * (c * sx + s * sy), .5 * (s * sx + c * sy)
+ gaps = {(1, 0): x - hx - lo[0], (-1, 0): hi[0] - x - hx, (0, 1): y - hy - lo[1], (0, -1): hi[1] - y - hy}
+ near = [n for n, g in gaps.items() if abs(g) <= .15 + 1e-9] # rounded coordinates land exactly on 0.15
+ if len(near) != 1:
+ continue
+ dot = math.cos(t) * near[0][0] + math.sin(t) * near[0][1]
+ if abs(dot) < .7:
+ continue
+ total, into = total + 1, into + (dot > 0)
+ return into, total
+
+
+def checks(row, layout, scene):
+ report = validate_scene(row["condition"], layout["objects"])
+ violations = Counter(c["code"] for c in report["checks"] if c["status"] == "violation")
+ places = Counter(o["place"] for o in scene["objects"])
+ return {"clean": not violations, "violations": dict(sorted(violations.items())), "places": dict(sorted(places.items())),
+ "require_placement": "unknown" not in places, "wall_backed_floor_facing_in": list(facing(row, layout))}
+
+
+def plan_extent(row):
+ """(x0, x1, y0, y1) of the square plan: the room's XY bounds plus PLAN_MARGIN_M around the longer side."""
+ _, lo, hi, _, _ = _room(row)
+ c, half = (lo + hi) / 2, float((hi - lo).max()) / 2 + PLAN_MARGIN_M
+ return c[0] - half, c[0] + half, c[1] - half, c[1] + half
+
+
+def cameras(row):
+ """[(eye, target)] at two opposite upper corners, aimed at the room centre at 0.4 * height (height 2.8 m when
+ unknown): along the line through the design's eye (1 m outside the corner on the room diagonal, at room height
+ + 1.2 m), backed off as RoomGenBench render.py does until the room's bounding sphere fills the field of view, so
+ the whole floor is in frame (from the design's eye itself, up to 32% of the floor fell outside the view)."""
+ _, lo, hi, floor, height = _room(row)
+ centre = (lo + hi) / 2
+ target = np.array([*centre, floor + .4 * height])
+ dist = .5 * math.hypot(*(hi - lo), height) / math.sin(math.radians(VIEW_FOV_DEG) / 2)
+ out = []
+ for corner in (lo, hi):
+ d = (corner - centre) / np.linalg.norm(corner - centre)
+ ray = np.array([*(corner + d), floor + height + 1.2]) - target
+ out.append((target + ray / np.linalg.norm(ray) * dist, target))
+ return out
+
+
+def project(points, eye, target, size=VIEW_PX):
+ """World points -> (pixel x, pixel y, depth along the view axis): pinhole, square image, VIEW_FOV_DEG vertical."""
+ f = (target - eye) / np.linalg.norm(target - eye)
+ r = np.cross(f, (0., 0., 1.))
+ r /= np.linalg.norm(r)
+ u = np.cross(r, f)
+ rel = np.asarray(points, float) - eye
+ depth = rel @ f
+ k = size / 2 / math.tan(math.radians(VIEW_FOV_DEG) / 2)
+ with np.errstate(divide="ignore", invalid="ignore"):
+ return size / 2 + k * (rel @ r) / depth, size / 2 - k * (rel @ u) / depth, depth
+
+
+def _shade(rgb, normal, factor=1.):
+ return np.asarray(rgb) / 255 * (.62 + .38 * max(0., float(np.dot(normal, LIGHT)))) * factor
+
+
+def view_primitives(row, its, eye):
+ """[(convex polygon (k, 3), rgb 0-1)]: floor, the walls whose inside faces the eye, then the boxes (top + 4 sides,
+ in top-height order; the front face darker)."""
+ polygon, _, _, floor, height = _room(row)
+ prims = [(np.c_[polygon, np.full(len(polygon), floor)], _shade(FLOOR_RGB, (0, 0, 1)))]
+ ccw = sum(x0 * y1 - x1 * y0 for (x0, y0), (x1, y1) in zip(polygon, np.roll(polygon, -1, 0))) > 0
+ for a, b in zip(polygon, np.roll(polygon, -1, 0)):
+ dx, dy = b - a
+ outward = np.array((dy, -dx) if ccw else (-dy, dx)) / math.hypot(dx, dy)
+ if np.dot(outward, eye[:2] - a) > 0: # the eye is outside this wall: removed, as RoomGenBench render.py
+ continue
+ prims.append((np.array([[*a, floor], [*b, floor], [*b, floor + height], [*a, floor + height]]),
+ _shade(WALL_RGB, (*-outward, 0))))
+ for it in by_height(its):
+ xy = corners_xy(it["size"], it["pos"], it["yaw"])
+ z0, z1 = it["pos"][2], it["pos"][2] + it["size"][2]
+ v = np.r_[np.c_[xy, np.full(4, z0)], np.c_[xy, np.full(4, z1)]]
+ c, s = math.cos(it["yaw"]), math.sin(it["yaw"])
+ rgb = PLACE_RGB[it["place"]]
+ prims += [(v[[4, 5, 6, 7]], _shade(rgb, (0, 0, 1))), (v[[1, 2, 6, 5]], _shade(rgb, (c, s, 0), .55)),
+ (v[[3, 0, 4, 7]], _shade(rgb, (-c, -s, 0))), (v[[2, 3, 7, 6]], _shade(rgb, (-s, c, 0))),
+ (v[[0, 1, 5, 4]], _shade(rgb, (s, -c, 0)))]
+ return prims
+
+
+def _fill(img, inv, ids, x, y, iz, rgb, ident):
+ """One triangle into the buffers; inv holds 1/depth (exactly linear in screen space), larger is nearer."""
+ h, w = inv.shape
+ x0, x1 = max(int(math.floor(x.min())), 0), min(int(math.ceil(x.max())), w)
+ y0, y1 = max(int(math.floor(y.min())), 0), min(int(math.ceil(y.max())), h)
+ area = (x[1] - x[0]) * (y[2] - y[0]) - (y[1] - y[0]) * (x[2] - x[0])
+ if x0 >= x1 or y0 >= y1 or abs(area) < 1e-9:
+ return
+ X, Y = np.meshgrid(np.arange(x0, x1) + .5, np.arange(y0, y1) + .5)
+ w0 = ((x[2] - x[1]) * (Y - y[1]) - (y[2] - y[1]) * (X - x[1])) / area
+ w1 = ((x[0] - x[2]) * (Y - y[2]) - (y[0] - y[2]) * (X - x[2])) / area
+ w2 = 1 - w0 - w1
+ depth = w0 * iz[0] + w1 * iz[1] + w2 * iz[2]
+ window = np.s_[y0:y1, x0:x1]
+ # at equal depth (coplanar faces, e.g. two overlapping tops of one height) the later face wins, as in the plan,
+ # instead of rounding noise picking pixel by pixel
+ win = (w0 >= 0) & (w1 >= 0) & (w2 >= 0) & (depth >= inv[window] * (1 - 1e-9))
+ inv[window][win], img[window][win], ids[window][win] = depth[win], rgb, ident
+
+
+def rasterize(prims, eye, target, size=VIEW_PX):
+ """z-buffer of convex polygons (fan triangulated) on white, 1 px dark lines where the visible face changes;
+ returns (uint8 image, face id buffer: the index into prims, -1 for background)."""
+ img, inv, ids = np.ones((size, size, 3)), np.zeros((size, size)), np.full((size, size), -1)
+ for ident, (poly, rgb) in enumerate(prims):
+ x, y, d = project(poly, eye, target, size)
+ if (d < NEAR_M).any(): # ponytail: drops a polygon crossing the near plane instead of clipping it
+ continue
+ for t in range(1, len(poly) - 1):
+ tri = [0, t, t + 1]
+ _fill(img, inv, ids, x[tri], y[tri], 1 / d[tri], rgb, ident)
+ edge = np.zeros(ids.shape, bool)
+ edge[:, 1:] |= ids[:, 1:] != ids[:, :-1]
+ edge[1:] |= ids[1:] != ids[:-1]
+ img[edge] *= .3
+ return (img * 255).round().astype(np.uint8), ids
+
+
+def view(row, its, eye, target):
+ """(image, [(n, x, y)]): one corner view and the boxes whose top-face centre shows their own top face."""
+ prims = view_primitives(row, its, eye)
+ img, ids = rasterize(prims, eye, target)
+ first = len(prims) - 5 * len(its) # after the floor and walls, each box adds its top then its four sides
+ top = {it["n"]: first + 5 * k for k, it in enumerate(by_height(its))}
+ tops = [[*it["pos"][:2], it["pos"][2] + it["size"][2]] for it in its]
+ return img, [(it["n"], x, y) for it, x, y, d in zip(its, *project(tops, eye, target))
+ if d > NEAR_M and 0 <= x < VIEW_PX and 0 <= y < VIEW_PX and ids[int(y), int(x)] == top[it["n"]]]
+
+
+def draw_plan(ax, row, its):
+ from matplotlib import patheffects
+ from matplotlib.patches import Polygon
+ polygon, lo, hi, _, _ = _room(row)
+ x0, x1, y0, y1 = plan_extent(row)
+ ax.set_xlim(x0, x1)
+ ax.set_ylim(y0, y1)
+ ax.set_aspect("equal")
+ ax.axis("off")
+ ppm = PLAN_PX / (x1 - x0)
+ for g in np.arange(math.ceil(lo[0] / .5) * .5, hi[0], .5):
+ ax.plot([g, g], [lo[1], hi[1]], color="#dddddd", lw=.6, zorder=1)
+ for g in np.arange(math.ceil(lo[1] / .5) * .5, hi[1], .5):
+ ax.plot([lo[0], hi[0]], [g, g], color="#dddddd", lw=.6, zorder=1)
+ ax.add_patch(Polygon(polygon, closed=True, fill=False, ec="black", lw=3, zorder=2))
+ for z, it in enumerate(by_height(its), start=3):
+ corners = corners_xy(it["size"], it["pos"], it["yaw"])
+ ax.add_patch(Polygon(corners, closed=True, fc=np.asarray(PLACE_RGB[it["place"]]) / 255, ec="#303030", lw=.8, zorder=z))
+ ax.plot(*corners[1:3].T, color=FRONT, lw=2.5, solid_capstyle="butt", zorder=z)
+ tail, head = front_arrow(it["size"], it["pos"], it["yaw"])
+ ax.annotate("", head, tail, zorder=z, arrowprops={"arrowstyle": "-|>", "color": FRONT, "lw": 1.2, "shrinkA": 0,
+ "shrinkB": 0, "mutation_scale": float(np.clip(.25 * it["size"][0] * ppm, 4, 12))})
+ renderer, pad = ax.figure.canvas.get_renderer(), 2 / ppm # pad: the labels' white halo
+
+ def extent(artist): # (centre x, centre y, half width, half height) in metres, the text as rendered plus pad
+ b = artist.get_window_extent(renderer).transformed(ax.transData.inverted())
+ return (b.x0 + b.x1) / 2, (b.y0 + b.y1) / 2, b.width / 2 + pad, b.height / 2 + pad
+
+ cx, yb = (lo[0] + hi[0]) / 2, lo[1] - PLAN_MARGIN_M / 2
+ ax.plot([cx - .5, cx + .5], [yb, yb], color="black", lw=4, solid_capstyle="butt")
+ placed = [(cx, yb, .5 + pad, 3 / ppm), extent(ax.text(cx, yb - .06, "1 m", ha="center", va="top", fontsize=10))]
+ centre, half = (lo + hi) / 2, np.linalg.norm(hi - lo) / 2
+ for k, (eye, _) in enumerate(cameras(row), 1): # 0.45 m outside the room corner the view looks from
+ x, y = centre + (eye[:2] - centre) / np.linalg.norm(eye[:2] - centre) * (half + .45)
+ placed.append(extent(ax.text(x, y, f"V{k}", ha="center", va="center", fontsize=11, weight="bold", color="#555555")))
+ halo = [patheffects.withStroke(linewidth=3, foreground="white")]
+ numbers_only = len(its) > NUMBERS_ONLY_ABOVE
+ size = 7.5 if numbers_only else 8.5
+ grid = [(i, j) for i in range(-8, 9) for j in range(-30, 31)]
+ for it in by_height(its): # at its box centre, else the nearest free slot (whole label widths / lines away)
+ x, y = it["pos"][:2]
+ label = ax.text(x, y, str(it["n"]) if numbers_only else f'{it["n"]} {it["category"]}', fontsize=size,
+ ha="center", va="center", zorder=10_000, path_effects=halo)
+ _, _, w, h = extent(label)
+ for i, j in sorted(grid, key=lambda o: math.hypot(o[0] * w, o[1] * h)):
+ sx, sy = x + 2 * i * w, y + 2 * j * h
+ if (x0 + w <= sx <= x1 - w and y0 + h <= sy <= y1 - h
+ and not any(abs(sx - px) < w + pw and abs(sy - py) < h + ph for px, py, pw, ph in placed)):
+ break
+ else: # ponytail: no free slot within 8 widths / 30 lines: overlap at the box
+ sx, sy = x, y
+ label.set_position((sx, sy))
+ placed.append((sx, sy, w, h))
+
+
+def render_panel(path, row, its):
+ import matplotlib
+ matplotlib.use("Agg")
+ from matplotlib import patheffects
+ import matplotlib.pyplot as plt
+ fig = plt.figure(figsize=(PANEL_W / DPI, PANEL_H / DPI), dpi=DPI)
+ draw_plan(fig.add_axes((0, 0, PLAN_PX / PANEL_W, 1)), row, its)
+ halo = [patheffects.withStroke(linewidth=2.5, foreground="white")]
+ for k, (eye, target) in enumerate(cameras(row)):
+ ax = fig.add_axes((PLAN_PX / PANEL_W, .5 - .5 * k, VIEW_PX / PANEL_W, VIEW_PX / PANEL_H))
+ img, labels = view(row, its, eye, target)
+ ax.imshow(img, interpolation="nearest", extent=(0, VIEW_PX, VIEW_PX, 0))
+ ax.set_xlim(0, VIEW_PX)
+ ax.set_ylim(VIEW_PX, 0)
+ ax.axis("off")
+ for n, x, y in labels:
+ ax.text(x, y, str(n), fontsize=7, ha="center", va="center", path_effects=halo)
+ ax.text(8, 8, f"V{k + 1}", ha="left", va="top", fontsize=11, weight="bold", color="#555555")
+ fig.savefig(path, dpi=DPI, metadata={"Software": None})
+ plt.close(fig)
+
+
+def panel_name(row_index, method):
+ return hashlib.sha256(f"{SALT}|{row_index}|{method}".encode()).hexdigest()[:16]
+
+
+def cmd_render(args):
+ rows, layouts = load(args.comparison_dir)
+ out = Path(args.out)
+ (out / "panels").mkdir(parents=True, exist_ok=True)
+ manifest = {"schema_version": "fastfill.vlm-judge-render.v1", "script_sha256": hashlib.sha256(Path(__file__).read_bytes()).hexdigest(),
+ "comparison_dir": str(Path(args.comparison_dir).resolve()), "seed": args.seed, "rows": {}, "panels": {}, "missing": []}
+ for r in args.rows:
+ row = rows[r]
+ polygon, lo, hi, _, _ = _room(row)
+ manifest["rows"][str(r)] = {
+ "room_type": row["condition"]["room"].get("room_type"), "source": row["provenance"]["source"],
+ "size_m": [float(v) for v in (*(hi - lo), _room(row)[4])], "height_assumed": row["condition"]["room"].get("height_m") is None,
+ "legend": [{"n": n, "id": o["id"], "category": o["category"], "description": o["description"][:80]}
+ for n, o in enumerate(row["condition"]["objects"], 1)]}
+ for method in args.methods:
+ layout = method_layout(rows, layouts, r, method, args.seed)
+ try:
+ if layout is None:
+ raise ValueError("no layout")
+ its, scene = items(row, layout)
+ except ValueError as error:
+ manifest["missing"].append({"row": r, "method": method, "reason": str(error)})
+ continue
+ name = panel_name(r, method)
+ render_panel(out / "panels" / f"{name}.png", row, its)
+ manifest["panels"][name] = {"row": r, "method": method, "file": f"panels/{name}.png", "checks": checks(row, layout, scene)}
+ print(f"row {r} {method} -> {name}", flush=True)
+ (out / "manifest.json").write_text(json.dumps(manifest, indent=1) + "\n")
+
+
+def cmd_anchors(args):
+ rows, layouts = load(args.comparison_dir)
+ out = Path(args.out)
+ out.mkdir(parents=True, exist_ok=True)
+ with (out / "anchors.jsonl").open("w") as stream:
+ for r in args.rows:
+ for name, layout in anchors(rows[r], r, layouts["gt"][r], args.seed).items():
+ stream.write(json.dumps({"row": r, "anchor": name, "seed": args.seed, "layout": layout}) + "\n")
+
+
+def cmd_handoff(args):
+ rows, layouts = load(args.comparison_dir)
+ root = Path(args.out) / "handoff"
+ root.mkdir(parents=True, exist_ok=False)
+ results = []
+ for r in args.rows:
+ for method in args.methods:
+ entry, layout = {"row": r, "method": method}, layouts[method][r]
+ if layout is None:
+ results.append({**entry, "pass": False, "stage": "no_layout"})
+ continue
+ directory = root / f"row{r:03d}-{method}"
+ try:
+ export_handoff(directory / "export", rows[r]["condition"], layout)
+ except ValueError as error:
+ results.append({**entry, "pass": False, "stage": "export", "message": str(error)})
+ continue
+ command = [sys.executable, "-m", "fastfill.v2.roomgenbench", "--handoff", str(directory / "export"),
+ "--output-dir", str(directory / "assembly"), "--method", "layout_boxes", "--require-placement",
+ "--roomgenbench-root", str(Path(args.roomgenbench_root).resolve())]
+ done = subprocess.run(command, cwd=REPO, capture_output=True, text=True)
+ lines = (done.stdout if done.returncode == 0 else done.stderr).strip().splitlines()
+ results.append({**entry, "pass": done.returncode == 0, "stage": "assembly", "returncode": done.returncode,
+ "message": lines[-1] if lines else "", "directory": str(directory)})
+ print(json.dumps(results[-1]), flush=True)
+ (root / "results.json").write_text(json.dumps(results, indent=1) + "\n")
+
+
+def cmd_contact(args):
+ out = Path(args.out)
+ manifest = json.loads((out / "manifest.json").read_text())
+ handoff_file = out / "handoff" / "results.json"
+ handoff = {(e["row"], e["method"]): e for e in json.loads(handoff_file.read_text())} if handoff_file.is_file() else {}
+ order = METHODS + ANCHORS
+ sections = []
+ for r, info in manifest["rows"].items():
+ r = int(r)
+ panels = sorted((p | {"hash": h} for h, p in manifest["panels"].items() if p["row"] == r), key=lambda p: order.index(p["method"]))
+ cards = []
+ for p in panels:
+ c = p["checks"]
+ into, total = c["wall_backed_floor_facing_in"]
+ lines = [f"validate_scene: {'clean' if c['clean'] else ', '.join(f'{k} {v}' for k, v in c['violations'].items())}",
+ "places: " + ", ".join(f"{k} {v}" for k, v in c["places"].items()),
+ f"require-placement: {'pass' if c['require_placement'] else 'FAIL'}",
+ f"wall-backed floor objects facing in: {into}/{total}"]
+ if (r, p["method"]) in handoff:
+ e = handoff[(r, p["method"])]
+ lines.append("RoomGen layout_boxes --require-placement assembly: " + (
+ "pass" if e["pass"] else f"FAIL ({html.escape(e.get('message', e['stage']))})"))
+ cards.append(f'{html.escape(NAMES[p["method"]])} {p["hash"]}
'
+ f'
'
+ + "".join(f"- {line}
" for line in lines) + "
{html.escape(NAMES[m["method"]])}
not rendered: {html.escape(m["reason"])}
Row {r}: {html.escape(str(info["room_type"]))} ({html.escape(info["source"])}), '
+ f'{sx:.2f} x {sy:.2f} x {sz:.2f} m{" (height assumed)" if info["height_assumed"] else ""}, '
+ f'{len(info["legend"])} objects
legend
{legend}
{"".join(cards)}
')
+ page = ("VLM judge pilot panelsVLM judge pilot: rendered layouts
"
+ "Colours: blue floor, green on an object, orange wall, grey unknown place. Dark edge and arrow: front (local +X). "
+ "V1/V2: the corner views. Operator sheet: method names are shown here only, never to the judge.
"
+ + "".join(sections) + "\n")
+ (out / "contact.html").write_text(page)
+
+
+def main(argv=None):
+ parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+ parser.add_argument("command", choices=("render", "anchors", "handoff", "contact"))
+ parser.add_argument("--out", required=True, help="output directory (panels/, manifest.json, anchors.jsonl, handoff/, contact.html)")
+ parser.add_argument("--comparison-dir", default=str(COMPARISON))
+ parser.add_argument("--rows", type=lambda s: [int(x) for x in s.split(",")], default=list(PILOT_ROWS))
+ parser.add_argument("--methods", type=lambda s: s.split(","), help="default: every method and anchor (handoff: methods only)")
+ parser.add_argument("--seed", type=int, default=0)
+ parser.add_argument("--roomgenbench-root", default=str(REPO / "RoomGenBench"))
+ args = parser.parse_args(argv)
+ allowed = METHODS if args.command == "handoff" else METHODS + ANCHORS
+ args.methods = args.methods or list(allowed)
+ if set(args.methods) - set(allowed):
+ parser.error(f"--methods must be among {allowed}")
+ {"render": cmd_render, "anchors": cmd_anchors, "handoff": cmd_handoff, "contact": cmd_contact}[args.command](args)
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/code/fastfill/v2/predict.py b/code/fastfill/v2/predict.py
new file mode 100644
index 0000000000000000000000000000000000000000..bcc1aa5d3a9895368067be1f7b3e71f4c8ae08aa
--- /dev/null
+++ b/code/fastfill/v2/predict.py
@@ -0,0 +1,113 @@
+"""Direct bbox checkpoint inference; the legacy asset loop is explicitly optional.
+
+The checkpoint binds its own settings: ``load_model`` applies ``model_config.json``
+(position head and grid, size reference, yaw bins, ...) and ``--max-length``
+defaults to the training ``max_length`` recorded beside the export
+(``io.load_checkpoint_config``); only a model directory with no run record falls
+back to 4096. An explicit ``--max-length`` always wins.
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import os
+from pathlib import Path
+import time
+
+import torch
+
+from fastfill.v2.batch import load_tokenizer
+from fastfill.v2.evaluate import DEFAULT_MAX_LENGTH, GRID_DECODES, predict_layout
+from fastfill.v2.io import load_checkpoint_config, safe_output
+from fastfill.v2.model import load_model
+from fastfill.v2.schema import validate_condition, validate_layout
+
+
+def main(argv=None):
+ p = argparse.ArgumentParser(description=__doc__)
+ p.add_argument("--checkpoint", type=Path, required=True)
+ inputs = p.add_mutually_exclusive_group(required=True)
+ inputs.add_argument("--condition", type=Path)
+ inputs.add_argument("--request", type=Path, help="room_type + room_size_m + furniture_list; no assets")
+ p.add_argument("--room-size-semantics", choices=["rectangular", "reference_extent"], default="rectangular",
+ help="For --request: known rectangular room, or a source XY reference range with unknown physical boundary/floor")
+ p.add_argument("--output", type=Path, required=True, help="New JSON file, never overwrite input")
+ p.add_argument("--baseline", choices=["structured", "text"], default="structured")
+ p.add_argument("--catalog", type=Path)
+ p.add_argument("--export-dir", type=Path, help="New bbox JSON/GLB/SVG handoff directory; no assets")
+ p.add_argument("--device", default="cpu")
+ p.add_argument("--grid-decode", choices=GRID_DECODES, default="spread",
+ help="grid position head: collision-aware spread (default) or plain per-object argmax")
+ p.add_argument("--max-length", type=int, help="default: the checkpoint's training max_length")
+ p.add_argument("--max-new-tokens", type=int, default=2048)
+ p.add_argument("--max-asset-retries", type=int, default=2)
+ p.add_argument("--max-repair-calls", type=int, default=0)
+ p.add_argument("--repair-step-m", type=float, default=.25)
+ p.add_argument("--max-seconds", type=float, default=10.)
+ p.add_argument("--commit-in-memory", action="store_true")
+ args = p.parse_args(argv)
+ if args.condition and args.room_size_semantics != "rectangular":
+ raise ValueError("--room-size-semantics applies only to --request; --condition declares its own room semantics")
+ output = safe_output(args.output)
+ if args.request and args.catalog:
+ raise ValueError("direct furniture requests export bbox geometry and do not resolve assets")
+ if args.export_dir and args.catalog:
+ raise ValueError("bbox handoff exports raw predictions; omit --catalog")
+ if args.export_dir:
+ handoff = safe_output(args.export_dir)
+ if output == handoff or output.is_relative_to(handoff) or handoff.is_relative_to(output):
+ raise ValueError("--output and --export-dir must not overlap or contain each other")
+ if args.request:
+ from fastfill.v2.direct_layout import request_to_condition
+ condition = request_to_condition(json.loads(args.request.read_text()),
+ room_size_semantics=args.room_size_semantics)
+ else:
+ condition = json.loads(args.condition.read_text())
+ validate_condition(condition)
+ if args.commit_in_memory and args.catalog is None:
+ raise ValueError("commit requires an actual asset catalog")
+ if args.max_length is None:
+ args.max_length = load_checkpoint_config(args.checkpoint)["max_length"] or DEFAULT_MAX_LENGTH
+ torch.set_num_threads(int(os.environ.get("OMP_NUM_THREADS", "2"))) # CPU inference may use more threads
+ start = time.perf_counter()
+ if args.baseline == "structured":
+ model = load_model(args.checkpoint, device=args.device)
+ tokenizer = load_tokenizer("tiny" if model.config.backbone == "tiny" else str(args.checkpoint.parent / "tokenizer"), local_files_only=True)
+ inference_start = time.perf_counter()
+ layout = predict_layout(model, tokenizer, condition, max_length=args.max_length, device=args.device,
+ grid_decode=args.grid_decode)
+ else:
+ from fastfill.v2.text_sft import generate_text, load_text_model
+ model, tokenizer = load_text_model(args.checkpoint, device=args.device)
+ inference_start = time.perf_counter()
+ layout = json.loads(generate_text(model, tokenizer, condition, max_length=args.max_length,
+ max_new_tokens=args.max_new_tokens, device=args.device))
+ validate_layout(layout, condition)
+ inference_ms = (time.perf_counter() - inference_start) * 1000
+ result = layout
+ if args.catalog:
+ from fastfill.v2.runtime import AtomicMemoryHost, BoundedTranslationRepair, RuntimeBudget, run_pipeline
+ from fastfill.v2.serve import load_catalog
+ host = AtomicMemoryHost() if args.commit_in_memory else None
+ result = run_pipeline(condition, layout, load_catalog(args.catalog), host=host,
+ budget=RuntimeBudget(max_asset_retries=args.max_asset_retries, max_repair_calls=args.max_repair_calls, max_seconds=args.max_seconds),
+ repair=BoundedTranslationRepair(args.repair_step_m) if args.max_repair_calls else None,
+ expected_world_version=0 if host else None, idempotency_key="cli-prediction")
+ result = {**result, "fastfill_latency_ms": inference_ms,
+ "cold_start_total_ms": (time.perf_counter() - start) * 1000}
+ output.parent.mkdir(parents=True, exist_ok=True)
+ with output.open("x") as stream:
+ json.dump(result, stream, indent=2, allow_nan=False)
+ stream.write("\n")
+ if args.export_dir:
+ from fastfill.v2.direct_layout import export_handoff
+ export_handoff(args.export_dir, condition, layout)
+ print(json.dumps({"output": str(output), "requested_objects": len(condition["objects"]),
+ "fastfill_latency_ms": inference_ms,
+ "runtime_ok": result["ok"] if args.catalog else None,
+ "committed": result.get("committed") if args.catalog else None}))
+ return 0 if args.catalog is None or result["ok"] else 2
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/code/fastfill/v2/qualified_data.py b/code/fastfill/v2/qualified_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a595b1312109ad90a5ff8fc3fc4564206458a03
--- /dev/null
+++ b/code/fastfill/v2/qualified_data.py
@@ -0,0 +1,217 @@
+"""New full-condition research revision: mask uncertain labels, preserve geometry.
+
+The three-field reference-extent dataset is a separate ablation. This main view
+retains room polygons, existing objects, support, identity and constraints.
+"""
+from __future__ import annotations
+
+import argparse
+from collections import Counter
+from contextlib import ExitStack
+from copy import deepcopy
+import json
+from pathlib import Path
+import shutil
+import tempfile
+
+from .io import fingerprint, safe_output
+from .legacy_bridge import HEIGHT_TOLERANCE_M, height_conflict
+from .multisource_data import (DEGENERATE_AXIS_M, HOLDOUT_REASON, ROOMGENBENCH_HOLDOUT_GROUPS, SPLITS, SOURCES,
+ _frozen_ir_guard, _holdout, _input_hashes, _loads, _parent_rows, _size_policy,
+ _split_integrity, _write, qualify_parent_masks)
+from .schema import migrate_legacy_row, validate_condition
+
+POLICY = "full-condition-mask-review-v1"
+YAW_POLICY = "inherit parent yaw validity and yaw_symmetry_order; no geometric promotion"
+PARENT_HASHES = {
+ "manifest.json": "7135a59f097e665ec6bb0973aae0233f323be447556e4ef903b868afbe779652",
+ "train.jsonl": "ad885654907242e9cb7b222653c9da5f9a5237c5586215e7ced94ed74c830c92",
+ "validation.jsonl": "5a0a45d6b2d82f8fcbf3a1f2d7f908ce9531e484efdac870b869dd9164a5b773",
+ "test.jsonl": "efc393bd36f1110ca3e037d89ddc70c60e7b6cd676bdc1c47b2f7f81e9648ea9",
+ "rejections.jsonl": "02925d15b20a613807ada2f8741c851c24c1b036720feaa8b1c0fadc8317f95a",
+}
+
+
+def qualify_sample(parent, *, model_config=None, holdout_groups=()):
+ """Pure full-task derivation; never promote yaw or rewrite target numbers."""
+ parent = migrate_legacy_row(parent)
+ source = parent["provenance"]["source"]
+ split = parent["provenance"]["split"]
+ if source not in SOURCES or split not in SPLITS or source in {"SceneSmith", "SpatialGen"} and split != "test":
+ raise ValueError("unknown source or evaluation-only split violation")
+ if "dataset_qualification_policy" in parent["provenance"]:
+ raise ValueError("parent is already qualification-reviewed")
+ validate_condition(parent["condition"])
+ requests = [obj["id"] for obj in parent["condition"]["objects"]]
+ targets = [obj["id"] for obj in parent["target"]["objects"]]
+ source_ids = parent["provenance"]["target_source_ids"]
+ if (requests != targets or len(set(targets)) != len(targets)
+ or len(source_ids) != len(targets) or len(set(source_ids)) != len(source_ids)):
+ raise ValueError("target identity/order must equal stable parent request order")
+ masks, changes = qualify_parent_masks(parent, model_config=model_config)
+ row = deepcopy(parent)
+ row["validity"] = {**row["validity"], **masks}
+ added = {"dataset_qualification_policy": POLICY, "qualification_changes": changes}
+ if source == "Scan2CAD":
+ room, meta = parent["condition"]["room"], parent["provenance"].get("source_meta", {})
+ row["condition"]["room"]["floor_known"] = False
+ added["qualification_changes"] = [{"field": "room.floor_known", "before": room.get("floor_known"),
+ "after": False, "reason": "Scan2CAD_floor_is_estimated_not_independent_physical_measurement"}, *changes]
+ added["estimated_floor_provenance"] = {"floor_source": "estimated",
+ "parent_floor_known": room.get("floor_known"), "parent_floor_z_m": room.get("floor_z_m"),
+ "source_meta_floor_z": meta.get("floor_z"), "source_meta_n_floor_snapped": meta.get("n_floor_snapped"),
+ "per_object_pre_snap_z": "unavailable_in_frozen_IR",
+ "per_object_snap_membership": "unknown_do_not_infer_from_zero_z"}
+ # K2 flag only: labels and the declared height stay. The flag follows the qualified masks; a
+ # differing parent (bridge) flag is replaced or removed and journaled with its value as ``before``.
+ before = parent["provenance"].get("height_conflict")
+ conflict = height_conflict(row["condition"]["room"].get("height_m"), parent["target"]["objects"],
+ masks["position"], masks["size"])
+ if conflict:
+ added["height_conflict"] = conflict
+ if conflict != before:
+ added["qualification_changes"] = [*added["qualification_changes"], {
+ "field": "provenance.height_conflict", "before": deepcopy(before), "after": deepcopy(conflict),
+ "reason": "target_exceeds_declared_height_flag_only" if before is None else "height_conflict_follows_qualified_masks"}]
+ # Position demotion can invalidate a previously legal exchangeable group.
+ # The protocol falls back to fixed identities; object count/roles stay intact.
+ groups = row["validity"].get("exchangeable_group", [])
+ invalid_groups = {g for i, g in enumerate(groups) if g is not None and not all(masks["position"][i])}
+ for i, group in enumerate(groups):
+ if group in invalid_groups:
+ row["validity"]["exchangeable_group"][i] = None
+ added["qualification_changes"] = [*added["qualification_changes"], {
+ "object_id": targets[i], "target_source_id": source_ids[i], "field": "exchangeable_group",
+ "before": group, "after": None, "reason": "mask_review_removed_complete_position_exchangeability"}]
+ provenance, moved = _holdout(row["provenance"], split, holdout_groups)
+ if moved:
+ added["qualification_changes"] = [*added["qualification_changes"], moved]
+ row["provenance"] = {**provenance, **added}
+ if not conflict:
+ row["provenance"].pop("height_conflict", None)
+ validate_condition(row["condition"])
+ return row
+
+
+def _protected(parent, target, ir):
+ roots = [parent, Path("/Volumes/harddisk/3D_Room_Collections").resolve()]
+ if ir is not None:
+ roots.append(Path(ir).resolve())
+ if any(target == root or root in target.parents or target in root.parents for root in roots):
+ raise ValueError("new output must be outside protected inputs and ancestors")
+
+
+def build_dataset(parent_root, output, *, expected_hashes=None, ir_root=None, frozen_manifest=None, model_config=None,
+ holdout_groups=ROOMGENBENCH_HOLDOUT_GROUPS):
+ """Publish only after parent pins, split integrity and before/after hashes pass."""
+ parent, target = Path(parent_root).resolve(), safe_output(output)
+ holdout_groups = tuple(holdout_groups)
+ _protected(parent, target, ir_root)
+ implementation_before = {str(path): fingerprint(path) for path in # legacy_bridge: the shared K2 height_conflict
+ (Path(__file__).resolve(), *(Path(__file__).with_name(name).resolve()
+ for name in ("multisource_data.py", "legacy_bridge.py")))}
+ pins = PARENT_HASHES if expected_hashes is None else expected_hashes
+ parent_inputs = {str(path): fingerprint(path) for path in [parent / (s + ".jsonl") for s in SPLITS]
+ + [parent / "manifest.json", parent / "rejections.jsonl"]}
+ if any(parent_inputs.get(str(parent / name)) != digest for name, digest in pins.items()):
+ raise ValueError("parent bytes differ from pinned reviewed inputs")
+ if expected_hashes is None and (ir_root is None or frozen_manifest is None):
+ raise ValueError("release build requires canonical frozen IR manifest and IR root")
+ evidence = {}
+ if ir_root is not None:
+ ir = Path(ir_root).resolve()
+ frozen = Path(frozen_manifest).resolve()
+ evidence = _input_hashes(parent, ir, frozen)
+ _frozen_ir_guard(ir, frozen, evidence)
+ before = {**parent_inputs, **evidence}
+ integrity = _split_integrity(parent)
+ parent_manifest = _loads((parent / "manifest.json").read_text())
+ counters = {key: Counter() for key in ("split_samples", "split_objects", "source_split_samples",
+ "source_split_objects", "validity_counts", "constraint_counts", "qualification_change_counts",
+ "fixed_objects_by_split", "source_split_validity", "supervised_yaw_scenes_by_split",
+ "source_yaw_symmetry_order_counts", "source_size_axis_swap_allowed_objects", "holdout_samples_by_parent_split")}
+ target.parent.mkdir(parents=True, exist_ok=True)
+ with tempfile.TemporaryDirectory(prefix=".qualified-build-", dir=target.parent) as temp:
+ stage = Path(temp)
+ with ExitStack() as stack:
+ changelog = stack.enter_context((stage / "changes.jsonl").open("x"))
+ destinations = {split: stack.enter_context((stage / (split + ".jsonl")).open("x")) for split in SPLITS}
+ for parent_split, line_number, parent_row in _parent_rows(parent, holdout_groups):
+ row = qualify_sample(parent_row, model_config=model_config, holdout_groups=holdout_groups)
+ split, source = row["provenance"]["split"], row["provenance"]["source"]
+ _write(destinations[split], row)
+ count = len(row["target"]["objects"])
+ for key, ident, n in (("split_samples", split, 1), ("split_objects", split, count),
+ ("source_split_samples", source + ":" + split, 1), ("source_split_objects", source + ":" + split, count),
+ ("fixed_objects_by_split", split, len(row["condition"]["room"].get("fixed_objects", []))),
+ ("holdout_samples_by_parent_split", parent_split, split != parent_split)):
+ counters[key][ident] += n
+ for field in ("position", "size", "yaw"):
+ n = sum(all(mask) if isinstance(mask, list) else mask for mask in row["validity"][field])
+ counters["validity_counts"][field] += n
+ counters["source_split_validity"][source + ":" + split + ":" + field] += n
+ counters["supervised_yaw_scenes_by_split"][split] += any(row["validity"]["yaw"])
+ for order in row["validity"].get("yaw_symmetry_order", []):
+ counters["source_yaw_symmetry_order_counts"][f"{source}:{order}"] += 1
+ counters["source_size_axis_swap_allowed_objects"][source] += sum(row["validity"]["size_axis_swap_allowed"])
+ for constraint in row["condition"].get("constraints", []):
+ counters["constraint_counts"][constraint["type"] + ":" + split] += 1
+ for change in row["provenance"]["qualification_changes"]:
+ counters["qualification_change_counts"][change["field"]] += 1
+ _write(changelog, {"uid": row["provenance"]["scene_id"], "source": source,
+ "split": parent_split, "parent_line": line_number, **change})
+ if counters["split_samples"][split] % 20000 == 0:
+ print(json.dumps({"split": split, "scenes": counters["split_samples"][split]}), flush=True)
+ print(json.dumps({"split_samples": dict(counters["split_samples"])}), flush=True)
+ shutil.copyfile(parent / "rejections.jsonl", stage / "rejections.jsonl")
+ if any(fingerprint(path) != digest for path, digest in before.items()):
+ raise ValueError("input bytes changed during immutable main build")
+ if any(fingerprint(path) != digest for path, digest in implementation_before.items()):
+ raise ValueError("producer implementation changed during immutable main build")
+ manifest = {"schema_version": "fastfill.v2", "builder": POLICY, "dataset_qualification_policy": POLICY,
+ "task_role": "full_condition_multisource_main", "parent_root": str(parent),
+ "input_sha256": before, "inputs_unchanged": True, "split_integrity": integrity,
+ "source_families": SOURCES, "evaluation_only_sources": ["SceneSmith", "SpatialGen"],
+ "auxiliary_families": ["3D-FRONT", "3RScan", "ARKitScenes"],
+ "size_output_policy": _size_policy(model_config), "yaw_policy": YAW_POLICY,
+ "parent_front_policy": parent_manifest.get("front_policy"), "parent_yaw_policy": parent_manifest.get("yaw_policy"),
+ "condition_policy": "preserve full parent conditions; qualify Scan2CAD estimated floor, mask degenerate (<3mm axis) sizes, "
+ "flag (never drop) a declared room height exceeded by a valid target in provenance.height_conflict, "
+ "revert groups losing complete position to fixed identity",
+ "degenerate_axis_m": DEGENERATE_AXIS_M, "height_tolerance_m": HEIGHT_TOLERANCE_M,
+ "roomgenbench_holdout_groups": sorted(holdout_groups), "holdout_reason": HOLDOUT_REASON,
+ "target_geometry_modified": False, "source_data_modified": False, "parent_data_modified": False,
+ "admission_policy": "retain every reviewed parent scene; separate actual-tokenizer active-objective/object/context preflight",
+ "implementation_sha256": implementation_before,
+ **{key: dict(value) for key, value in counters.items()}}
+ manifest["output_sha256"] = {path.name: fingerprint(path) for path in sorted(stage.iterdir())}
+ with (stage / "manifest.json").open("x") as stream:
+ _write(stream, manifest)
+ target.mkdir(exist_ok=False)
+ for path in stage.iterdir():
+ path.rename(target / path.name)
+ return manifest
+
+
+def main(argv=None):
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--parent-root", required=True)
+ parser.add_argument("--output", required=True)
+ parser.add_argument("--ir-root", required=True)
+ parser.add_argument("--frozen-manifest", required=True)
+ parser.add_argument("--holdout-group", action="append", dest="holdout_groups",
+ help="provenance.group written to test (repeatable); default: RoomGenBench benchmark rooms")
+ parser.add_argument("--pin-current-parent", action="store_true",
+ help="pin the parent bytes as found now (recorded in input_sha256) instead of PARENT_HASHES")
+ args = parser.parse_args(argv)
+ holdout = ROOMGENBENCH_HOLDOUT_GROUPS if args.holdout_groups is None else tuple(args.holdout_groups)
+ pins = {name: fingerprint(Path(args.parent_root) / name) for name in PARENT_HASHES} if args.pin_current_parent else None
+ result = build_dataset(args.parent_root, args.output, ir_root=args.ir_root, frozen_manifest=args.frozen_manifest,
+ holdout_groups=holdout, expected_hashes=pins)
+ print(json.dumps({"output": args.output, "split_samples": result["split_samples"],
+ "qualification_changes": result["qualification_change_counts"]}))
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/code/fastfill/v2/regularizers.py b/code/fastfill/v2/regularizers.py
new file mode 100644
index 0000000000000000000000000000000000000000..af2dc04c6a77b5940f0866c23a0c94ad8a540807
--- /dev/null
+++ b/code/fastfill/v2/regularizers.py
@@ -0,0 +1,100 @@
+"""Optional scene regularizers, distinct from matched prediction-vs-GT GIoU.
+
+Collision is normalized upright OBB intersection volume. Declared support pairs
+are exempt only from volumetric overlap regularization; runtime verifies actual
+contact. Boundary uses a convex input polygon's halfplanes; concave or unknown
+boundaries fail explicitly when this training term is requested.
+"""
+from __future__ import annotations
+
+import torch
+
+from fastfill.v2.boxes import intersection_area
+from fastfill.v2.geometry import bev_corners
+from fastfill.v2.validation import effective_support_requests
+
+
+def _convex_polygon(vertices):
+ from shapely.geometry import Polygon
+ polygon = Polygon(vertices)
+ if not polygon.is_valid or polygon.area <= 0 or abs(polygon.convex_hull.area - polygon.area) > 1e-8:
+ raise ValueError("boundary training regularizer requires a known convex polygon")
+ area = sum(a[0] * b[1] - a[1] * b[0] for a, b in zip(vertices, vertices[1:] + vertices[:1]))
+ return vertices if area > 0 else list(reversed(vertices))
+
+
+def _collision(p, s, y, q, t, z):
+ original = p
+ # Compute all volume factors in float64 before multiplying/dividing. A
+ # float32 box side can be finite while its volume overflows or underflows.
+ # Metal has no float64 kernels; CPU transfers remain part of autograd.
+ values = (p, s, y, q, t, z)
+ p, s, y, q, t, z = (value.cpu().double() if original.device.type == "mps" else value.double()
+ for value in values)
+ offset = p.detach()
+ p, q = p - offset, q - offset
+ zero = sum((value * 0).sum() for value in (p, s, y, q, t, z))
+ height = (torch.minimum(p[2] + s[2], q[2] + t[2]) - torch.maximum(p[2], q[2])).clamp_min(0)
+ if not bool(height.detach() > 0):
+ # Multiply before reducing: finite large coordinates can overflow a
+ # sum even though the correct disjoint-box contribution is exactly zero.
+ result = zero
+ else:
+ volume = intersection_area(p, s, y, q, t, z) * height
+ denominator = (s.prod() + t.prod()) / 2
+ if not bool(torch.isfinite(denominator) & (denominator > 0)):
+ raise ValueError("collision normalization requires finite positive float64 box volume")
+ result = volume / denominator + zero
+ # Separate the CPU dtype conversion and transfer: a combined `.to` can
+ # attempt a forbidden float64 conversion on Metal during backward.
+ return result.to(dtype=original.dtype).to(original.device) if original.device.type == "mps" else result
+
+
+def _boundary(p, s, y, room):
+ if room.get("boundary_known", True) is not True:
+ raise ValueError("boundary regularizer cannot use unknown/incomplete room boundaries")
+ poly = p.new_tensor(_convex_polygon(room["floor_polygon_xy_m"]))
+ edge = poly.roll(-1, 0) - poly
+ offsets = bev_corners(p, s, y)[:, None] - poly[None]
+ cross = edge[None, :, 0] * offsets[..., 1] - edge[None, :, 1] * offsets[..., 0]
+ distance = cross / edge.norm(dim=-1).clamp_min(torch.finfo(p.dtype).tiny)
+ return torch.relu(-distance).mean()
+
+
+def regularizer_specs(condition, config):
+ """One immutable enumeration for loss terms and eligibility counting."""
+ objects, room = effective_support_requests(condition), condition["room"]
+ if config.boundary and objects:
+ if room.get("boundary_known", True) is not True:
+ raise ValueError("boundary regularizer cannot use unknown/incomplete room boundaries")
+ _convex_polygon(room["floor_polygon_xy_m"])
+ pairs, fixed_pairs = [], []
+ if config.collision:
+ for i, obj in enumerate(objects):
+ pairs.extend((i, j) for j in range(i)
+ if obj.get("support_parent") != objects[j]["id"]
+ and objects[j].get("support_parent") != obj["id"])
+ fixed_pairs.extend((i, fixed) for fixed in room.get("fixed_objects", [])
+ if obj.get("support_parent") != fixed["id"])
+ return objects, room, tuple(pairs), tuple(fixed_pairs)
+
+
+def scene_regularizers(positions, sizes, yaws, batch, config, global_counts=True):
+ from fastfill.v2.losses import _mean
+ zero = (positions[batch["slot_mask"]] * 0).sum()
+ collision, boundary = [], []
+ if "conditions" not in batch:
+ raise ValueError("scene regularizers require original conditions")
+ for b, condition in enumerate(batch["conditions"]):
+ objects, room, pairs, fixed_pairs = regularizer_specs(condition, config)
+ if config.boundary:
+ boundary.extend(_boundary(positions[b, i], sizes[b, i], yaws[b, i], room) for i in range(len(objects)))
+ collision.extend(_collision(positions[b, i], sizes[b, i], yaws[b, i],
+ positions[b, j], sizes[b, j], yaws[b, j]) for i, j in pairs)
+ for i, fixed in fixed_pairs:
+ q, t, z = [positions.new_tensor(fixed[k]) for k in ("bottom_center_m", "size_local_m", "yaw_rad")]
+ collision.append(_collision(positions[b, i], sizes[b, i], yaws[b, i], q, t, z))
+ terms = {"collision": collision, "boundary": boundary}
+ return {**{key: _mean(sum(values, zero), len(values), global_counts)[0] for key, values in terms.items()},
+ "sums": {key: sum((v.detach().float() for v in values), torch.zeros((), device=zero.device)) for key, values in terms.items()},
+ "counts": {key: len(values) for key, values in terms.items()}}
diff --git a/code/fastfill/v2/remask.py b/code/fastfill/v2/remask.py
new file mode 100644
index 0000000000000000000000000000000000000000..170556eca82a03a73d036f32776c8e480c6cc487
--- /dev/null
+++ b/code/fastfill/v2/remask.py
@@ -0,0 +1,296 @@
+"""Remasking decoding (Stage 2 of masked layout training): place a request's objects over several rounds.
+
+MaskGIT / SceneNAT confidence-first iterative decoding at the object level, for a ``pose_conditioning`` model:
+
+1. ``model.encode`` once (backbone + slot pooling); every round only ``model.decode`` runs.
+2. Each round decodes every slot with the poses committed so far given (``given_pose`` / ``given_pose_mask``,
+ ``masked_layout.pose_features`` of the committed pose, as in training). Each open slot picks its argmax cell;
+ its confidence is the softmax probability of that cell under the raw logits (SceneNAT's score of the sampled
+ token). The most confident open slots are committed so that ``floor(N * cos(pi/2 * (r + 1) / R))`` slots stay
+ open after round ``r`` of ``R`` (at least one commit per round; none open after the last round).
+3. With ``legal`` the cell choice is restricted, per slot, to its least-cost cells under ``cell_costs`` (the
+ expected-geometry costs: footprint outside the floor, a door's clearance zone for floor objects that can block,
+ the room's fixed objects, and, round by round, the footprints of the objects already committed whose height interval overlaps the
+ slot's, as the scorer's collision check counts them). Costs use the slot's own predicted size, yaw and z. Slots of one round are committed in confidence order and each re-checks its cell against the
+ footprints committed just before it. A slot whose every cell costs something takes its cheapest cells. The
+ bottom centre decoded in the chosen cell is kept when it is as cheap as the cell's best candidate position
+ (``cell_costs.cell_positions``), else it snaps to the nearest least-cost candidate.
+
+4. With ``retract`` (SceneNAT's sampler) a committed slot can be taken back: each round ranks the open slots by
+ their confidence now and the committed ones by their confidence when committed, keeps the most confident so
+ that the schedule's number stays open, and masks the others again (pose, footprint and score dropped), to be
+ re-predicted with the poses kept so far. Slots given by the request are never retracted.
+5. A ``support_head`` model's support prediction (floor or not) stands in for an undeclared ``support_parent``
+ in deciding which objects block doors, and its ``support_logits`` ride along with the committed rows.
+
+6. Supports: an object standing on another one (its declared ``support_parent``, a hard ``on`` constraint, or, for a
+ ``support_head`` model, its predicted parent) takes its cell without the legality filter and blocks nothing while
+ the rounds run (its parent's footprint is no obstacle to it); after the last round ``attach_supports`` puts it on
+ its parent like ``evaluate.spread_grid_xy`` does: bottom on the parent's top (or kept inside the parent, between
+ its bottom and ``INNER_M`` below its top: a book on a shelf), centre inside the parent's footprint (the nearest
+ point of it, in the parent's frame, when the decoded one is outside), parents before their children. One declared
+ (or predicted) on the floor stands on it. Without this the rounds treated a lamp's own nightstand as an occupied
+ cell and committed the lamp elsewhere in the room, and argmax serialization never put it back.
+
+The result is a predictions dict like the model's: every slot carries the rows (cell logits restricted to its
+legal cells, residuals, z, size, yaw logits and residuals) of the round that committed it, so
+``evaluate.serialize_predictions(..., grid_decode="argmax")`` writes exactly the committed poses. ``rounds=1``
+without ``legal`` is the plain argmax decode.
+
+Pose features need each slot's yaw symmetry order and K1 flag; batches collated from labelled rows carry them
+(``yaw_symmetry_order`` / ``size_axis_swap_allowed``), a bare condition gets the collate defaults (order 1, no swap).
+"""
+from __future__ import annotations
+
+import math
+
+import numpy as np
+import torch
+
+from fastfill.v2.cell_costs import (THIN_HEIGHT_M, RoomRaster, box_corners, cell_cost_maps, cell_cost_options, cell_positions,
+ footprint_offsets, vertical_extent)
+from fastfill.v2.geometry import decode_grid_position, decode_yaw
+from fastfill.v2.masked_layout import pose_features
+from fastfill.v2.support import decode_support_parents
+from fastfill.v2.validation import effective_support_requests
+
+FLOOR_PREDICTION_TOLERANCE_M = .15 # a predicted bottom this close to the floor stands on it
+INNER_M = .05 # a child more than this below its parent's top (and not below its bottom) stays inside the parent
+ILLEGAL_LOGIT = -1e4
+ROW_KEYS = ("position_normalized", "size", "yaw_logits", "yaw_residuals", "position_cell_logits", "position_cell_residuals")
+
+
+def open_after(total: int, round_index: int, rounds: int) -> int:
+ """Slots left open after round ``round_index`` (0-based) of ``rounds``: MaskGIT's cosine schedule."""
+ return math.floor(total * math.cos(math.pi / 2 * (round_index + 1) / rounds))
+
+
+def _point_cost(raster: RoomRaster, points: np.ndarray, size, yaw: float, blocks: bool, z_range, options: dict) -> np.ndarray:
+ """Cost (as in ``cell_cost_maps``) of the footprint with its bottom centre at each of ``points`` (k x 2)."""
+ samples = points[:, None, :] + footprint_offsets(size[:2], yaw, options)[None]
+ outside, door = raster.lookup(samples)
+ cost = outside.mean(-1) + door.mean(-1) * blocks
+ occupied = raster.occupied(z_range)
+ if occupied is not None:
+ cost = cost + raster.lookup_occupied(samples, occupied).mean(-1)
+ return cost
+
+
+def _snap(raster, candidates, decoded, size, yaw, blocks, z_range, options) -> np.ndarray:
+ """The decoded bottom centre if it costs no more than the cell's best candidate, else the nearest of the
+ least-cost candidates: the cell was chosen for some legal position in it, the residual may miss it."""
+ points = np.vstack((decoded[None], candidates))
+ cost = _point_cost(raster, points, size, yaw, blocks, z_range, options)
+ best = cost.min()
+ if cost[0] <= best + 1e-6:
+ return decoded
+ legal = np.flatnonzero(cost <= best + 1e-6)
+ return points[legal[np.argmin(np.linalg.norm(points[legal] - decoded, axis=1))]]
+
+
+def _blocks(condition_object: dict, z_world: float, floor_z, height: float, predicted_floor: bool | None = None) -> bool:
+ """Floor object that can block a door or collide with other floor objects: a declared support wins, then the
+ support head's prediction (floor or not), else a predicted bottom near the floor; thin objects (carpets) never
+ block."""
+ declared = condition_object.get("support_parent")
+ if declared is not None:
+ on_floor = declared == "floor"
+ elif predicted_floor is not None:
+ on_floor = predicted_floor
+ else:
+ on_floor = isinstance(floor_z, (int, float)) and abs(z_world - floor_z) <= FLOOR_PREDICTION_TOLERANCE_M
+ return on_floor and height > THIN_HEIGHT_M
+
+
+def support_parents(condition: dict, support_logits=None) -> list:
+ """One parent per request (slot order): the declared one (``support_parent`` or a hard ``on``), else the support
+ head's prediction when ``support_logits`` (n x classes) is given, else None."""
+ requests = effective_support_requests(condition)
+ if support_logits is None:
+ return [r.get("support_parent") for r in requests]
+ return decode_support_parents(support_logits[:len(requests)].detach().cpu(), requests)
+
+
+def attach_supports(out: dict, batch: dict, parents: list) -> int:
+ """Put every slot whose parent is another request or a fixed object on that parent, and every slot whose parent is
+ "floor" on the floor, in ``out["position_normalized"]`` (in place, parents first); returns the slots moved.
+ ``parents``: per row, ``support_parents`` of its condition."""
+ origin, scale = batch["origin"].cpu().double(), batch["scale"].cpu().double()
+ yaw = decode_yaw(out["yaw_logits"].float(), out["yaw_residuals"].float()).cpu().double()
+ moved = 0
+ for row, condition in enumerate(batch["conditions"]):
+ ids = [o["id"] for o in condition["objects"]]
+ slot = {ident: i for i, ident in enumerate(ids)}
+ fixed = {f["id"]: f for f in condition["room"].get("fixed_objects", ())}
+ floor = condition["room"].get("floor_z_m")
+ world = (out["position_normalized"][row, :len(ids)].cpu().double() * scale[row] + origin[row]).numpy()
+ size = out["size"][row, :len(ids)].float().cpu().double().numpy()
+
+ def depth(i):
+ chain, seen = 0, set()
+ while parents[row][i] in slot and i not in seen:
+ seen.add(i)
+ i, chain = slot[parents[row][i]], chain + 1
+ return chain
+
+ for i in sorted(range(len(ids)), key=depth):
+ parent = parents[row][i]
+ before = world[i].copy()
+ if parent == "floor" and isinstance(floor, (int, float)):
+ world[i, 2] = floor
+ elif parent in slot or parent in fixed:
+ if parent in slot:
+ j = slot[parent]
+ centre, box, angle = world[j], size[j], float(yaw[row, j])
+ else:
+ f = fixed[parent]
+ centre, box, angle = np.asarray(f["bottom_center_m"], float), np.asarray(f["size_local_m"], float), f["yaw_rad"]
+ c, s = math.cos(angle), math.sin(angle)
+ d = world[i, :2] - centre[:2]
+ local = np.array((c * d[0] + s * d[1], c * d[1] - s * d[0]))
+ local = np.clip(local, -box[:2] / 2, box[:2] / 2) # the nearest point of the parent's footprint
+ world[i, :2] = centre[:2] + np.array((c * local[0] - s * local[1], s * local[0] + c * local[1]))
+ top = centre[2] + box[2]
+ if not centre[2] <= world[i, 2] < top - INNER_M: # else: inside the parent, e.g. on a shelf
+ world[i, 2] = top
+ if not np.allclose(before, world[i]):
+ moved += 1
+ out["position_normalized"][row, :len(ids)] = torch.from_numpy((world - origin[row].numpy()) / scale[row].numpy()).to(
+ out["position_normalized"].dtype).to(out["position_normalized"].device)
+ return moved
+
+
+@torch.no_grad()
+def remask_predictions(model, batch: dict, *, rounds: int = 4, legal: bool = True, retract: bool = False,
+ door_depth_m: float = .8, stats: dict | None = None) -> dict:
+ """Decode a collated batch over ``rounds`` rounds (see the module docstring). ``stats`` (a dict) receives
+ ``decode_calls``, ``commits_per_round`` and ``retractions_per_round``."""
+ if rounds < 1:
+ raise ValueError("rounds must be at least 1")
+ if model.config.position_head != "grid_residual":
+ raise ValueError("remasking decoding needs the grid_residual position head")
+ if rounds > 1 and not model.config.pose_conditioning:
+ raise ValueError("remasking decoding over several rounds needs a model built with pose_conditioning")
+ model.eval()
+ grid = model.config.position_grid
+ slot_mask = batch["slot_mask"].bool()
+ b, n = slot_mask.shape
+ encoded = model.encode(batch["input_ids"], batch["attention_mask"], batch["object_spans"], batch["slot_mask"])
+ fixed = {key: batch[key] for key in ("fixed_position_normalized", "fixed_position_mask", "fixed_size", "fixed_size_mask")
+ if key in batch}
+ device = slot_mask.device
+ pinned = batch.get("given_pose_mask", torch.zeros_like(slot_mask)).bool() & slot_mask # given by the request
+ committed = pinned.clone()
+ features = batch.get("given_pose", torch.zeros((b, n, 8), device=device)).clone().float()
+ out = None
+ options = cell_cost_options({"grid": grid, "door_depth_m": door_depth_m})
+ rasters = [RoomRaster(c["room"], options, .1) if legal else None for c in batch["conditions"]]
+ base_blockers = [list(r.blockers) if legal else [] for r in rasters] # the room's fixed objects
+ footprints = [{} for _ in range(b)] # committed slot -> its blocker
+ candidates = [cell_positions(c["room"], options) if legal else None for c in batch["conditions"]]
+ origin, scale = batch["origin"].cpu().double(), batch["scale"].cpu().double()
+ symmetry = batch.get("yaw_symmetry_order", torch.ones((b, n), dtype=torch.long)).to(device)
+ swap = batch.get("size_axis_swap_allowed", torch.zeros((b, n), dtype=torch.bool)).to(device)
+ total = (slot_mask & ~pinned).sum(1).tolist()
+ score = [{} for _ in range(b)] # committed slot -> its confidence when committed (SceneNAT keeps it)
+ log = {"decode_calls": 0, "commits_per_round": [], "retractions_per_round": [], "attached": 0}
+ for r in range(rounds):
+ kwargs = dict(fixed)
+ if model.config.pose_conditioning:
+ kwargs.update(given_pose=features.to(encoded["slots"].dtype), given_pose_mask=committed)
+ pred = model.decode(encoded, **kwargs)
+ log["decode_calls"] += 1
+ if out is None:
+ out = {key: pred[key].clone() for key in (*ROW_KEYS, *(("support_logits",) if "support_logits" in pred else ()))}
+ out["slot_mask"] = pred["slot_mask"]
+ raw = pred["position_cell_logits"].float()
+ yaw = decode_yaw(pred["yaw_logits"].float(), pred["yaw_residuals"].float())
+ support = pred.get("support_logits")
+ commits = retractions = 0
+ for row in range(b):
+ open_slots = [i for i in range(n) if slot_mask[row, i] and not committed[row, i]]
+ if not open_slots:
+ continue
+ objects, room = batch["conditions"][row]["objects"], batch["conditions"][row]["room"]
+ size = pred["size"][row].float().cpu().double().numpy()
+ yaw_row = yaw[row].cpu().double().numpy()
+ z_world = (pred["position_normalized"][row, :, 2].float().cpu().double() * scale[row, 2] + origin[row, 2]).numpy()
+ predicted_floor = (support[row].argmax(-1) == 0).tolist() if support is not None else [None] * n
+ parents = support_parents(batch["conditions"][row], support[row] if support is not None else None)
+ on_object = {i for i, p in enumerate(parents) if p is not None and p not in ("floor", "wall")}
+ blocks = {i: _blocks(objects[i], z_world[i], room.get("floor_z_m"), size[i, 2], predicted_floor[i])
+ for i in open_slots}
+ z_range = {i: vertical_extent(z_world[i], size[i, 2], options) for i in open_slots}
+ version = [0] # bumped whenever the occupied footprints change
+
+ def restricted(i):
+ if not legal or i in on_object: # placed on its parent after the rounds (attach_supports)
+ return raw[row, i]
+ costs, _ = cell_cost_maps(room, [(size[i, :2].tolist(), float(yaw_row[i]), blocks[i], z_range[i])], options,
+ raster=rasters[row])
+ cost = torch.from_numpy(costs[0]).to(raw.device)
+ return torch.where(cost <= cost.min() + 1e-6, raw[row, i], torch.full_like(raw[row, i], ILLEGAL_LOGIT))
+
+ if retract and legal and footprints[row]:
+ # rank the open slots against the room and its fixed objects only: committed footprints may be
+ # retracted below, the slots that commit are re-checked against the ones kept
+ rasters[row].blockers = list(base_blockers[row])
+ version[0] += 1
+ logits = {i: restricted(i) for i in open_slots}
+ seen = version[0]
+ probability = raw[row].softmax(-1)
+ confidence = {i: float(probability[i, int(logits[i].argmax())]) for i in open_slots}
+ keep_open = 0 if r == rounds - 1 else open_after(total[row], r, rounds)
+ if retract:
+ # SceneNAT: the kept set is the most confident of the open slots (scored now) and the committed ones
+ # (scored when committed); a committed slot outside it is masked again and re-predicted later
+ pool = {**score[row], **confidence}
+ keep = set(sorted(pool, key=lambda k: -pool[k])[:max(0, len(pool) - keep_open)])
+ for i in [i for i in score[row] if i not in keep]:
+ committed[row, i] = False
+ features[row, i] = 0.
+ del score[row][i]
+ footprints[row].pop(i, None)
+ retractions += 1
+ if legal and footprints[row]:
+ rasters[row].blockers = base_blockers[row] + list(footprints[row].values())
+ version[0] += 1
+ chosen = sorted((i for i in open_slots if i in keep), key=lambda k: -confidence[k])
+ else:
+ count = max(1, len(open_slots) - keep_open)
+ chosen = sorted(open_slots, key=lambda k: -confidence[k])[:count]
+ for i in chosen:
+ if legal and version[0] != seen:
+ logits[i] = restricted(i) # footprints committed (or retracted) since the logits were computed
+ cell_logits = logits[i]
+ xy = decode_grid_position(cell_logits[None], pred["position_cell_residuals"][row, i][None].float(), grid)[0]
+ if legal and i not in on_object:
+ lo, sc = origin[row, :2].numpy(), scale[row, :2].numpy()
+ world = _snap(rasters[row], candidates[row][int(cell_logits.argmax())], xy.cpu().double().numpy() * sc + lo,
+ size[i], float(yaw_row[i]), blocks[i], z_range[i], options)
+ xy = torch.from_numpy((world - lo) / sc).to(xy)
+ position = torch.cat((xy.to(pred["position_normalized"].dtype), pred["position_normalized"][row, i, 2:]))
+ for key in out:
+ if key != "slot_mask":
+ out[key][row, i] = pred[key][row, i]
+ out["position_cell_logits"][row, i] = cell_logits.to(out["position_cell_logits"].dtype)
+ out["position_normalized"][row, i] = position
+ features[row, i] = pose_features(position[None].float(), pred["size"][row, i][None].float(),
+ yaw[row, i][None], symmetry[row, i][None], swap[row, i][None])[0]
+ committed[row, i] = True
+ score[row][i] = confidence[i]
+ commits += 1
+ if legal and i not in on_object:
+ world = position[:2].cpu().double().numpy() * scale[row, :2].numpy() + origin[row, :2].numpy()
+ rasters[row].block(box_corners(world, size[i, :2], float(yaw_row[i])), z_range[i])
+ footprints[row][i] = rasters[row].blockers[-1]
+ version[0] += 1
+ log["commits_per_round"].append(commits)
+ log["retractions_per_round"].append(retractions)
+ if not (slot_mask & ~committed).any():
+ break
+ log["attached"] = attach_supports(out, batch, [support_parents(c, out["support_logits"][row] if "support_logits" in out else None)
+ for row, c in enumerate(batch["conditions"])])
+ if stats is not None:
+ stats.update(log)
+ return out
diff --git a/code/fastfill/v2/requirements-roomgenbench.txt b/code/fastfill/v2/requirements-roomgenbench.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3a178ea6d807396239a685481c57ac6e3c153354
--- /dev/null
+++ b/code/fastfill/v2/requirements-roomgenbench.txt
@@ -0,0 +1,6 @@
+# Optional CPU mesh assembly; no learned generation, Blender or sparse-conv stack.
+# RoomGenBench/bench/assemble.py imports all four packages below at module load.
+numpy>=1.24
+scipy>=1.11
+trimesh>=4.12.2
+Pillow>=12.1.1
diff --git a/code/fastfill/v2/requirements.txt b/code/fastfill/v2/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0e041cdec0fa2b85081b563d6d4fa214f4893600
--- /dev/null
+++ b/code/fastfill/v2/requirements.txt
@@ -0,0 +1,8 @@
+# Install the correct CPU/Metal/CUDA PyTorch wheel for your training machine first.
+-r ../requirements.txt
+scipy>=1.11
+torch>=2.4
+# Patched transitive runtime dependencies; keep v1 requirements unchanged.
+anyio>=4.14.2
+fsspec>=2026.6.0
+# Offline validation: pip install pytest pytest-cov
diff --git a/code/fastfill/v2/review_data.py b/code/fastfill/v2/review_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e46c6dc9b54a3848c5b72329bb853517c1c0c8e
--- /dev/null
+++ b/code/fastfill/v2/review_data.py
@@ -0,0 +1,321 @@
+"""Publish a new evidence-qualified revision; never repair parent geometry.
+
+Only frozen source-derived ``faces`` constraints lacking semantic-yaw evidence
+are omitted. Explicit user requirements survive. Below-floor target positions
+are diagnostics, not clamped labels. This module intentionally uses stdlib only.
+"""
+from __future__ import annotations
+
+import argparse
+from collections import Counter
+from copy import deepcopy
+import hashlib
+from itertools import zip_longest
+import json
+import math
+import os
+from pathlib import Path
+import shutil
+import tempfile
+
+SPLITS = ("train", "validation", "test")
+INPUT_FILES = tuple(f"{split}.jsonl" for split in SPLITS) + ("manifest.json", "rejections.jsonl")
+OUTPUT_FILES = tuple(name for name in INPUT_FILES if name != "manifest.json")
+POLICY = "source-derived-facing-evidence-v1"
+BUILDER = "immutable-parent-review-v1"
+FLOOR_TOLERANCE_M = 1e-4
+LEGACY_CONSTRAINT_SOURCE = "frozen_sparse_legacy_request"
+RAW_SOURCE_ROOT = Path("/Volumes/harddisk/3D_Room_Collections")
+REVIEW_FIELDS = ("review_parent_scene_id", "review_policy", "review_source_qualification",
+ "review_omitted_constraints", "review_geometry_diagnostics")
+
+
+def _digest(path):
+ value = hashlib.sha256()
+ with Path(path).open("rb") as stream:
+ for block in iter(lambda: stream.read(1024 * 1024), b""):
+ value.update(block)
+ return value.hexdigest()
+
+
+def _reject_constant(value):
+ raise ValueError(f"nonfinite JSON number: {value}")
+
+
+def _read_json(value):
+ return json.loads(value, parse_constant=_reject_constant)
+
+
+def _parent_inputs(root):
+ for name in INPUT_FILES:
+ if not (root / name).is_file():
+ raise FileNotFoundError(f"required parent input missing: {root / name}")
+ raw = (root / "manifest.json").read_bytes()
+ manifest = _read_json(raw)
+ if not isinstance(manifest, dict) or manifest.get("schema_version") != "fastfill.v2":
+ raise ValueError("parent manifest must identify fastfill.v2 data")
+ hashes = {name: _digest(root / name) for name in INPUT_FILES if name != "manifest.json"}
+ return manifest, {**hashes, "manifest.json": hashlib.sha256(raw).hexdigest()}
+
+
+def _recheck(root, hashes):
+ if any(_digest(root / name) != expected for name, expected in hashes.items()):
+ raise ValueError("parent input or manifest changed during review")
+
+
+def _new_output(parent_root, output):
+ if Path(output).is_symlink():
+ raise FileExistsError(f"immutable review output already exists as a symlink: {output}")
+ parent, target = Path(parent_root).resolve(), Path(output).resolve()
+ for protected in (parent, RAW_SOURCE_ROOT.resolve()):
+ if target == protected or protected in target.parents or target in protected.parents:
+ raise ValueError("output must be outside source data and its ancestors")
+ if target.exists():
+ raise FileExistsError(f"immutable review output already exists: {target}")
+ return parent, target
+
+
+def _complete(mask, dimensions):
+ return mask is True or (isinstance(mask, list) and len(mask) == dimensions
+ and all(value is True for value in mask))
+
+
+def _mask(sample, field, index, dimensions):
+ values = sample["validity"].get(field, [])
+ return index < len(values) and _complete(values[index], dimensions)
+
+
+def _finite(value):
+ return isinstance(value, (int, float)) and not isinstance(value, bool) and math.isfinite(value)
+
+
+def _indices(sample):
+ if not isinstance(sample, dict) or sample.get("schema_version") != "fastfill.v2":
+ raise ValueError("parent rows must use fastfill.v2 schema")
+ condition, target = sample.get("condition", {}), sample.get("target", {})
+ if not isinstance(condition, dict) or not isinstance(target, dict):
+ raise ValueError("condition and target must be objects")
+ collections = (condition.get("objects"), target.get("objects"))
+ ids = []
+ for objects in collections:
+ if not isinstance(objects, list) or any(not isinstance(obj, dict)
+ or not isinstance(obj.get("id"), str) or not obj["id"] for obj in objects):
+ raise ValueError("request and target objects require nonempty string IDs")
+ names = [obj["id"] for obj in objects]
+ if len(names) != len(set(names)):
+ raise ValueError("duplicate request or target ID")
+ ids.append(names)
+ if set(ids[0]) != set(ids[1]):
+ raise ValueError("target IDs must equal requested IDs exactly once")
+ validity = sample.get("validity")
+ if not isinstance(validity, dict) or any(not isinstance(validity.get(field, []), list)
+ or len(validity.get(field, [])) > len(ids[1]) for field in ("position", "size", "yaw")):
+ raise ValueError("invalid geometry validity rows")
+ if not isinstance(condition.get("room"), dict) or not isinstance(condition.get("constraints", []), list):
+ raise ValueError("room and constraints must be structured fields")
+ provenance = sample.get("provenance")
+ if not isinstance(provenance, dict) or any(not isinstance(provenance.get(key), str)
+ or not provenance[key] for key in ("scene_id", "legacy_uid")):
+ raise ValueError("parent provenance requires stable scene_id and legacy_uid")
+ evidence = provenance.get("field_evidence", [])
+ if (not isinstance(evidence, list) or len(evidence) > len(ids[1])
+ or any(not isinstance(row, dict) for row in evidence)):
+ raise ValueError("field evidence must follow target object indices")
+ if any(key in provenance for key in REVIEW_FIELDS):
+ raise ValueError("parent already contains review provenance")
+ return {name: index for index, name in enumerate(ids[1])}
+
+
+def _trusted_yaw(sample, index):
+ """Facing needs a semantic front: a valid yaw whose symmetry order is 1 (axis-only mod-pi yaw is not evidence)."""
+ orders = sample["validity"].get("yaw_symmetry_order", [])
+ if not _mask(sample, "yaw", index, 1) or index < len(orders) and orders[index] != 1:
+ return False
+ angle = sample["target"]["objects"][index].get("yaw_rad")
+ if not _finite(angle) or not -math.pi <= angle < math.pi:
+ raise ValueError("valid yaw label must be finite and wrapped to [-pi, pi)")
+ evidence = sample["provenance"].get("field_evidence", [])
+ field = evidence[index] if index < len(evidence) else {}
+ recorded = field.get("recorded_source_evidence", {})
+ if not isinstance(recorded, dict):
+ raise ValueError("recorded source evidence must be an object")
+ return "front_unknown_reason" not in recorded
+
+
+def _floor_diagnostics(sample):
+ room = sample["condition"]["room"]
+ known = room.get("floor_known") is True
+ floor = room.get("floor_z_m") if known else None
+ if known and not _finite(floor):
+ raise ValueError("known floor requires a finite floor_z_m")
+ conflicts = []
+ for index, target in enumerate(sample["target"]["objects"]):
+ if not known or not _mask(sample, "position", index, 3):
+ continue
+ position = target.get("bottom_center_m")
+ if not isinstance(position, list) or len(position) != 3 or not all(map(_finite, position)):
+ raise ValueError("valid position label must be finite XYZ")
+ if position[2] < floor - FLOOR_TOLERANCE_M:
+ conflicts.append({"object_id": target["id"], "bottom_minus_floor_m": position[2] - floor})
+ return {"known_floor": known, "floor_z_m": floor, "tolerance_m": FLOOR_TOLERANCE_M,
+ "below_known_floor": conflicts,
+ "minimum_bottom_minus_floor_m": min((c["bottom_minus_floor_m"] for c in conflicts), default=None),
+ "labels_modified": False}
+
+
+def review_sample(parent):
+ """Pure derivation: only omit unverified frozen legacy facing constraints."""
+ indices = _indices(parent)
+ legacy = parent["provenance"].get("constraint_source") == LEGACY_CONSTRAINT_SOURCE
+ kept, omitted = [], []
+ for constraint in parent["condition"].get("constraints", []):
+ if not isinstance(constraint, dict):
+ raise ValueError("constraint must be an object")
+ if legacy and constraint.get("type") == "faces":
+ object_id = constraint.get("object_id")
+ if object_id not in indices:
+ raise ValueError("source-derived facing refers to an unknown request ID")
+ if not _trusted_yaw(parent, indices[object_id]):
+ omitted.append({"constraint": deepcopy(constraint),
+ "reason": "unverified_source_derived_facing"})
+ continue
+ kept.append(deepcopy(constraint))
+ result = deepcopy(parent)
+ if "constraints" in parent["condition"]:
+ result["condition"] = {**result["condition"], "constraints": kept}
+ result["provenance"] = {**result["provenance"],
+ "review_parent_scene_id": parent["provenance"]["scene_id"], "review_policy": POLICY,
+ "review_source_qualification": "Frozen legacy constraints are source-derived annotations, not newly "
+ "synthesized user requirements; unverified source-derived facing is omitted. "
+ "Explicit/nonlegacy requirements and existing exchangeability groups are preserved.",
+ "review_omitted_constraints": omitted, "review_geometry_diagnostics": _floor_diagnostics(parent)}
+ return result
+
+
+def _rows(root, split, seen):
+ with (root / f"{split}.jsonl").open("rb") as stream:
+ for number, raw in enumerate(stream, 1):
+ if not raw.strip():
+ continue
+ try:
+ sample = _read_json(raw)
+ provenance = sample["provenance"]
+ uid = provenance["legacy_uid"]
+ if not isinstance(uid, str) or not uid or uid in seen or provenance.get("split") != split:
+ raise ValueError("duplicate/missing UID or provenance split disagrees with its file")
+ seen.add(uid)
+ except (ValueError, KeyError, TypeError) as exc:
+ raise ValueError(f"invalid row {split}.jsonl:{number}: {exc}") from exc
+ yield sample
+
+
+def _empty_counts():
+ return {key: Counter() for key in ("split_samples", "split_objects", "source_split_samples",
+ "source_omitted_faces", "source_floor_conflict_objects", "totals")}
+
+
+def _count(counts, sample, split):
+ source = sample["provenance"].get("source", "unknown")
+ omitted = len(sample["provenance"]["review_omitted_constraints"])
+ conflicts = len(sample["provenance"]["review_geometry_diagnostics"]["below_known_floor"])
+ counts["split_samples"][split] += 1
+ counts["split_objects"][split] += len(sample["target"]["objects"])
+ counts["source_split_samples"][f"{source}:{split}"] += 1
+ for key, value in (("omitted_faces", omitted), ("rows_with_omitted_faces", bool(omitted)),
+ ("floor_conflict_objects", conflicts), ("rows_with_floor_conflicts", bool(conflicts))):
+ counts["totals"][key] += value
+ if omitted:
+ counts["source_omitted_faces"][source] += omitted
+ if conflicts:
+ counts["source_floor_conflict_objects"][source] += conflicts
+
+
+def _summary(counts):
+ result = {key: dict(value) for key, value in counts.items() if key != "totals"}
+ for key in ("split_samples", "split_objects"):
+ result[key] = {split: counts[key][split] for split in SPLITS}
+ return {**result, **counts["totals"], "samples_written": sum(counts["split_samples"].values()),
+ "objects_written": sum(counts["split_objects"].values())}
+
+
+def _write_records(parent, staging):
+ seen, counts = set(), _empty_counts()
+ for split in SPLITS:
+ with (staging / f"{split}.jsonl").open("xb") as output:
+ for record in _rows(parent, split, seen):
+ child = review_sample(record)
+ output.write((json.dumps(child, ensure_ascii=False, allow_nan=False,
+ separators=(",", ":")) + "\n").encode())
+ _count(counts, child, split)
+ shutil.copyfile(parent / "rejections.jsonl", staging / "rejections.jsonl")
+ return _summary(counts)
+
+
+def build_reviewed(parent_root, output):
+ """Stream all rows and atomically publish to a new immutable directory."""
+ parent, target = _new_output(parent_root, output)
+ manifest, hashes = _parent_inputs(parent)
+ target.parent.mkdir(parents=True, exist_ok=True)
+ with tempfile.TemporaryDirectory(prefix=f".{target.name}-", dir=target.parent) as temporary:
+ staging = Path(temporary)
+ result = {"schema_version": "fastfill.v2", "builder": BUILDER, "review_policy": POLICY,
+ "parent_root": str(parent), "parent_builder": manifest.get("builder"),
+ "parent_front_policy": manifest.get("front_policy"),
+ "front_policy": manifest.get("front_policy"), "yaw_policy": manifest.get("yaw_policy"), "parent_sha256": hashes,
+ "split_rule": "inherit every parent UID and split without re-splitting or filtering",
+ "labels_modified": False, "source_data_modified": False,
+ "implementation_sha256": _digest(__file__), **_write_records(parent, staging),
+ "output_sha256": {name: _digest(staging / name) for name in OUTPUT_FILES}}
+ (staging / "manifest.json").write_text(json.dumps(result, indent=2, allow_nan=False) + "\n")
+ _recheck(parent, hashes)
+ if target.exists() or target.is_symlink():
+ raise FileExistsError("immutable review output appeared during derivation")
+ os.rename(staging, target)
+ return result
+
+
+def verify_reviewed(data_root, parent_root):
+ """Check fingerprints, every deterministic row, preserved identities and totals."""
+ child, parent = Path(data_root).resolve(), Path(parent_root).resolve()
+ _, parent_hashes = _parent_inputs(parent)
+ manifest, child_hashes = _parent_inputs(child)
+ if (manifest.get("builder") != BUILDER or manifest.get("review_policy") != POLICY
+ or manifest.get("parent_sha256") != parent_hashes):
+ raise ValueError("review manifest policy or parent fingerprints do not match")
+ expected_hashes = {name: child_hashes[name] for name in OUTPUT_FILES}
+ if manifest.get("output_sha256") != expected_hashes:
+ raise ValueError("review output hashes do not match manifest")
+ if child_hashes["rejections.jsonl"] != parent_hashes["rejections.jsonl"]:
+ raise ValueError("parent rejections were changed")
+ parent_seen, child_seen, counts = set(), set(), _empty_counts()
+ for split in SPLITS:
+ pairs = zip_longest(_rows(parent, split, parent_seen), _rows(child, split, child_seen))
+ for number, (original, observed) in enumerate(pairs, 1):
+ if original is None or observed != review_sample(original):
+ raise ValueError(f"reviewed row differs from deterministic parent revision: {split}:{number}")
+ _count(counts, observed, split)
+ summary = _summary(counts)
+ if any(manifest.get(key) != value for key, value in summary.items()):
+ raise ValueError("review manifest counts do not match verified rows")
+ if manifest.get("labels_modified") is not False or manifest.get("source_data_modified") is not False:
+ raise ValueError("review manifest must declare unchanged labels and parent data")
+ _recheck(parent, parent_hashes)
+ _recheck(child, child_hashes)
+ return {"verified": True, **summary, "manifest_sha256": child_hashes["manifest.json"]}
+
+
+def main(argv=None):
+ parser = argparse.ArgumentParser(description=__doc__)
+ commands = parser.add_subparsers(dest="command", required=True)
+ for command in ("build", "verify"):
+ sub = commands.add_parser(command)
+ sub.add_argument("--parent-root", type=Path, required=True)
+ sub.add_argument("--output" if command == "build" else "--data-root", type=Path, required=True)
+ args = vars(parser.parse_args(argv))
+ command = args.pop("command")
+ result = build_reviewed(**args) if command == "build" else verify_reviewed(**args)
+ print(json.dumps(result, indent=2, allow_nan=False))
+
+
+if __name__ == "__main__":
+ main()
diff --git a/code/fastfill/v2/roomgenbench.py b/code/fastfill/v2/roomgenbench.py
new file mode 100644
index 0000000000000000000000000000000000000000..e3fdfd2e7a1443fb932047a55a65239529eb77c1
--- /dev/null
+++ b/code/fastfill/v2/roomgenbench.py
@@ -0,0 +1,444 @@
+"""Dynamic RoomGenBench assembly with explicit paths and truthful mesh/proxy receipts.
+
+Only the upstream geometry helpers are imported. Its fixed five-scene CLI,
+global paths, reference GT branch and source files are never changed. Asset
+generation is a separate step; this adapter consumes generated GLB/sidecars.
+``--requests-from`` instead writes the benchmark scenes as FastFill requests (``benchmark_request``).
+"""
+from __future__ import annotations
+
+import argparse
+from collections import Counter
+from copy import deepcopy
+import importlib.util
+import json
+import sys
+from pathlib import Path
+import re
+
+from .direct_layout import layout_to_roomgenbench
+from .io import fingerprint, safe_output
+from .schema import finite
+
+
+FIT_POLICY = "roomgenbench_anisotropic_yaw_snap_tip"
+
+
+def _read_json(path):
+ with Path(path).open() as stream:
+ return json.load(stream)
+
+
+def _reference(root):
+ source = Path(root).resolve() / "bench" / "assemble.py"
+ if not source.is_file():
+ raise FileNotFoundError(f"RoomGenBench assembler not found: {source}")
+ spec = importlib.util.spec_from_file_location("_fastfill_roomgenbench_geometry", source)
+ module = importlib.util.module_from_spec(spec)
+ previous, sys.dont_write_bytecode = sys.dont_write_bytecode, True # never write into the downstream checkout
+ try:
+ spec.loader.exec_module(module)
+ finally:
+ sys.dont_write_bytecode = previous
+ return module, {"source": str(source), "sha256": fingerprint(source)}
+
+
+def _handoff(path, require_placement):
+ directory = Path(path).resolve()
+ condition, layout = (_read_json(directory / name) for name in ("condition.json", "layout.json"))
+ scene = layout_to_roomgenbench(condition, layout)
+ if _read_json(directory / "roomgenbench_scene.json") != scene:
+ raise ValueError("RoomGenBench handoff must match the canonical condition and layout")
+ if require_placement and any(o["place_id"] is None for o in scene["objects"]):
+ raise ValueError("this downstream policy requires explicit placement for every requested object")
+ return condition, scene
+
+
+def _extents(parts):
+ import numpy as np
+ vertices = np.vstack([part.vertices for part in parts])
+ if not np.isfinite(vertices).all():
+ raise ValueError("asset vertices must be finite")
+ lengths = vertices.max(0) - vertices.min(0)
+ if (lengths <= 0).any():
+ raise ValueError("asset must have positive three-dimensional extents")
+ return lengths.tolist()
+
+
+def _asset_parts(module, obj, method, assets_dir):
+ """Missing/failed/invalid assets retain a requested instance as a placeholder."""
+ key = obj["asset_key"]
+ sidecar, glb = assets_dir / f"{key}.json", assets_dir / f"{key}.glb"
+ if not sidecar.is_file():
+ return [], {"status": "missing", "reason": "sidecar_missing"}
+ try:
+ record = _read_json(sidecar)
+ if (not isinstance(record, dict) or record.get("asset_key") != key
+ or record.get("method") != method or not isinstance(record.get("status"), str)
+ or record["status"] not in {"ok", "fallback", "failed"}):
+ raise ValueError("sidecar requires matching asset_key/method and explicit ok/fallback/failed status")
+ except (OSError, ValueError) as error:
+ return [], {"status": "invalid_sidecar", "reason": str(error)}
+ status = module.effective_status(method, sidecar)
+ if status == "failed":
+ return [], {"status": "failed", "reason": "method_reported_failure"}
+ if not glb.is_file():
+ return [], {"status": "missing", "reason": "glb_missing"}
+ try:
+ parts = module.load_glb_parts(glb)
+ native = _extents(parts)
+ sage_parts = [part.copy() for part in parts]
+ for part in sage_parts:
+ part.apply_transform(module.GLTF_TO_SAGE)
+ fit_log = {"status": status, "native_size_gltf_m": native,
+ "native_size_sage_local_m": _extents(sage_parts)}
+ fitted = module.fit_asset(parts, obj["dimensions"], fit_log)
+ extents = _extents(fitted)
+ target = [obj["dimensions"][k] for k in ("width", "length", "height")]
+ # Upstream clamps thin native axes before scaling. Report a resulting
+ # target mismatch rather than accepting the method's original ok status.
+ if any(abs(a - b) > max(1e-9, 1e-5 * b) for a, b in zip(extents, target)):
+ fit_log = {**fit_log, "status": "fit_mismatch", "source_status": status}
+ return fitted, {**fit_log, "fitted_size_sage_local_m": extents,
+ "source_glb_sha256": fingerprint(glb), "source_sidecar_sha256": fingerprint(sidecar)}
+ except Exception as error:
+ return [], {"status": "failed", "reason": f"asset_load_or_fit_failed: {type(error).__name__}: {error}"}
+
+
+def _box_parts(module, dimensions):
+ import trimesh
+ lengths = [dimensions[k] for k in ("width", "length", "height")]
+ transform = trimesh.transformations.translation_matrix([0, 0, lengths[2] / 2])
+ return [module.colored_box(lengths, transform, module.PLACEHOLDER_RGBA)]
+
+
+def _objects(module, scene, method, assets_dir, output):
+ logs = []
+ for index, obj in enumerate(scene["objects"]):
+ parts, fit_log = ([], {"status": "box"}) if method == "layout_boxes" else _asset_parts(module, obj, method, assets_dir)
+ kind = "generated_fitted_mesh" if fit_log["status"] == "ok" else (
+ "fitted_mesh_with_size_mismatch" if fit_log["status"] == "fit_mismatch" else
+ "fallback_mesh" if parts else "target_bbox_proxy" if method == "layout_boxes" else "placeholder_bbox")
+ parts = parts or _box_parts(module, obj["dimensions"])
+ node = f"obj_{index:03d}"
+ for part_index, part in enumerate(parts):
+ name = node if len(parts) == 1 else f"{node}_p{part_index}"
+ output.add_geometry(part, node_name=name, geom_name=name,
+ transform=module.SAGE_WORLD_TO_GLTF @ module.place_matrix(obj))
+ logs = logs + [{"id": obj["id"], "asset_key": obj["asset_key"], "type": obj["type"],
+ "place": obj["place"], "support_parent": obj["place_id"],
+ "support_status": obj["support_status"], "geometry_kind": kind,
+ "node": node, **fit_log}]
+ return logs
+
+
+def _shell_parts(module, room):
+ """Reference build_shell geometry with wall normals from the polygon winding, not the bbox centre.
+
+ build_shell extrudes each wall away from the room's bounding-box centre,
+ which pushes some walls of a non-convex polygon into the room. Walls trace
+ the floor polygon in order, so its signed area fixes the interior side;
+ each wall is built alone with a stand-in centre placed on that side.
+ build_shell names every door panel of a wall ``_door``; the second and later
+ panels of one wall get a running suffix (``_1``, ``_2``, ...) so no scene node is overwritten.
+ """
+ parts, walls = module.build_shell({**room, "walls": [], "doors": []}) # floor slab only
+ points = [(w["start_point"]["x"], w["start_point"]["y"]) for w in room["walls"]]
+ ccw = sum(x0 * y1 - x1 * y0 for (x0, y0), (x1, y1) in zip(points, points[1:] + points[:1])) > 0
+ for index, wall in enumerate(room["walls"]):
+ a, b = wall["start_point"], wall["end_point"]
+ dx, dy = b["x"] - a["x"], b["y"] - a["y"]
+ inward = (-dy, dx) if ccw else (dy, -dx)
+ cx, cy = (a["x"] + b["x"]) / 2 + inward[0], (a["y"] + b["y"]) / 2 + inward[1]
+ single = {"dimensions": {"width": 2., "length": 2.}, "position": {"x": cx - 1., "y": cy - 1.},
+ "walls": [wall], "doors": [d for d in room["doors"] if d["wall_id"] == wall["id"]]}
+ wall_parts, meta = module.build_shell(single)
+ seen = Counter()
+ for name, part in wall_parts:
+ if name != "shell_floor":
+ name = name.replace("shell_wall_0", f"shell_wall_{index}", 1)
+ seen[name] += 1
+ parts.append((name if seen[name] == 1 else f"{name}_{seen[name] - 1}", part))
+ walls += [{**meta[0], "name": f"shell_wall_{index}"}]
+ return parts, walls
+
+
+def _display_geometry(module, condition, scene, output, display_height):
+ """Reference shell (polygon walls, door cutouts) plus fixed bbox proxies; the condition is retained.
+
+ Unknown room height renders walls at the display reference height. The
+ reference shell puts its floor at Z=0, so it is translated to the floor.
+ Doors are part of the shell; other fixed objects become bbox proxies.
+ """
+ import trimesh
+ room, floor = condition["room"], condition["room"].get("floor_z_m")
+ height = room.get("height_m")
+ display_floor = 0. if floor is None else floor
+ downstream = scene["room"] if height is not None else {**scene["room"], "walls": [
+ {**wall, "height": display_height} for wall in scene["room"]["walls"]]}
+ shell, walls = _shell_parts(module, downstream)
+ lift = module.SAGE_WORLD_TO_GLTF @ trimesh.transformations.translation_matrix([0., 0., display_floor])
+ for name, part in shell:
+ output.add_geometry(part, node_name=name, geom_name=name, transform=lift)
+ doors = {door["id"] for door in downstream["doors"]}
+ proxies = 0
+ for index, fixed in enumerate(room.get("fixed_objects", [])):
+ if fixed["id"] in doors:
+ continue
+ w, d, h = fixed["size_local_m"]
+ # Same +X -> +Y conversion as the requested-object adapter.
+ obj = {"position": dict(zip("xyz", fixed["bottom_center_m"])),
+ "rotation": {"x": 0., "y": 0., "z": 180 * fixed["yaw_rad"] / 3.141592653589793 - 90},
+ "dimensions": {"width": d, "length": w, "height": h}}
+ node = f"fixed_bbox_{index:03d}"
+ output.add_geometry(_box_parts(module, obj["dimensions"])[0], node_name=node, geom_name=node,
+ transform=module.SAGE_WORLD_TO_GLTF @ module.place_matrix(obj))
+ proxies += 1
+ return walls, proxies, {**downstream["dimensions"],
+ "height": display_height if height is None else height,
+ "height_source": "display_reference" if height is None else "condition",
+ "source_height_m": height, "floor_known": room.get("floor_known", floor is not None),
+ "boundary_known": room.get("boundary_known", True),
+ "shell_geometry_kind": "reference_build_shell_polygon_walls", "wall_normal_source": "floor_polygon_winding",
+ "display_floor_z_m": display_floor, "source_floor_z_m": floor}
+
+
+def assemble_handoff(handoff, output_dir, *, method="layout_boxes", assets_dir=None,
+ roomgenbench_root=None, require_placement=False, display_height_m=3.):
+ """Consume an arbitrary FastFill handoff and optionally generated mesh assets.
+
+ The returned receipt always uses all requested objects as its denominator.
+ `generated_mesh_success` means every requested GLB loaded/fitted with status
+ ok; it does not assert native dimensions, room feasibility or deployment.
+ """
+ if not isinstance(method, str) or not re.fullmatch(r"[a-z][a-z0-9_]{0,63}", method) or method == "sage_gt":
+ raise ValueError("method must be a short snake_case generated method or layout_boxes; sage_gt is unavailable")
+ if method != "layout_boxes" and assets_dir is None:
+ raise ValueError("generated mesh assembly requires --assets-dir")
+ height = finite(display_height_m, "display reference height", positive=True)
+ target = safe_output(output_dir)
+ inputs = [Path(handoff).resolve()] + ([Path(assets_dir).resolve()] if assets_dir is not None else [])
+ if any(target == source or target in source.parents or source in target.parents for source in inputs):
+ raise ValueError("output directory must not overlap handoff or asset inputs")
+ condition, scene = _handoff(handoff, require_placement)
+ reference = Path(roomgenbench_root or Path(__file__).resolve().parents[2] / "RoomGenBench").resolve()
+ if target == reference or target in reference.parents or reference in target.parents:
+ raise ValueError("output directory must remain independent of the reference repository")
+ module, source_info = _reference(reference)
+ import trimesh
+ output = trimesh.Scene()
+ walls, proxies, room = _display_geometry(module, condition, scene, output, height)
+ objects = _objects(module, scene, method, Path(assets_dir).resolve() if assets_dir is not None else None, output)
+ counts = dict(Counter(obj["status"] for obj in objects))
+ generated_success = method != "layout_boxes" and bool(objects) and counts.get("ok", 0) == len(objects)
+ receipt = {"schema_version": "fastfill.roomgenbench-assembly.v1", "scene_key": scene["scene_key"],
+ "scene": scene["scene_key"], "room_type": scene["room_type"], "walls": walls,
+ "method": method, "assembly_complete": True, "generated_mesh_success": generated_success,
+ "requested_objects": len(objects), "counts": counts, "objects": objects, "room": room,
+ "fixed_bbox_proxies": proxies,
+ "condition": deepcopy(condition), "fit_policy": FIT_POLICY if method != "layout_boxes" else "target_bbox_proxy",
+ "original_asset_geometry_acceptance": "not_checked", "support_verification": "not_checked",
+ "validator": "not_attempted", "physics": "not_attempted", "host_commit": "not_attempted",
+ "reference_assembler": source_info}
+ # Build/serialize everything before creating the immutable output directory.
+ payload = output.export(file_type="glb")
+ metadata = json.dumps(receipt, indent=2, allow_nan=False) + "\n"
+ target.mkdir(parents=True, exist_ok=False)
+ (target / f'{scene["scene_key"]}.glb').write_bytes(payload)
+ (target / f'{scene["scene_key"]}.json').write_text(metadata)
+ (target / "receipt.json").write_text(metadata)
+ return receipt
+
+
+def benchmark_request(scene):
+ """FastFill request for a RoomGenBench scene (bench/inputs/scenes/*.json): every object, wall ones included.
+
+ Entries are named ``obj_%04d`` in scene order, the training id style (``obj_0007`` is
+ ``scene["objects"][7]``): the scene's long ids put the restaurant at 9,362 Qwen3 condition tokens,
+ over the 8,192 training ``max_length`` (6,727 with these). Each declares its ``place_id`` ("floor",
+ "wall" or the supporter, renamed alike) as ``support_parent``; room_size_m is [width, length, height].
+ """
+ dims = scene["room"]["dimensions"]
+ ids = {o["id"]: f"obj_{index:04d}" for index, o in enumerate(scene["objects"])}
+ return {"room_type": scene["room_type"], "room_size_m": [dims["width"], dims["length"], dims["height"]],
+ "furniture_list": [{"id": ids[o["id"]], "category": o["type"], "description": o["description"],
+ "support_parent": ids.get(o["place_id"], o["place_id"])} for o in scene["objects"]]}
+
+
+def write_benchmark_requests(scenes_dir, output_dir, *, roomgenbench_root=None):
+ """Write ``.json`` per scene into a new directory outside the reference checkout;
+ returns per-scene counts (requested, floor, wall, on_object)."""
+ from .direct_layout import request_to_condition
+ target = safe_output(output_dir)
+ reference = Path(roomgenbench_root or Path(__file__).resolve().parents[2] / "RoomGenBench").resolve()
+ if target == reference or reference in target.parents:
+ raise ValueError("request directory must remain outside the reference repository")
+ scenes = sorted(Path(scenes_dir).glob("*.json"))
+ if not scenes:
+ raise ValueError(f"no RoomGenBench scene JSON in {scenes_dir}")
+ payloads, summary = {}, []
+ for path in scenes:
+ scene = _read_json(path)
+ request = benchmark_request(scene)
+ request_to_condition(request) # schema check only, with predict --request's default max_objects
+ places = Counter(o["support_parent"] if o["support_parent"] in ("floor", "wall") else "on_object"
+ for o in request["furniture_list"])
+ payloads[f'{scene["scene_key"]}.json'] = json.dumps(request, indent=2) + "\n"
+ summary.append({"scene_key": scene["scene_key"], "requested": len(request["furniture_list"]),
+ **{place: places[place] for place in ("floor", "wall", "on_object")}})
+ if len(payloads) != len(scenes):
+ raise ValueError("RoomGenBench scene keys must be unique")
+ target.mkdir(parents=True, exist_ok=False)
+ for name, payload in payloads.items():
+ (target / name).write_text(payload)
+ return summary
+
+
+def ground_truth_layout(condition, scene):
+ """FastFill layout of a benchmark scene: the exact inverse of ``layout_to_roomgenbench``.
+
+ Request ``obj_%04d`` is ``scene["objects"][i]`` (``benchmark_request``); size = [length, width,
+ height], bottom centre = position, yaw = radians(rotation.z) + pi/2 wrapped; rotation.x / .y
+ (benchmark tilts) have no FastFill counterpart and are dropped. Misaligned ids, categories or
+ counts raise ValueError.
+ """
+ import math
+ from .geometry import wrap_yaw
+ from .schema import SCHEMA_VERSION, validate_layout
+ requested, truth = condition["objects"], scene["objects"]
+ if len(requested) != len(truth):
+ raise ValueError(f"request has {len(requested)} objects, benchmark scene {len(truth)}")
+ objects = []
+ for index, (request, obj) in enumerate(zip(requested, truth)):
+ if request["id"] != f"obj_{index:04d}" or request["category"] != obj["type"]:
+ raise ValueError(f"request object {index} ({request['id']}, {request['category']}) is not "
+ f"benchmark object {index} (obj_{index:04d}, {obj['type']})")
+ d = obj["dimensions"]
+ objects.append({"id": request["id"], "target_size_local_m": [d["length"], d["width"], d["height"]],
+ "bottom_center_m": [obj["position"][k] for k in "xyz"],
+ "yaw_rad": wrap_yaw(math.radians(obj["rotation"]["z"]) + math.pi / 2)})
+ return validate_layout({"schema_version": SCHEMA_VERSION, "objects": objects}, condition)
+
+
+def reference_check(handoff_dir, scene_path):
+ """Same-rule comparison of a hand-off with its benchmark scene's ground truth.
+
+ validate_scene(hand-off condition) runs on both layouts (per check code: pass / violation /
+ unknown). Exchangeable requests (evaluate's groups, ``matching.group_labels``: identical request
+ fields but the id, support parent included, and a certified swap) are matched: within each group
+ predictions are assigned to truth objects by a Hungarian assignment on bottom-centre distance
+ (``predicted_id`` names the one matched to each truth row). Per-object FastFill-vs-truth errors
+ are pooled over all objects and per ground-truth place: bottom-centre distance and, primary, the
+ box-equivalent (size, yaw) of evaluate's ``_box_equivalent_errors`` (one joint minimum over yaw +
+ k pi/2 with sx/sy swapped for odd k: the same box scores 0), here for every object (evaluate uses it
+ only for ``size_axis_swap_allowed`` ones); ``*_marginal_min`` minimise separately (mean |log size
+ ratio| over the (sx, sy) swap, yaw modulo pi), and ``*_by_id`` score the prediction of the same id,
+ without matching. ``box_equivalent_*`` repeat the primary values under their schema-v1 names.
+ Baselines: room centre (``room_center_position_error_m``, floor-level centre of the room's XY bounds)
+ and uniform yaw on the same box-equivalent footing: ``uniform_yaw_baseline_error_rad`` is the exact
+ expected box-equivalent yaw error of a uniformly random yaw with the truth's own size (per object and
+ pooled), ``uniform_yaw_baseline_error_rad_marginal_min`` the pi / 4 of ``yaw_error_rad_marginal_min``.
+ """
+ import math
+ import numpy as np
+ from scipy.optimize import linear_sum_assignment
+ from .evaluate import _box_equivalent_errors, _log_size_error, _uniform_yaw_error, _yaw_error
+ from .direct_layout import place_of
+ from .matching import group_labels
+ from .schema import normalize_room
+ from .validation import CHECK_STATUSES, validate_scene
+ condition, _ = _handoff(handoff_dir, False)
+ layout, scene = _read_json(Path(handoff_dir) / "layout.json"), _read_json(scene_path)
+ truth = ground_truth_layout(condition, scene)
+ checks = {}
+ for name, candidate in (("fastfill", layout), ("ground_truth", truth)):
+ report = validate_scene(condition, candidate["objects"])
+ by_code = {}
+ for check in report["checks"]:
+ by_code.setdefault(check["code"], dict.fromkeys(CHECK_STATUSES, 0))[check["status"]] += 1
+ checks[name] = {"ok": report["ok"], "counts": report["counts"], "by_code": by_code}
+ origin, scale = normalize_room(condition["room"])
+ center = [origin[0] + scale[0] / 2, origin[1] + scale[1] / 2, origin[2]]
+ predicted, truth_by_id = {o["id"]: o for o in layout["objects"]}, {o["id"]: o for o in truth["objects"]}
+ groups = {} # evaluate's exchangeable groups (every truth position is known); ungrouped requests match themselves
+ labels = group_labels(condition["objects"], condition.get("constraints", []), [[True] * 3] * len(condition["objects"]))
+ for request, label in zip(condition["objects"], labels):
+ groups.setdefault(label or request["id"], []).append(request["id"])
+ matched = {} # truth id -> the predicted id assigned to it
+ for ids in groups.values():
+ cost = [[math.dist(predicted[a]["bottom_center_m"], truth_by_id[b]["bottom_center_m"]) for b in ids] for a in ids]
+ for a, b in zip(*linear_sum_assignment(np.asarray(cost))):
+ matched[ids[b]] = ids[a]
+
+ def errors(p, t):
+ (sx, sy, sz), (box_size, box_yaw) = t["target_size_local_m"], _box_equivalent_errors(p, t, True, True)
+ return {"position_error_m": math.dist(p["bottom_center_m"], t["bottom_center_m"]),
+ "log_size_error": box_size, "yaw_error_rad": box_yaw,
+ "log_size_error_marginal_min": min(_log_size_error(p["target_size_local_m"], s) for s in ((sx, sy, sz), (sy, sx, sz))),
+ "yaw_error_rad_marginal_min": _yaw_error(p["yaw_rad"], t["yaw_rad"], 2)}
+
+ rows = []
+ for t, obj in zip(truth["objects"], scene["objects"]):
+ row = errors(predicted[matched[t["id"]]], t)
+ rows.append({"id": t["id"], "predicted_id": matched[t["id"]], "category": obj["type"], "place": place_of(obj["place_id"]),
+ **row, "room_center_position_error_m": math.dist(center, t["bottom_center_m"]),
+ "uniform_yaw_baseline_error_rad": _uniform_yaw_error(4, True, t["target_size_local_m"]),
+ "box_equivalent_log_size_error": row["log_size_error"], "box_equivalent_yaw_error_rad": row["yaw_error_rad"],
+ **{f"{key}_by_id": value for key, value in errors(predicted[t["id"]], t).items()}})
+ metrics = [key for key in rows[0] if key not in ("id", "predicted_id", "category", "place")] if rows else []
+ pool = lambda group: {"objects": len(group), **{key: float(np.mean([r[key] for r in group])) if group else None
+ for key in metrics}}
+ places = sorted({r["place"] for r in rows})
+ return {"schema_version": "fastfill.roomgenbench-reference-check.v2", "handoff": str(Path(handoff_dir).resolve()),
+ "scene": str(Path(scene_path).resolve()), "scene_key": scene.get("scene_key"), "objects": len(rows),
+ "ground_truth_tilted_objects": sum(max(abs(o["rotation"].get(k, 0)) for k in "xy") > 1.
+ for o in scene["objects"]), # > 1 degree about X or Y; dropped upright
+ "checks": checks, "errors": {"all": pool(rows), **{place: pool([r for r in rows if r["place"] == place])
+ for place in places}},
+ "uniform_yaw_baseline_error_rad": pool(rows)["uniform_yaw_baseline_error_rad"] if rows else None,
+ "uniform_yaw_baseline_error_rad_marginal_min": math.pi / 4, "per_object": rows}
+
+
+def main(argv=None):
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--handoff", help="directory from predict --export-dir")
+ parser.add_argument("--output-dir")
+ parser.add_argument("--method", default="layout_boxes")
+ parser.add_argument("--assets-dir", help="method GLB/JSON sidecar directory")
+ parser.add_argument("--roomgenbench-root")
+ parser.add_argument("--require-placement", action="store_true")
+ parser.add_argument("--display-height-m", type=float, default=3.)
+ parser.add_argument("--requests-from", help="instead of assembling: RoomGenBench scenes directory to turn into requests")
+ parser.add_argument("--requests-out", help="new directory for the .json requests of --requests-from")
+ parser.add_argument("--reference-check", metavar="HANDOFF", help="instead of assembling: compare HANDOFF with --scene")
+ parser.add_argument("--scene", help="benchmark scene JSON (bench/inputs/scenes/.json) for --reference-check")
+ parser.add_argument("--output", help="new JSON report path for --reference-check")
+ args = parser.parse_args(argv)
+ if args.reference_check is not None or args.scene is not None or args.output is not None:
+ if None in (args.reference_check, args.scene, args.output) or args.handoff or args.output_dir or args.requests_from or args.requests_out:
+ parser.error("--reference-check, --scene and --output go together, without other modes")
+ target = safe_output(args.output)
+ report = reference_check(args.reference_check, args.scene)
+ target.parent.mkdir(parents=True, exist_ok=True)
+ with target.open("x") as stream:
+ stream.write(json.dumps(report, indent=2, allow_nan=False) + "\n")
+ print(json.dumps({"output": str(target), "objects": report["objects"],
+ **{name: {"ok": c["ok"], **c["counts"]} for name, c in report["checks"].items()}}))
+ return 0
+ if args.requests_from is not None or args.requests_out is not None:
+ if args.requests_from is None or args.requests_out is None or args.handoff or args.output_dir:
+ parser.error("--requests-from and --requests-out go together, without --handoff/--output-dir")
+ for row in write_benchmark_requests(args.requests_from, args.requests_out, roomgenbench_root=args.roomgenbench_root):
+ print(json.dumps(row))
+ return 0
+ if args.handoff is None or args.output_dir is None:
+ parser.error("the following arguments are required: --handoff, --output-dir")
+ receipt = assemble_handoff(args.handoff, args.output_dir, method=args.method, assets_dir=args.assets_dir,
+ roomgenbench_root=args.roomgenbench_root, require_placement=args.require_placement, display_height_m=args.display_height_m)
+ print(json.dumps({"output_dir": str(Path(args.output_dir).resolve()), "counts": receipt["counts"],
+ "generated_mesh_success": receipt["generated_mesh_success"]}))
+ return 0 if args.method == "layout_boxes" or receipt["generated_mesh_success"] else 2
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/code/fastfill/v2/runtime.py b/code/fastfill/v2/runtime.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4bf7251ad3cfdba01d50d1b6e97ad5c991df2d3
--- /dev/null
+++ b/code/fastfill/v2/runtime.py
@@ -0,0 +1,448 @@
+"""Offline v2 asset loop and the external WorldEdge transaction contract.
+
+Asset.canonical_transform maps raw asset coordinates (including its pivot,
+source scale/axis conventions) to a metre, Z-up, bbox-bottom-centred frame.
+The world transform is T(bottom_center) @ Rz(yaw) @ canonical_transform.
+No mesh rescaling or deletion is performed to satisfy validation.
+
+Callbacks must implement their own I/O timeout: max_seconds is checked at
+callback boundaries, rather than claiming to interrupt arbitrary host code.
+"""
+from copy import deepcopy
+from dataclasses import dataclass
+import hashlib
+import json
+import math
+from threading import RLock
+from time import monotonic
+from typing import Protocol
+
+from .validation import _capability_names, _polygon, _validate_support_bbox, _vector, effective_support_requests, validate_required_levels, validate_scene
+
+
+IDENTITY = ((1., 0., 0., 0.), (0., 1., 0., 0.), (0., 0., 1., 0.), (0., 0., 0., 1.))
+
+
+@dataclass(frozen=True)
+class SupportSurface:
+ """Verified horizontal surface in the asset canonical frame, not bbox top."""
+ surface_id: str
+ local_polygon_xy_m: tuple
+ local_z_m: float
+
+ def __post_init__(self):
+ if not isinstance(self.surface_id, str) or not self.surface_id:
+ raise ValueError("support surface needs a stable ID")
+ _polygon(self.local_polygon_xy_m)
+ _vector((self.local_z_m,), 1)
+ object.__setattr__(self, "local_polygon_xy_m", tuple(tuple(p) for p in self.local_polygon_xy_m))
+
+ def as_dict(self):
+ return {"surface_id": self.surface_id, "local_polygon_xy_m": [list(p) for p in self.local_polygon_xy_m],
+ "local_z_m": self.local_z_m}
+
+
+@dataclass(frozen=True)
+class Asset:
+ ref: str
+ category: str
+ actual_size_local_m: tuple
+ description: str = ""
+ canonical_transform: tuple = IDENTITY
+ semantic_front_local: tuple | None = None
+ capabilities: tuple | None = None
+ support_surfaces: tuple = ()
+ provenance: tuple = ()
+
+ def __post_init__(self):
+ if not isinstance(self.ref, str) or not self.ref.strip() or not isinstance(self.category, str) or not self.category.strip():
+ raise ValueError("asset needs a reference and category")
+ if not isinstance(self.description, str):
+ raise ValueError("asset description must be a string")
+ object.__setattr__(self, "actual_size_local_m", _vector(self.actual_size_local_m, 3, positive=True))
+ rows = tuple(_vector(row, 4) for row in self.canonical_transform)
+ if len(rows) != 4 or rows[3] != (0., 0., 0., 1.):
+ raise ValueError("canonical_transform must be a finite affine 4x4 transform")
+ determinant = (rows[0][0]*(rows[1][1]*rows[2][2]-rows[1][2]*rows[2][1]) -
+ rows[0][1]*(rows[1][0]*rows[2][2]-rows[1][2]*rows[2][0]) +
+ rows[0][2]*(rows[1][0]*rows[2][1]-rows[1][1]*rows[2][0]))
+ if abs(determinant) < 1e-12:
+ raise ValueError("canonical_transform is singular")
+ object.__setattr__(self, "canonical_transform", rows)
+ if self.semantic_front_local is not None:
+ front = _vector(self.semantic_front_local, 3)
+ if math.hypot(front[0], front[1]) < 1e-12 or abs(front[2]) > 1e-6:
+ raise ValueError("semantic front must be a nonzero horizontal direction")
+ if front[0] <= 0 or abs(front[1] / math.hypot(front[0], front[1])) > 1e-6:
+ raise ValueError("known semantic front must already be canonical +X; normalize asset size and transform upstream")
+ object.__setattr__(self, "semantic_front_local", (1., 0., 0.))
+ if self.capabilities is not None:
+ object.__setattr__(self, "capabilities", _capability_names(self.capabilities))
+ if any(not isinstance(surface, SupportSurface) for surface in self.support_surfaces):
+ raise ValueError("invalid verified support surface metadata")
+ _validate_support_bbox([surface.as_dict() for surface in self.support_surfaces], self.actual_size_local_m)
+ object.__setattr__(self, "support_surfaces", tuple(self.support_surfaces))
+ if not isinstance(self.provenance, (tuple, list)) or any(not isinstance(x, str) for x in self.provenance):
+ raise ValueError("asset provenance must be a list/tuple of source references")
+ object.__setattr__(self, "provenance", tuple(self.provenance))
+
+
+def _eligible(asset, request, prediction):
+ # Asset has no verified typed-attribute evidence contract. Description text
+ # or an arbitrary metadata claim cannot satisfy explicit color/material/etc.
+ if request.get("attributes"):
+ return False
+ if asset.category.casefold().strip() != request["category"].casefold().strip():
+ return False
+ required = set(request.get("required_capabilities", ()))
+ if required and (asset.capabilities is None or not required.issubset(asset.capabilities)):
+ return False
+ if request.get("semantic_front_required") and asset.semantic_front_local is None:
+ return False
+ size = asset.actual_size_local_m
+ fixed = request.get("fixed_size_local_m")
+ if fixed is not None and any(b is not None and abs(a-b) > 1e-6 for a, b in zip(size, fixed)):
+ return False
+ bounds = request.get("size_bounds_local_m")
+ if bounds is not None and any(x < lo-1e-6 or x > hi+1e-6 for x, lo, hi in zip(size, bounds["min"], bounds["max"])):
+ return False
+ tolerance = request.get("retrieval_tolerance_log", request.get("retrieval_tolerance"))
+ if tolerance is not None:
+ limits = (tolerance,) * 3 if isinstance(tolerance, (int, float)) else tuple(tolerance)
+ if len(limits) != 3 or any(not math.isfinite(x) or x < 0 for x in limits):
+ raise ValueError("retrieval_tolerance is a scalar or three max absolute log ratios")
+ if any(abs(math.log(a/b)) > limit+1e-12 for a, b, limit in
+ zip(size, prediction["target_size_local_m"], limits)):
+ return False
+ return True
+
+
+class Resolver(Protocol):
+ def resolve(self, request_object, prediction, *, excluded_refs=()) -> Asset | None: ...
+
+
+@dataclass(frozen=True)
+class CatalogResolver:
+ """Category/capability reference catalog; unverified typed attributes reject.
+
+ Natural-language descriptions are not a verified attribute evidence source.
+ Attribute-bearing requests need an explicit external evidence contract before
+ this reference loop can admit them.
+ """
+ assets: tuple
+
+ def __post_init__(self):
+ if any(not isinstance(asset, Asset) for asset in self.assets):
+ raise ValueError("catalog entries must be Asset instances")
+ if len({a.ref for a in self.assets}) != len(self.assets):
+ raise ValueError("duplicate asset references")
+ object.__setattr__(self, "assets", tuple(self.assets))
+
+ def resolve(self, request_object, prediction, *, excluded_refs=()):
+ options = tuple(a for a in self.assets if a.ref not in excluded_refs and _eligible(a, request_object, prediction))
+ return min(options, key=lambda a: (sum(abs(math.log(x/y)) for x, y in
+ zip(a.actual_size_local_m, prediction["target_size_local_m"])), a.ref), default=None)
+
+
+def _transform(asset, position, yaw):
+ c, s = math.cos(yaw), math.sin(yaw)
+ world = ((c, -s, 0, position[0]), (s, c, 0, position[1]), (0, 0, 1, position[2]), (0, 0, 0, 1))
+ return [[sum(world[i][k]*asset.canonical_transform[k][j] for k in range(4)) for j in range(4)] for i in range(4)]
+
+
+def reconcile(assets, condition, prediction):
+ """Resolve actual geometry and propagate verified support heights without changing targets."""
+ requests = {obj["id"]: obj for obj in effective_support_requests(condition)}
+ records = {obj["id"]: {**deepcopy(obj), "target_bottom_center_m": list(obj["bottom_center_m"]),
+ "target_yaw_rad": obj["yaw_rad"], "actual_size_local_m": list(assets[obj["id"]].actual_size_local_m),
+ "asset_ref": assets[obj["id"]].ref,
+ "capabilities": list(assets[obj["id"]].capabilities) if assets[obj["id"]].capabilities is not None else None,
+ "semantic_front_local": list(assets[obj["id"]].semantic_front_local) if
+ assets[obj["id"]].semantic_front_local is not None else None,
+ "support_parent": requests[obj["id"]].get("support_parent"),
+ "support_surfaces": [surface.as_dict() for surface in assets[obj["id"]].support_surfaces],
+ "provenance": list(assets[obj["id"]].provenance)} for obj in prediction["objects"]}
+ done = frozenset()
+ fixed = {obj["id"]: obj for obj in condition["room"].get("fixed_objects", ())}
+ while len(done) < len(records):
+ ready = tuple(ident for ident in records if ident not in done and
+ (requests[ident].get("support_parent") not in records or requests[ident].get("support_parent") in done))
+ if not ready:
+ raise ValueError("support graph contains a cycle")
+ for ident in ready:
+ parent = requests[ident].get("support_parent")
+ parent_obj = records.get(parent, fixed.get(parent))
+ surfaces = parent_obj.get("support_surfaces", ()) if parent_obj else ()
+ surface_id = requests[ident].get("support_surface_id")
+ surfaces = tuple(sf for sf in surfaces if surface_id is None or sf["surface_id"] == surface_id)
+ # Multiple unselected surfaces are ambiguous and remain unknown until a selector is supplied.
+ if len(surfaces) == 1:
+ p = records[ident]["bottom_center_m"]
+ records = {**records, ident: {**records[ident], "bottom_center_m":
+ [p[0], p[1], parent_obj["bottom_center_m"][2]+surfaces[0]["local_z_m"]],
+ "support_surface_id": surfaces[0]["surface_id"]}}
+ done = done.union(ready)
+ return [{**records[obj["id"]], "asset_transform": _transform(assets[obj["id"]],
+ records[obj["id"]]["bottom_center_m"], records[obj["id"]]["yaw_rad"])} for obj in prediction["objects"]]
+
+
+@dataclass(frozen=True)
+class RuntimeBudget:
+ max_asset_retries: int = 2
+ max_repair_calls: int = 0
+ max_seconds: float = 10.0
+
+ def __post_init__(self):
+ for count in (self.max_asset_retries, self.max_repair_calls):
+ if isinstance(count, bool) or not isinstance(count, int) or count < 0:
+ raise ValueError("retry and repair budgets must be nonnegative integers")
+ if isinstance(self.max_seconds, bool) or not math.isfinite(self.max_seconds) or self.max_seconds < 0:
+ raise ValueError("time budget must be finite and nonnegative")
+
+
+@dataclass(frozen=True)
+class BoundedTranslationRepair:
+ """Small deterministic XY move for boundary/box penetration; no geometry changes.
+
+ Moves one offending object and its support descendants per call. Other
+ constraints may still fail, so every returned scene requires revalidation.
+ This is a reference local repair, not a proof of feasible re-layout.
+ """
+ max_step_m: float = .25
+
+ def __post_init__(self):
+ if isinstance(self.max_step_m, bool) or not math.isfinite(self.max_step_m) or self.max_step_m <= 0:
+ raise ValueError("translation step must be finite and positive")
+
+ def __call__(self, condition, objects, report):
+ index = {obj["id"]: obj for obj in objects}
+ failure = next((check for check in report["checks"] if check["status"] == "violation" and
+ check["code"] in ("boundary", "collision", "fixed_collision")), None)
+ if failure is None:
+ return deepcopy(objects)
+ ident = next((ident for ident in failure["object_ids"] if ident in index), None)
+ if ident is None:
+ return deepcopy(objects)
+ obj = index[ident]
+ if failure["code"] == "boundary":
+ if condition["room"].get("boundary_known") is False:
+ return deepcopy(objects)
+ point = _polygon(condition["room"]["floor_polygon_xy_m"]).representative_point()
+ delta = (point.x - obj["bottom_center_m"][0], point.y - obj["bottom_center_m"][1])
+ else:
+ fixed = {fixed["id"]: fixed for fixed in condition["room"].get("fixed_objects", ())}
+ other = next((index.get(i, fixed.get(i)) for i in failure["object_ids"] if i != ident), None)
+ if other is None:
+ return deepcopy(objects)
+ delta = tuple(obj["bottom_center_m"][q]-other["bottom_center_m"][q] for q in (0, 1))
+ norm = math.hypot(*delta)
+ delta = tuple(x * min(self.max_step_m, norm)/norm for x in delta) if norm > 1e-12 else (self.max_step_m, 0.)
+ parents = {req["id"]: req.get("support_parent") for req in effective_support_requests(condition)}
+ affected = frozenset((ident,))
+ while True:
+ expanded = affected.union(child for child, parent in parents.items() if parent in affected)
+ if expanded == affected:
+ break
+ affected = expanded
+ return [{**obj, "bottom_center_m": [obj["bottom_center_m"][0]+delta[0],
+ obj["bottom_center_m"][1]+delta[1], obj["bottom_center_m"][2]]}
+ if obj["id"] in affected else deepcopy(obj) for obj in objects]
+
+
+class Host(Protocol):
+ """A real adapter must atomically check version, deduplicate, and commit all objects."""
+ def commit(self, objects, *, idempotency_key, expected_world_version) -> dict: ...
+
+
+@dataclass(frozen=True)
+class _HostState:
+ version: int = 0
+ objects: tuple = ()
+ requests: tuple = ()
+
+
+class AtomicMemoryHost:
+ """Offline reference transaction sink; never changes an external world."""
+ def __init__(self):
+ self._state = _HostState()
+ self._lock = RLock()
+
+ def snapshot(self):
+ with self._lock:
+ return {"version": self._state.version, "objects": deepcopy(list(self._state.objects)),
+ "commits": len(self._state.requests)}
+
+ def commit(self, objects, *, idempotency_key, expected_world_version):
+ if not isinstance(idempotency_key, str) or not idempotency_key:
+ raise ValueError("commit requires a stable idempotency key")
+ if isinstance(expected_world_version, bool) or not isinstance(expected_world_version, int):
+ raise ValueError("commit requires an integer world version")
+ payload = deepcopy(list(objects))
+ fingerprint = hashlib.sha256(json.dumps(payload, sort_keys=True, allow_nan=False).encode()).hexdigest()
+ with self._lock:
+ previous = next((x for x in self._state.requests if x[0] == idempotency_key), None)
+ if previous:
+ if previous[1] != fingerprint:
+ raise ValueError("idempotency key already used for a different scene")
+ return {"committed": True, "world_version": previous[2], "idempotent": True}
+ if self._state.version != expected_world_version:
+ raise ValueError("world version conflict")
+ ids = [obj.get("id") for obj in payload]
+ existing = {obj["id"] for obj in self._state.objects}
+ if any(not isinstance(ident, str) or not ident for ident in ids) or len(ids) != len(set(ids)) or existing.intersection(ids):
+ raise ValueError("commit object IDs must be new, nonempty and unique")
+ version = self._state.version + 1
+ self._state = _HostState(version, self._state.objects + tuple(payload),
+ self._state.requests + ((idempotency_key, fingerprint, version),))
+ return {"committed": True, "world_version": version, "idempotent": False}
+
+
+def _repair_is_pose_only(before, after):
+ if not isinstance(after, (list, tuple)) or len(after) != len(before):
+ return False
+ index = {obj.get("id"): obj for obj in after if isinstance(obj, dict)}
+ if len(index) != len(before) or set(index) != {obj["id"] for obj in before}:
+ return False
+ allowed = {"bottom_center_m", "yaw_rad", "asset_transform"}
+ return all({k: v for k, v in obj.items() if k not in allowed} ==
+ {k: v for k, v in index[obj["id"]].items() if k not in allowed} for obj in before)
+
+
+def _result(condition, raw, target, first, final, first_objects, objects, diagnostics, counts, start, commit=None):
+ n = len(condition.get("objects", ())) if isinstance(condition, dict) else 0
+ mismatch = [sum(abs(math.log(a/b)) for a, b in zip(obj["actual_size_local_m"], obj["target_size_local_m"]))
+ for obj in objects]
+ return {"ok": bool(final and final["ok"] and not counts.get("fatal")), "committed": bool(commit and commit.get("committed")),
+ "raw_model_output": deepcopy(raw), "actual_first_pass_objects": deepcopy(first_objects),
+ "final_objects": deepcopy(objects), "validation": {"target": target, "actual_first_pass": first, "final": final},
+ "diagnostics": list(diagnostics), "commit": commit,
+ "attempts": list(counts.get("attempts", ())),
+ "metrics": {"model": {"schema_success": counts.get("schema_success", False),
+ "requested_ids_exactly_once": counts.get("schema_success", False),
+ "positive_valid_size": counts.get("schema_success", False),
+ "target_geometry_valid": bool(target and target["ok"])},
+ "asset": {"retrieval_coverage": counts.get("resolved", 0)/n if n else 1.,
+ "resolver_calls": counts.get("resolver_calls", 0),
+ "target_actual_log_size_l1_mean": sum(mismatch)/len(mismatch) if mismatch else None,
+ "capability_satisfaction": bool(objects) and not counts.get("asset_filter_failure", False),
+ "first_pass_actual_geometry_validation": bool(first and first["ok"])},
+ "system": {"repaired_success": bool(final and final["ok"] and
+ (counts.get("repair_calls", 0) or counts.get("asset_retries", 0))),
+ "fallback_rate": 0., "final_commit": bool(commit and commit.get("committed")),
+ "commit_attempted": counts.get("commit_attempted", False),
+ "solver_success": None, "fastfill_latency_ms": None,
+ "end_to_end_latency_ms": (monotonic()-start)*1000,
+ "asset_retries": counts.get("asset_retries", 0), "repair_calls": counts.get("repair_calls", 0)}}}
+
+
+def run_pipeline(condition, prediction, resolver, *, host=None, budget=RuntimeBudget(), repair=None,
+ expected_world_version=None, idempotency_key=None, required_levels=("bbox",)):
+ """Resolve, reconcile, validate, bounded retry/pose repair, then one atomic commit.
+
+ repair(condition, actual_objects, report) receives copies and may change
+ positions/yaw only. It must explicitly account for support dependencies.
+ A real host adapter carries responsibility for an atomic external transaction;
+ this function never calls it for a failed or unknown required validation.
+ """
+ start, diagnostics, counts = monotonic(), (), {}
+ raw, objects, first_objects, target, first, final = deepcopy(prediction), [], [], None, None, None
+ def finish(commit=None):
+ return _result(condition, raw, target, first, final, first_objects, objects, diagnostics, counts, start, commit)
+ def expired():
+ return monotonic()-start >= budget.max_seconds
+ if expired():
+ diagnostics = ({"code": "budget_exhausted", "phase": "start"},)
+ return finish()
+ try:
+ from .schema import validate_condition, validate_layout
+ required_levels = validate_required_levels(required_levels)
+ validate_condition(condition)
+ validate_layout(prediction, condition)
+ requests = {obj["id"]: obj for obj in effective_support_requests(condition)}
+ counts = {**counts, "schema_success": True}
+ target = validate_scene(condition, prediction["objects"], required_levels=required_levels)
+ predictions = {o["id"]: o for o in prediction["objects"]}
+ assets, excluded = {}, {ident: () for ident in requests}
+ for ident in requests:
+ if expired():
+ raise TimeoutError("asset_resolution")
+ counts = {**counts, "resolver_calls": counts.get("resolver_calls", 0)+1}
+ asset = resolver.resolve(deepcopy(requests[ident]), deepcopy(predictions[ident]), excluded_refs=())
+ counts = {**counts, "attempts": counts.get("attempts", ())+
+ ({"type": "asset_resolution", "object_id": ident, "asset_ref": asset.ref if isinstance(asset, Asset) else None},)}
+ if asset is None or not isinstance(asset, Asset) or not _eligible(asset, requests[ident], predictions[ident]):
+ diagnostics += ({"code": "asset_unavailable", "object_id": ident,
+ "requirements": deepcopy(requests[ident]),
+ "target_size_local_m": list(predictions[ident]["target_size_local_m"])},)
+ counts = {**counts, "asset_filter_failure": True}
+ return finish()
+ assets = {**assets, ident: asset}
+ counts = {**counts, "resolved": len(assets)}
+ objects = reconcile(assets, condition, prediction)
+ first_objects = deepcopy(objects)
+ first = final = validate_scene(condition, objects, stage="actual", required_levels=required_levels)
+ while not final["ok"] and counts.get("asset_retries", 0) < budget.max_asset_retries:
+ if expired():
+ raise TimeoutError("asset_retry")
+ failed = tuple(ident for c in final["checks"] if c["hard"] and c["status"] != "pass" for ident in c["object_ids"])
+ candidates = tuple(ident for ident in requests if ident in failed) or tuple(requests)
+ changed = False
+ for ident in candidates:
+ if counts.get("asset_retries", 0) >= budget.max_asset_retries:
+ break
+ if expired():
+ raise TimeoutError("asset_retry")
+ excluded = {**excluded, ident: excluded[ident]+(assets[ident].ref,)}
+ counts = {**counts, "asset_retries": counts.get("asset_retries", 0)+1,
+ "resolver_calls": counts.get("resolver_calls", 0)+1}
+ asset = resolver.resolve(deepcopy(requests[ident]), deepcopy(predictions[ident]), excluded_refs=excluded[ident])
+ counts = {**counts, "attempts": counts.get("attempts", ())+
+ ({"type": "asset_retry", "object_id": ident, "asset_ref": asset.ref if isinstance(asset, Asset) else None,
+ "excluded_refs": list(excluded[ident])},)}
+ if asset is not None and isinstance(asset, Asset) and asset.ref not in excluded[ident] and _eligible(asset, requests[ident], predictions[ident]):
+ assets, changed = {**assets, ident: asset}, True
+ diagnostics += ({"code": "asset_reselected", "object_id": ident, "asset_ref": asset.ref},)
+ break
+ if not changed:
+ break
+ objects = reconcile(assets, condition, prediction)
+ final = validate_scene(condition, objects, stage="actual", required_levels=required_levels)
+ while not final["ok"] and repair is not None and counts.get("repair_calls", 0) < budget.max_repair_calls:
+ if expired():
+ raise TimeoutError("repair")
+ counts = {**counts, "repair_calls": counts.get("repair_calls", 0)+1}
+ repaired = repair(deepcopy(condition), deepcopy(objects), deepcopy(final))
+ counts = {**counts, "attempts": counts.get("attempts", ())+
+ ({"type": "pose_repair", "call": counts["repair_calls"]},)}
+ if not _repair_is_pose_only(objects, repaired):
+ diagnostics += ({"code": "invalid_repair", "reason": "repair must preserve IDs, targets, assets and actual sizes"},)
+ break
+ objects = [{**obj, "asset_transform": _transform(assets[obj["id"]], obj["bottom_center_m"], obj["yaw_rad"])} for obj in repaired]
+ final = validate_scene(condition, objects, stage="actual", required_levels=required_levels)
+ if expired():
+ raise TimeoutError("before_commit")
+ if not final["ok"]:
+ diagnostics += ({"code": "validation_failed", "required_failures":
+ [c for c in final["checks"] if c["hard"] and c["status"] != "pass"]},)
+ return finish()
+ if host is not None:
+ try:
+ counts = {**counts, "commit_attempted": True}
+ commit = host.commit(deepcopy(objects), idempotency_key=idempotency_key,
+ expected_world_version=expected_world_version)
+ if not isinstance(commit, dict) or commit.get("committed") is not True:
+ raise ValueError("host did not acknowledge an atomic commit")
+ return finish(commit)
+ except (ValueError, RuntimeError, OSError) as exc:
+ diagnostics += ({"code": "commit_failed", "message": str(exc)},)
+ counts = {**counts, "fatal": True}
+ return finish()
+ return finish()
+ except TimeoutError as exc:
+ diagnostics += ({"code": "budget_exhausted", "phase": str(exc)},)
+ except (ValueError, KeyError, TypeError) as exc:
+ diagnostics += ({"code": "invalid_input_or_geometry", "message": str(exc)},)
+ except (RuntimeError, OSError) as exc:
+ diagnostics += ({"code": "runtime_failure", "message": str(exc)},)
+ counts = {**counts, "fatal": True}
+ return finish()
diff --git a/code/fastfill/v2/schema.py b/code/fastfill/v2/schema.py
new file mode 100644
index 0000000000000000000000000000000000000000..2656d41b591f9f974fecc4dce2ae8d5a752dd9c2
--- /dev/null
+++ b/code/fastfill/v2/schema.py
@@ -0,0 +1,338 @@
+"""Strict canonical protocol. Validation never repairs or mutates inputs."""
+from __future__ import annotations
+
+import math
+from collections.abc import Mapping, Sequence
+
+from fastfill.v2 import SCHEMA_VERSION
+
+
+def _keys(value, allowed, required, label):
+ if not isinstance(value, Mapping):
+ raise ValueError(f"{label} must be an object")
+ if set(value) - set(allowed):
+ raise ValueError(f"{label}: unknown fields {sorted(set(value) - set(allowed))}")
+ if set(required) - set(value):
+ raise ValueError(f"{label}: missing fields {sorted(set(required) - set(value))}")
+
+
+def finite(value, label="value", positive=False):
+ if isinstance(value, bool) or not isinstance(value, (int, float)) or not math.isfinite(value):
+ raise ValueError(f"{label} must be a finite number")
+ if positive and value <= 0:
+ raise ValueError(f"{label} must be strictly positive")
+ return float(value)
+
+
+def vector(value, n=3, label="vector", positive=False):
+ if not isinstance(value, (list, tuple)) or len(value) != n:
+ raise ValueError(f"{label} must have length {n}")
+ return [finite(v, label, positive) for v in value]
+
+
+def _id(value, label="id"):
+ if not isinstance(value, str) or not value.strip() or len(value) > 256:
+ raise ValueError(f"{label} must be a nonempty string of at most 256 characters")
+ return value
+
+
+def _polygon(value, label):
+ if not isinstance(value, Sequence) or isinstance(value, (str, bytes)) or len(value) < 3:
+ raise ValueError(f"{label} needs at least three XY points")
+ points = [vector(p, 2, label) for p in value]
+ area = abs(sum(a[0] * b[1] - b[0] * a[1]
+ for a, b in zip(points, points[1:] + points[:1]))) / 2
+ if area <= 0:
+ raise ValueError(f"{label} must have positive area")
+ # Exact shape validity is checked once at the input boundary, not repaired by buffer(0).
+ from shapely.geometry import Polygon
+ if not Polygon(points).is_valid:
+ raise ValueError(f"{label} is self-intersecting or otherwise invalid")
+
+
+ROOM_FIELDS = {"frame", "floor_polygon_xy_m", "floor_z_m", "height_m", "boundary_quality",
+ "boundary_known", "floor_known", "fixed_objects", "room_type", "openings", "description"}
+# exchangeable_group is supervision bookkeeping: it lives in validity (target order), never in the condition.
+OBJECT_FIELDS = {"id", "category", "description", "support_parent", "support_surface_id",
+ "size_bounds_local_m", "fixed_size_local_m", "required_capabilities",
+ "retrieval_tolerance", "retrieval_tolerance_log", "attributes",
+ "constraint_role", "semantic_front_required"}
+
+
+def migrate_legacy_row(row):
+ """Move pre-C1 ``condition.objects[i].exchangeable_group`` into ``validity.exchangeable_group``
+ and give pre-K1 rows an all-false ``validity.size_axis_swap_allowed``.
+
+ The returned row never renders groups into the condition. Labels follow
+ target order (like validity.position); rows without targets keep request order.
+ """
+ objects = row.get("condition", {}).get("objects", [])
+ if not isinstance(objects, list):
+ return row
+ order = row.get("target", {}).get("objects") or objects
+ if any(isinstance(o, Mapping) and "exchangeable_group" in o for o in objects):
+ validity = row.get("validity", {})
+ if "exchangeable_group" in validity:
+ raise ValueError("exchangeable_group declared in both condition objects and validity")
+ groups = {o.get("id"): o.get("exchangeable_group") for o in objects}
+ condition = {**row["condition"], "objects": [{k: v for k, v in o.items() if k != "exchangeable_group"} for o in objects]}
+ row = {**row, "condition": condition,
+ "validity": {**validity, "exchangeable_group": [groups.get(o.get("id")) for o in order]}}
+ validity = row.get("validity")
+ if isinstance(validity, Mapping) and "size_axis_swap_allowed" not in validity:
+ row = {**row, "validity": {**validity, "size_axis_swap_allowed": [False] * len(order)}}
+ return row
+
+
+def _metadata(value, label, depth=0):
+ """Allow JSON metadata, with explicit nulls and finite numeric values."""
+ if depth > 32:
+ raise ValueError(f"{label} metadata exceeds nesting limit")
+ if value is None or isinstance(value, (str, bool)):
+ return
+ if isinstance(value, (int, float)):
+ finite(value, label)
+ elif isinstance(value, Mapping):
+ if any(not isinstance(k, str) for k in value):
+ raise ValueError(f"{label} metadata keys must be strings")
+ for child in value.values():
+ _metadata(child, label, depth + 1)
+ elif isinstance(value, (list, tuple)):
+ for child in value:
+ _metadata(child, label, depth + 1)
+ else:
+ raise ValueError(f"{label} must contain JSON-compatible metadata")
+
+
+def _capabilities(value, label, unknown_allowed=False):
+ if unknown_allowed and value is None:
+ return
+ if not isinstance(value, (list, tuple)) or any(not isinstance(v, str) or not v.strip() for v in value):
+ raise ValueError(f"{label} must contain nonempty capability names")
+
+
+def _surfaces(value):
+ if value is None:
+ return
+ if not isinstance(value, (list, tuple)):
+ raise ValueError("verified support surfaces must be a list or null")
+ ids = []
+ for surface in value:
+ _keys(surface, {"surface_id", "local_polygon_xy_m", "local_z_m"},
+ {"surface_id", "local_polygon_xy_m", "local_z_m"}, "support surface")
+ ids.append(_id(surface["surface_id"], "surface id"))
+ _polygon(surface["local_polygon_xy_m"], "support surface polygon")
+ finite(surface["local_z_m"], "support height")
+ if len(ids) != len(set(ids)):
+ raise ValueError("support surface IDs must be unique per asset")
+
+
+def _support_graph(objects):
+ parents = {obj["id"]: obj.get("support_parent") for obj in objects}
+ for obj in objects:
+ seen, current = set(), obj["id"]
+ while current in parents:
+ if current in seen:
+ raise ValueError("support graph contains a cycle")
+ seen = seen | {current}
+ current = parents[current]
+
+
+def _object(obj):
+ _keys(obj, OBJECT_FIELDS, {"id", "category", "description"}, "request object")
+ _id(obj["id"])
+ _id(obj["category"], "category")
+ if not isinstance(obj["description"], str):
+ raise ValueError("description must be a string")
+ for key in ("support_parent", "support_surface_id"):
+ if key in obj and obj[key] is not None:
+ _id(obj[key], key)
+ if "constraint_role" in obj:
+ _id(obj["constraint_role"], "constraint role")
+ if "semantic_front_required" in obj and not isinstance(obj["semantic_front_required"], bool):
+ raise ValueError("semantic_front_required must be a boolean")
+ if "attributes" in obj:
+ if not isinstance(obj["attributes"], Mapping):
+ raise ValueError("attributes must be an object")
+ _metadata(obj["attributes"], "attributes")
+ if "fixed_size_local_m" in obj:
+ fixed = obj["fixed_size_local_m"]
+ if not isinstance(fixed, (list, tuple)) or len(fixed) != 3:
+ raise ValueError("fixed size must contain three numbers or nulls")
+ for value in fixed:
+ if value is not None:
+ finite(value, "fixed size", positive=True)
+ if "size_bounds_local_m" in obj:
+ bounds = obj["size_bounds_local_m"]
+ _keys(bounds, {"min", "max"}, {"min", "max"}, "size bounds")
+ lo, hi = vector(bounds["min"], positive=True), vector(bounds["max"], positive=True)
+ if any(a > b for a, b in zip(lo, hi)):
+ raise ValueError("size bounds min exceeds max")
+ if "fixed_size_local_m" in obj:
+ if any(v is not None and not a <= v <= b for v, a, b in zip(obj["fixed_size_local_m"], lo, hi)):
+ raise ValueError("fixed size conflicts with size bounds")
+ if "required_capabilities" in obj:
+ _capabilities(obj["required_capabilities"], "required_capabilities")
+ for key in ("retrieval_tolerance", "retrieval_tolerance_log"):
+ if key in obj:
+ value = obj[key]
+ values = vector(value) if isinstance(value, (list, tuple)) else [finite(value)]
+ if any(v < 0 for v in values):
+ raise ValueError("retrieval tolerance must be nonnegative")
+
+
+def _constraints(constraints, refs):
+ if not isinstance(constraints, list):
+ raise ValueError("constraints must be a list")
+ for c in constraints:
+ if not isinstance(c, Mapping) or not isinstance(c.get("type"), str):
+ raise ValueError("constraint needs a type")
+ kind = _id(c["type"], "constraint type")
+ _metadata(c, "constraint")
+ if "hard" in c and not isinstance(c["hard"], bool):
+ raise ValueError("constraint hard flag must be a boolean")
+ if kind in {"faces_direction", "faces", "near", "clearance", "on", "between", "against_wall"}:
+ if c.get("object_id") not in refs:
+ raise ValueError("known constraint requires an object reference")
+ if kind in {"faces", "near", "clearance"}:
+ if c.get("target_id") not in refs or c["target_id"] == c["object_id"]:
+ raise ValueError("binary constraint requires a distinct object target")
+ if kind == "between":
+ targets = c.get("target_ids")
+ if (not isinstance(targets, list) or len(targets) != 2
+ or any(not isinstance(target, str) for target in targets) or len(set(targets)) != 2 or any(
+ target not in refs or target == c["object_id"] for target in targets)):
+ raise ValueError("between requires two distinct valid target objects")
+ if kind == "on":
+ target = c.get("target_id", c.get("parent_id"))
+ if target not in refs | {"floor", "wall"} or target == c["object_id"]:
+ raise ValueError("on constraint requires a distinct support parent")
+ if "target_id" in c and "parent_id" in c and c["target_id"] != c["parent_id"]:
+ raise ValueError("on constraint has conflicting parent references")
+ for kind_name, field in (("near", "max_distance_m"), ("clearance", "min_distance_m")):
+ if kind == kind_name and field not in c:
+ raise ValueError(f"{kind} constraint requires {field}")
+ for key in ("object_id", "target_id", "parent_id"):
+ if key in c and c[key] not in refs | {"floor", "wall"}:
+ raise ValueError(f"constraint references missing {key}")
+ if c["type"] == "faces_direction":
+ direction = vector(c.get("direction_xy"), 2)
+ if math.hypot(*direction) == 0:
+ raise ValueError("facing direction must be nonzero")
+ if c["type"] == "keepout":
+ _polygon(c.get("polygon_xy_m"), "keepout polygon")
+ for key in ("tolerance_rad", "tolerance_m", "max_distance_m", "min_distance_m"):
+ if key in c and finite(c[key], key) < 0:
+ raise ValueError(f"{key} must be nonnegative")
+
+
+def validate_condition(condition):
+ _keys(condition, {"schema_version", "room", "objects", "constraints"},
+ {"schema_version", "room", "objects", "constraints"}, "condition")
+ if condition["schema_version"] != SCHEMA_VERSION:
+ raise ValueError("unsupported schema version")
+ room = condition["room"]
+ _keys(room, ROOM_FIELDS, {"frame", "floor_polygon_xy_m"}, "room")
+ if room["frame"] != "right_handed_z_up":
+ raise ValueError("room frame must be right_handed_z_up")
+ _polygon(room["floor_polygon_xy_m"], "floor polygon")
+ for key in ("floor_z_m", "height_m"):
+ if room.get(key) is not None:
+ finite(room[key], key, positive=key == "height_m")
+ for key in ("floor_known", "boundary_known"):
+ if key in room and not isinstance(room[key], bool):
+ raise ValueError(f"{key} must be a boolean")
+ if room.get("floor_known") is True and room.get("floor_z_m") is None:
+ raise ValueError("known floor requires a floor height")
+ fixed = room.get("fixed_objects", [])
+ if not isinstance(fixed, list):
+ raise ValueError("fixed objects must be a list")
+ fixed_ids = []
+ for obj in fixed:
+ _keys(obj, {"id", "size_local_m", "bottom_center_m", "yaw_rad", "category", "support_surfaces",
+ "capabilities", "required_capabilities", "semantic_front_local"},
+ {"id", "size_local_m", "bottom_center_m", "yaw_rad"}, "fixed object")
+ fixed_ids.append(_id(obj["id"]))
+ vector(obj["size_local_m"], positive=True)
+ vector(obj["bottom_center_m"])
+ finite(obj["yaw_rad"], "fixed yaw")
+ if "category" in obj:
+ _id(obj["category"], "fixed category")
+ for key in ("capabilities", "required_capabilities"):
+ if key in obj:
+ _capabilities(obj[key], key, unknown_allowed=key == "capabilities")
+ if "support_surfaces" in obj:
+ _surfaces(obj["support_surfaces"])
+ if obj.get("semantic_front_local") is not None:
+ front = vector(obj["semantic_front_local"])
+ if math.hypot(*front[:2]) == 0 or abs(front[2]) > 1e-6:
+ raise ValueError("semantic front must be a nonzero horizontal vector")
+ if not isinstance(condition["objects"], list):
+ raise ValueError("objects must be a list")
+ for obj in condition["objects"]:
+ _object(obj)
+ ids = [obj["id"] for obj in condition["objects"]] + fixed_ids
+ if len(set(ids)) != len(ids) or any(i in {"floor", "wall"} for i in ids):
+ raise ValueError("object IDs must be unique and not reserved")
+ for obj in condition["objects"]:
+ parent = obj.get("support_parent")
+ if parent is not None and (parent not in set(ids) | {"floor", "wall"} or parent == obj["id"]):
+ raise ValueError("invalid support parent")
+ _support_graph(condition["objects"])
+ for key in ("room_type", "description", "boundary_quality"):
+ if room.get(key) is not None and not isinstance(room[key], str):
+ raise ValueError(f"room {key} must be a string or null")
+ if "openings" in room:
+ if not isinstance(room["openings"], list):
+ raise ValueError("room openings must be a list")
+ _metadata(room["openings"], "room openings")
+ _constraints(condition["constraints"], set(ids))
+ from fastfill.v2.validation import effective_support_requests
+ effective_support_requests(condition)
+ return condition
+
+
+def validate_layout(layout, condition=None):
+ _keys(layout, {"schema_version", "objects"}, {"schema_version", "objects"}, "layout")
+ if layout["schema_version"] != SCHEMA_VERSION or not isinstance(layout["objects"], list):
+ raise ValueError("invalid layout schema")
+ ids = []
+ for obj in layout["objects"]:
+ _keys(obj, {"id", "target_size_local_m", "bottom_center_m", "yaw_rad", "support_parent"},
+ {"id", "target_size_local_m", "bottom_center_m", "yaw_rad"}, "output object")
+ if "support_parent" in obj:
+ _id(obj["support_parent"], "output support_parent")
+ ids.append(_id(obj["id"]))
+ vector(obj["target_size_local_m"], positive=True)
+ vector(obj["bottom_center_m"])
+ yaw = finite(obj["yaw_rad"], "yaw")
+ if not -math.pi <= yaw < math.pi:
+ raise ValueError("yaw must be wrapped to [-pi, pi)")
+ if len(ids) != len(set(ids)):
+ raise ValueError("duplicate output IDs")
+ if condition is not None and set(ids) != {o["id"] for o in condition["objects"]}:
+ raise ValueError("output IDs must equal all requested IDs exactly once")
+ # optional predicted support: floor, wall, another output object or a fixed object; never itself, no cycle
+ parents = {obj["id"]: obj["support_parent"] for obj in layout["objects"] if "support_parent" in obj}
+ known = {"floor", "wall", *ids, *(f["id"] for f in (condition or {}).get("room", {}).get("fixed_objects", ()))}
+ for child, parent in parents.items():
+ if parent == child or (condition is not None and parent not in known):
+ raise ValueError(f"output support_parent of {child!r} must name the floor, a wall, another object or a fixed object")
+ seen = {child}
+ while parent in parents:
+ if parent in seen:
+ raise ValueError("output support_parent references form a cycle")
+ seen.add(parent)
+ parent = parents[parent]
+ return layout
+
+
+def normalize_room(room, reference_height=3.0):
+ """Origin/scales depend only on room input; missing height uses fixed 3m."""
+ points = room["floor_polygon_xy_m"]
+ xmin, ymin = [min(p[q] for p in points) for q in range(2)]
+ xmax, ymax = [max(p[q] for p in points) for q in range(2)]
+ height = room.get("height_m") or finite(reference_height, positive=True)
+ if xmax <= xmin or ymax <= ymin:
+ raise ValueError("room XY normalization needs positive extents")
+ return [float(xmin), float(ymin), float(room.get("floor_z_m") or 0.)], [float(xmax - xmin), float(ymax - ymin), float(height)]
diff --git a/code/fastfill/v2/serve.py b/code/fastfill/v2/serve.py
new file mode 100644
index 0000000000000000000000000000000000000000..d129d68cef281be45913480db6d986c83e862e08
--- /dev/null
+++ b/code/fastfill/v2/serve.py
@@ -0,0 +1,87 @@
+"""Offline JSON runner for the v2 runtime; no network service or real Host writes.
+
+The demo catalog ranks by category, typed capabilities and target-size distance.
+Nuanced natural-language retrieval needs an external semantic Resolver adapter.
+"""
+import argparse
+import json
+from pathlib import Path
+import sys
+
+from .runtime import Asset, AtomicMemoryHost, BoundedTranslationRepair, CatalogResolver, RuntimeBudget, SupportSurface, run_pipeline
+
+
+def asset_from_dict(value):
+ allowed = {"ref", "category", "description", "actual_size_local_m", "canonical_transform",
+ "semantic_front_local", "capabilities", "support_surfaces", "provenance"}
+ if not isinstance(value, dict) or set(value) - allowed:
+ raise ValueError("invalid catalog asset fields")
+ required = {"ref", "category", "actual_size_local_m"}
+ if not required.issubset(value):
+ raise ValueError("asset needs ref, category and actual_size_local_m")
+ surfaces = tuple(SupportSurface(**surface) for surface in value.get("support_surfaces", ()))
+ return Asset(**{**value, "support_surfaces": surfaces})
+
+
+def load_catalog(path):
+ value = _read(path)
+ if isinstance(value, dict):
+ if set(value) != {"assets"}:
+ raise ValueError("catalog object must contain only assets")
+ value = value["assets"]
+ if not isinstance(value, list):
+ raise ValueError("catalog must be an asset array or {assets: array}")
+ return CatalogResolver(tuple(asset_from_dict(asset) for asset in value))
+
+
+def _read(path):
+ def reject_constant(value):
+ raise ValueError(f"nonfinite JSON constant {value}")
+ with Path(path).open() as handle:
+ return json.load(handle, parse_constant=reject_constant)
+
+
+def main(argv=None):
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--condition", required=True, type=Path)
+ parser.add_argument("--prediction", required=True, type=Path)
+ parser.add_argument("--catalog", required=True, type=Path)
+ parser.add_argument("--output", type=Path, help="New repository/output JSON path; defaults to stdout")
+ parser.add_argument("--max-asset-retries", type=int, default=2)
+ parser.add_argument("--max-repair-calls", type=int, default=0)
+ parser.add_argument("--repair-step-m", type=float, default=.25)
+ parser.add_argument("--max-seconds", type=float, default=10.)
+ parser.add_argument("--required-levels", nargs="+", choices=("bbox", "mesh", "physics", "solver"), default=["bbox"])
+ parser.add_argument("--commit-in-memory", action="store_true", help="Exercise only the offline atomic Host contract")
+ parser.add_argument("--idempotency-key", default="offline-request")
+ args = parser.parse_args(argv)
+ try:
+ if args.output is not None:
+ forbidden = Path("/Volumes/harddisk/3D_Room_Collections").resolve()
+ if args.output.resolve().is_relative_to(forbidden):
+ raise ValueError("source datasets are read-only; choose a new output path")
+ if args.output.exists():
+ raise ValueError("output already exists; choose a new path")
+ host = AtomicMemoryHost() if args.commit_in_memory else None
+ report = run_pipeline(_read(args.condition), _read(args.prediction), load_catalog(args.catalog),
+ host=host, budget=RuntimeBudget(args.max_asset_retries, args.max_repair_calls, args.max_seconds),
+ repair=BoundedTranslationRepair(args.repair_step_m) if args.max_repair_calls else None,
+ required_levels=tuple(args.required_levels), idempotency_key=args.idempotency_key,
+ expected_world_version=0 if host else None)
+ if host:
+ report = {**report, "offline_host_snapshot": host.snapshot()}
+ output = json.dumps(report, indent=2, allow_nan=False) + "\n"
+ if args.output:
+ args.output.parent.mkdir(parents=True, exist_ok=True)
+ with args.output.open("x") as handle:
+ handle.write(output)
+ else:
+ sys.stdout.write(output)
+ return 0 if report["ok"] else 2
+ except (ValueError, TypeError, KeyError, OSError) as exc:
+ sys.stderr.write(json.dumps({"error": "invalid_runtime_request", "message": str(exc)}) + "\n")
+ return 2
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/code/fastfill/v2/size_range.py b/code/fastfill/v2/size_range.py
new file mode 100644
index 0000000000000000000000000000000000000000..4d8b2fdf339195fac40f08dda06b04f67d4a5252
--- /dev/null
+++ b/code/fastfill/v2/size_range.py
@@ -0,0 +1,38 @@
+"""Configuration-specific target eligibility; never clamp or rewrite labels."""
+from __future__ import annotations
+
+import math
+
+import torch
+
+from .objective import supervision_masks
+
+
+def size_target_conflicts(batch, model_config, loss_config):
+ """Report learned, supervised axes outside reference * exp(+-limit).
+
+ Complete-field masks follow the criterion. Fixed request dimensions bypass
+ the learned size head. Missing labels are selected out before log arithmetic;
+ disabled size/box objectives impose no size-target range requirement.
+ A 1e-6 log tolerance only handles float32 rounding at the exact endpoints.
+ """
+ masks = supervision_masks(batch)
+ slots = torch.zeros_like(batch["slot_mask"])
+ if loss_config.size > 0:
+ slots = slots | masks["size"]
+ if loss_config.box > 0:
+ slots = slots | masks["box"]
+ fixed = batch.get("fixed_size_mask", torch.zeros_like(batch["validity"]["size"]))
+ coordinates = slots.unsqueeze(-1) & ~fixed
+ conflicts = []
+ for b, i, q in coordinates.nonzero().tolist():
+ target = float(batch["targets"]["size"][b, i, q])
+ reference = model_config.size_reference[q]
+ log_ratio = math.log(target) - math.log(reference)
+ limit = model_config.size_log_limit
+ if abs(log_ratio) > limit + 1e-6:
+ conflicts = conflicts + [{"batch_index": b, "object_id": batch["objects"][b][i]["id"],
+ "dimension": ("w", "d", "h")[q], "target_m": target,
+ "minimum_m": reference * math.exp(-limit),
+ "maximum_m": reference * math.exp(limit)}]
+ return conflicts
diff --git a/code/fastfill/v2/support.py b/code/fastfill/v2/support.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4cff36dbb358d051aad74a568083f580889de12
--- /dev/null
+++ b/code/fastfill/v2/support.py
@@ -0,0 +1,38 @@
+"""Support pointer decoding: the ``support_head``'s logits (floor | wall | request slots) -> ``support_parent`` ids.
+
+A request that declares its support (``support_parent``, or a hard ``on`` constraint merged in by
+``validation.effective_support_requests``) keeps it. The others take their most probable parent that keeps the
+support graph acyclic, the most confident slots first; floor and wall never close a cycle, so every slot gets one.
+"""
+from __future__ import annotations
+
+import torch
+
+from fastfill.v2.batch import SUPPORT_CLASSES
+
+
+def _closes_cycle(parents: dict, child: str, parent: str) -> bool:
+ seen = set()
+ while parent is not None and parent not in seen:
+ if parent == child:
+ return True
+ seen.add(parent)
+ parent = parents.get(parent)
+ return False
+
+
+def decode_support_parents(logits: torch.Tensor, requests: list[dict]) -> list[str]:
+ """``logits`` (n, 2 + n') of one scene's ``n`` requests (``requests``: effective support requests, in slot
+ order) -> one parent per request: "floor", "wall" or another request's id."""
+ ids = [r["id"] for r in requests]
+ names = list(SUPPORT_CLASSES) + ids
+ probability = logits[:, :len(names)].float().softmax(-1)
+ parents = {r["id"]: r["support_parent"] for r in requests if r.get("support_parent") is not None}
+ order = sorted((i for i, r in enumerate(requests) if r.get("support_parent") is None),
+ key=lambda i: -float(probability[i].max()))
+ for i in order:
+ for k in probability[i].argsort(descending=True).tolist():
+ if names[k] != ids[i] and not _closes_cycle(parents, ids[i], names[k]):
+ parents[ids[i]] = names[k]
+ break
+ return [parents[i] for i in ids]
diff --git a/code/fastfill/v2/tests/fixtures/validation_rows_fix20261006.jsonl b/code/fastfill/v2/tests/fixtures/validation_rows_fix20261006.jsonl
new file mode 100644
index 0000000000000000000000000000000000000000..ba9a297df46074ba1b3d2b88a42de870fcf9fa4d
--- /dev/null
+++ b/code/fastfill/v2/tests/fixtures/validation_rows_fix20261006.jsonl
@@ -0,0 +1,3 @@
+{"schema_version": "fastfill.v2", "condition": {"schema_version": "fastfill.v2", "room": {"frame": "right_handed_z_up", "floor_polygon_xy_m": [[2.434470000000001, 1.45157], [0.0, 1.45157], [0.0, 0.0], [2.434470000000001, 0.0]], "boundary_known": true, "boundary_quality": "polygon", "floor_known": true, "floor_z_m": 0.0, "height_m": 3.3, "room_type": "closet"}, "objects": [{"id": "obj_0000", "category": "clothes rack", "description": "clothes rack"}, {"id": "obj_0001", "category": "clothes rack", "description": "clothes rack"}, {"id": "obj_0002", "category": "clothes rack", "description": "clothes rack"}, {"id": "obj_0003", "category": "clothes hanger", "description": "clothes hanger"}], "constraints": []}, "target": {"schema_version": "fastfill.v2", "objects": [{"id": "obj_0000", "target_size_local_m": [0.316352, 1.188116, 1.999938], "bottom_center_m": [2.2761429999999976, 0.8575110000000001, 0.0], "yaw_rad": -3.1415925936651883}, {"id": "obj_0001", "target_size_local_m": [0.316352, 1.188116, 1.999938], "bottom_center_m": [0.158338999999998, 0.8575079999999999, 0.0], "yaw_rad": -5.992460572201708e-08}, {"id": "obj_0002", "target_size_local_m": [0.316352, 1.188118, 1.999938], "bottom_center_m": [1.4771420000000006, 0.158327, 0.0], "yaw_rad": 1.570796406794896}, {"id": "obj_0003", "target_size_local_m": [0.316702, 0.61225, 1.999938], "bottom_center_m": [0.6226389999999995, 0.158497, 0.0], "yaw_rad": 1.570796406794896}]}, "validity": {"position": [[true, true, true], [true, true, true], [true, true, true], [true, true, true]], "size": [[true, true, true], [true, true, true], [true, true, true], [true, true, true]], "yaw": [false, false, false, false], "exchangeable_group": ["anonymous_0", "anonymous_0", "anonymous_0", null], "yaw_symmetry_order": [2, 2, 2, 2]}, "provenance": {"source": "HSSD200", "scene_id": "HSSD200:102344328:7", "legacy_uid": "HSSD200:102344328:7", "house_id": "hssd:102344328", "group": "hssd:102344328", "split": "validation", "legacy_flags": {}, "target_source_ids": ["102344328:165", "102344328:166", "102344328:167", "102344328:163"], "field_evidence": [{"tilted": false, "front_policy": "strict", "size_semantics": "canonical_source_IR", "source_support_inferred": false, "legacy_support_inferred": false, "raw_anchor": null, "legacy_z_snap_applied_to_target": false, "recorded_source_evidence": {}}, {"tilted": false, "front_policy": "strict", "size_semantics": "canonical_source_IR", "source_support_inferred": false, "legacy_support_inferred": false, "raw_anchor": null, "legacy_z_snap_applied_to_target": false, "recorded_source_evidence": {}}, {"tilted": false, "front_policy": "strict", "size_semantics": "canonical_source_IR", "source_support_inferred": false, "legacy_support_inferred": false, "raw_anchor": null, "legacy_z_snap_applied_to_target": false, "recorded_source_evidence": {}}, {"tilted": false, "front_policy": "strict", "size_semantics": "canonical_source_IR", "source_support_inferred": false, "legacy_support_inferred": false, "raw_anchor": null, "legacy_z_snap_applied_to_target": false, "recorded_source_evidence": {}}], "source_meta": {"n_tilted": 0, "n_incomplete": 0, "room_id": "102344328:7", "region_name": "closet", "floor_height": 0.0, "n_architectural_dropped": 0, "n_below_floor_dropped": 0, "n_articulated_twin_dropped": 0, "n_opening_no_mesh_bounds": 0, "front_known": true, "v2_evidence": {"status": "applied_no_matching_records", "scope": "recorded_source_evidence_only", "loaded_files": ["designer/opening-target-join.json", "internscenes/k0_saved_object_exposure.jsonl"], "missing_files": [], "evidence_root": "/Users/slian/Desktop/3D/Worldedge/OptiScene/.release/audits/2026-09-28/source-check", "opening_width_corrections": 0, "front_unknown_objects": 0}}, "vertical_reframe_m": 0.0, "legacy_height_dropped": false, "omitted_legacy_constraints": [], "constraint_source": "frozen_sparse_legacy_request", "request_order": "SHA256(seed,uid,source_id)", "descriptions": "category_only_no_asset_or_pose_text", "review_parent_scene_id": "HSSD200:102344328:7", "review_policy": "source-derived-facing-evidence-v1", "review_source_qualification": "Frozen legacy constraints are source-derived annotations, not newly synthesized user requirements; unverified source-derived facing is omitted. Explicit/nonlegacy requirements and existing exchangeability groups are preserved.", "review_omitted_constraints": [], "review_geometry_diagnostics": {"known_floor": true, "floor_z_m": 0.0, "tolerance_m": 0.0001, "below_known_floor": [], "minimum_bottom_minus_floor_m": null, "labels_modified": false}}}
+{"schema_version": "fastfill.v2", "condition": {"schema_version": "fastfill.v2", "room": {"frame": "right_handed_z_up", "floor_polygon_xy_m": [[0.8513099999999998, 1.2740299999999998], [0.0, 1.2740299999999998], [0.0, 0.0], [0.8513099999999998, 0.0]], "boundary_known": true, "boundary_quality": "polygon", "floor_known": true, "floor_z_m": 0.0, "height_m": 2.8, "room_type": "laundryroom/mudroom"}, "objects": [{"id": "obj_0000", "category": "washer", "description": "washer"}, {"id": "obj_0001", "category": "sink cabinet", "description": "sink cabinet"}, {"id": "obj_0002", "category": "washer", "description": "washer"}], "constraints": []}, "target": {"schema_version": "fastfill.v2", "objects": [{"id": "obj_0000", "target_size_local_m": [0.547176, 0.600502, 0.84998], "bottom_center_m": [0.26358999999999977, 0.9737799999999996, 0.8600000000000001], "yaw_rad": -5.992460572201708e-08}, {"id": "obj_0001", "target_size_local_m": [0.53257, 0.65529, 0.9], "bottom_center_m": [0.26627899999999993, 0.34587900000000005, 0.0], "yaw_rad": -5.992460572201708e-08}, {"id": "obj_0002", "target_size_local_m": [0.547176, 0.600502, 0.84998], "bottom_center_m": [0.27358999999999956, 0.9737799999999996, 0.0], "yaw_rad": -5.992460572201708e-08}]}, "validity": {"position": [[true, true, true], [true, true, true], [true, true, true]], "size": [[true, true, true], [true, true, true], [true, true, true]], "yaw": [false, false, false], "exchangeable_group": ["anonymous_0", null, "anonymous_0"], "yaw_symmetry_order": [2, 2, 2]}, "provenance": {"source": "HSSD200", "scene_id": "HSSD200:102816852:4", "legacy_uid": "HSSD200:102816852:4", "house_id": "hssd:102816852", "group": "hssd:102816852", "split": "validation", "legacy_flags": {}, "target_source_ids": ["102816852:6", "102816852:88", "102816852:5"], "field_evidence": [{"tilted": false, "front_policy": "strict", "size_semantics": "canonical_source_IR", "source_support_inferred": false, "legacy_support_inferred": true, "raw_anchor": null, "legacy_z_snap_applied_to_target": false, "recorded_source_evidence": {}}, {"tilted": false, "front_policy": "strict", "size_semantics": "canonical_source_IR", "source_support_inferred": false, "legacy_support_inferred": false, "raw_anchor": null, "legacy_z_snap_applied_to_target": false, "recorded_source_evidence": {}}, {"tilted": false, "front_policy": "strict", "size_semantics": "canonical_source_IR", "source_support_inferred": false, "legacy_support_inferred": false, "raw_anchor": null, "legacy_z_snap_applied_to_target": false, "recorded_source_evidence": {}}], "source_meta": {"n_tilted": 0, "n_incomplete": 0, "room_id": "102816852:4", "region_name": "laundryroom", "floor_height": 0.0, "n_architectural_dropped": 0, "n_below_floor_dropped": 0, "n_articulated_twin_dropped": 2, "n_opening_no_mesh_bounds": 0, "front_known": true, "v2_evidence": {"status": "applied_no_matching_records", "scope": "recorded_source_evidence_only", "loaded_files": ["designer/opening-target-join.json", "internscenes/k0_saved_object_exposure.jsonl"], "missing_files": [], "evidence_root": "/Users/slian/Desktop/3D/Worldedge/OptiScene/.release/audits/2026-09-28/source-check", "opening_width_corrections": 0, "front_unknown_objects": 0}}, "vertical_reframe_m": 0.0, "legacy_height_dropped": false, "omitted_legacy_constraints": [], "constraint_source": "frozen_sparse_legacy_request", "request_order": "SHA256(seed,uid,source_id)", "descriptions": "category_only_no_asset_or_pose_text", "review_parent_scene_id": "HSSD200:102816852:4", "review_policy": "source-derived-facing-evidence-v1", "review_source_qualification": "Frozen legacy constraints are source-derived annotations, not newly synthesized user requirements; unverified source-derived facing is omitted. Explicit/nonlegacy requirements and existing exchangeability groups are preserved.", "review_omitted_constraints": [], "review_geometry_diagnostics": {"known_floor": true, "floor_z_m": 0.0, "tolerance_m": 0.0001, "below_known_floor": [], "minimum_bottom_minus_floor_m": null, "labels_modified": false}}}
+{"schema_version": "fastfill.v2", "condition": {"schema_version": "fastfill.v2", "room": {"frame": "right_handed_z_up", "floor_polygon_xy_m": [[8.27745, 1.30248], [7.3867329999999995, 6.353988], [0.0, 5.051508], [0.890717, 0.0]], "boundary_known": false, "boundary_quality": "hull", "floor_known": false, "floor_z_m": null, "height_m": 2.165477037429808, "room_type": "office"}, "objects": [{"id": "obj_0000", "category": "pencil sharpener", "description": "pencil sharpener"}, {"id": "obj_0001", "category": "paper cutter", "description": "paper cutter"}, {"id": "obj_0002", "category": "printer", "description": "printer"}, {"id": "obj_0003", "category": "stapler", "description": "stapler"}, {"id": "obj_0004", "category": "stool", "description": "stool"}, {"id": "obj_0005", "category": "table", "description": "table"}, {"id": "obj_0006", "category": "cabinet", "description": "cabinet"}, {"id": "obj_0007", "category": "cabinet", "description": "cabinet"}, {"id": "obj_0008", "category": "shredder", "description": "shredder"}, {"id": "obj_0009", "category": "printer", "description": "printer"}, {"id": "obj_0010", "category": "sealing machine", "description": "sealing machine"}], "constraints": []}, "target": {"schema_version": "fastfill.v2", "objects": [{"id": "obj_0000", "target_size_local_m": [0.22515030500076383, 0.11744581636994929, 0.09557998180389404], "bottom_center_m": [3.460318802574322, 1.4224110169514785, 0.4151170253753653], "yaw_rad": 1.7934704383169873}, {"id": "obj_0001", "target_size_local_m": [0.6174891910492967, 0.544981965428644, 0.21339800953865046], "bottom_center_m": [4.142971675551725, 1.5647833800611777, 0.4153980016708365], "yaw_rad": 1.7782675829829397}, {"id": "obj_0002", "target_size_local_m": [0.7725702184687147, 1.190895468932238, 1.1509240418672562], "bottom_center_m": [6.374112676657621, 2.8295527399746905, -0.5026500225067148], "yaw_rad": -2.9215825792198284}, {"id": "obj_0003", "target_size_local_m": [0.3811419372667676, 0.36511252450630644, 0.06409102678298943], "bottom_center_m": [3.7195057046474522, 1.3422594552831353, 0.40135499835014254], "yaw_rad": 1.8587501834867286}, {"id": "obj_0004", "target_size_local_m": [0.3422284146641652, 0.3146645110894726, 0.24305200576782227], "bottom_center_m": [2.769591716681627, 2.970423689140374, -0.4892369508743295], "yaw_rad": -1.4212962120266965}, {"id": "obj_0005", "target_size_local_m": [0.7976945324530131, 0.7691675696053999, 0.7669870257377625], "bottom_center_m": [5.645776177021336, 2.0061237354527175, -0.5119709968566903], "yaw_rad": 1.8139434179098437}, {"id": "obj_0006", "target_size_local_m": [1.0121993820730912, 2.8419536102247105, 0.9642030596733092], "bottom_center_m": [4.221795662983227, 3.6283326585974933, -0.5132980346679694], "yaw_rad": -1.3632482579416454}, {"id": "obj_0007", "target_size_local_m": [0.651299504043213, 2.2437281855733717, 0.9670749604701997], "bottom_center_m": [4.3164905588687645, 1.6172285899713346, -0.5163259506225597], "yaw_rad": 1.7860636011476059}, {"id": "obj_0008", "target_size_local_m": [0.570002626499535, 0.3094490414224724, 0.598803997039795], "bottom_center_m": [5.332034806154268, 2.361828732810158, -0.5092999935150158], "yaw_rad": 1.8832341699674497}, {"id": "obj_0009", "target_size_local_m": [0.6859226849537953, 0.5099539676378908, 0.5366659983992577], "bottom_center_m": [5.693316911977103, 1.9335597329945475, 0.2130820155143729], "yaw_rad": 2.002827478996048}, {"id": "obj_0010", "target_size_local_m": [0.21868895177798298, 0.5816855765585504, 0.121288001537323], "bottom_center_m": [3.3330662444674486, 1.3839172692545285, 0.41613900661468417], "yaw_rad": 0.20953639052479467}]}, "validity": {"position": [[true, true, true], [true, true, true], [true, true, true], [true, true, true], [true, true, true], [true, true, true], [true, true, true], [true, true, true], [true, true, true], [true, true, true], [true, true, true]], "size": [[true, true, true], [true, true, true], [true, true, true], [true, true, true], [true, true, true], [true, true, true], [true, true, true], [true, true, true], [true, true, true], [true, true, true], [true, true, true]], "yaw": [true, true, true, true, true, true, true, true, true, true, true], "exchangeable_group": [null, null, "anonymous_2", null, null, null, "anonymous_6", "anonymous_6", null, "anonymous_2", null], "yaw_symmetry_order": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}, "provenance": {"source": "MultiScan", "scene_id": "MultiScan::scene_00021_00", "legacy_uid": "MultiScan::scene_00021_00", "house_id": "multiscan:scene_00021", "group": "multiscan:scene_00021", "split": "validation", "legacy_flags": {}, "target_source_ids": ["o8", "o6", "o19", "o7", "o22", "o13", "o15", "o1", "o14", "o18", "o9"], "field_evidence": [{"tilted": false, "front_policy": "strict", "size_semantics": "canonical_source_IR", "source_support_inferred": false, "legacy_support_inferred": true, "raw_anchor": null, "legacy_z_snap_applied_to_target": false, "recorded_source_evidence": {}}, {"tilted": false, "front_policy": "strict", "size_semantics": "canonical_source_IR", "source_support_inferred": false, "legacy_support_inferred": true, "raw_anchor": null, "legacy_z_snap_applied_to_target": false, "recorded_source_evidence": {}}, {"tilted": false, "front_policy": "strict", "size_semantics": "canonical_source_IR", "source_support_inferred": false, "legacy_support_inferred": false, "raw_anchor": null, "legacy_z_snap_applied_to_target": false, "recorded_source_evidence": {}}, {"tilted": false, "front_policy": "strict", "size_semantics": "canonical_source_IR", "source_support_inferred": false, "legacy_support_inferred": true, "raw_anchor": null, "legacy_z_snap_applied_to_target": false, "recorded_source_evidence": {}}, {"tilted": false, "front_policy": "strict", "size_semantics": "canonical_source_IR", "source_support_inferred": false, "legacy_support_inferred": false, "raw_anchor": null, "legacy_z_snap_applied_to_target": false, "recorded_source_evidence": {}}, {"tilted": false, "front_policy": "strict", "size_semantics": "canonical_source_IR", "source_support_inferred": false, "legacy_support_inferred": false, "raw_anchor": null, "legacy_z_snap_applied_to_target": false, "recorded_source_evidence": {}}, {"tilted": false, "front_policy": "strict", "size_semantics": "canonical_source_IR", "source_support_inferred": false, "legacy_support_inferred": false, "raw_anchor": null, "legacy_z_snap_applied_to_target": false, "recorded_source_evidence": {}}, {"tilted": false, "front_policy": "strict", "size_semantics": "canonical_source_IR", "source_support_inferred": false, "legacy_support_inferred": false, "raw_anchor": null, "legacy_z_snap_applied_to_target": false, "recorded_source_evidence": {}}, {"tilted": false, "front_policy": "strict", "size_semantics": "canonical_source_IR", "source_support_inferred": false, "legacy_support_inferred": false, "raw_anchor": null, "legacy_z_snap_applied_to_target": false, "recorded_source_evidence": {}}, {"tilted": false, "front_policy": "strict", "size_semantics": "canonical_source_IR", "source_support_inferred": false, "legacy_support_inferred": true, "raw_anchor": null, "legacy_z_snap_applied_to_target": false, "recorded_source_evidence": {}}, {"tilted": false, "front_policy": "strict", "size_semantics": "canonical_source_IR", "source_support_inferred": false, "legacy_support_inferred": true, "raw_anchor": null, "legacy_z_snap_applied_to_target": false, "recorded_source_evidence": {}}], "source_meta": {"n_tilted": 0, "n_incomplete": 0, "room_id": "scene_00021_00", "scan_id": "scene_00021_00", "scene_id": "scene_00021", "device": "iPad8,10", "floor_z": -1.3646475076675417, "floor_z_source": "median bottom of rests_on_floor objects", "floor_height": -0.8540120124816886, "poly_source": "floor", "boundary_source": "convex hull of floor slabs + wall segments", "n_floor_objects_outside_structure": 0, "height_reliable": true, "front_known": true, "front_source": "annotated front vector", "tilt_tol_deg": 5.0, "n_front_vertical": 0, "v2_evidence": {"status": "applied_no_matching_records", "scope": "recorded_source_evidence_only", "loaded_files": ["designer/opening-target-join.json", "internscenes/k0_saved_object_exposure.jsonl"], "missing_files": [], "evidence_root": "/Users/slian/Desktop/3D/Worldedge/OptiScene/.release/audits/2026-09-28/source-check", "opening_width_corrections": 0, "front_unknown_objects": 0}}, "vertical_reframe_m": -0.5106354951858532, "legacy_height_dropped": false, "omitted_legacy_constraints": [], "constraint_source": "frozen_sparse_legacy_request", "request_order": "SHA256(seed,uid,source_id)", "descriptions": "category_only_no_asset_or_pose_text", "review_parent_scene_id": "MultiScan::scene_00021_00", "review_policy": "source-derived-facing-evidence-v1", "review_source_qualification": "Frozen legacy constraints are source-derived annotations, not newly synthesized user requirements; unverified source-derived facing is omitted. Explicit/nonlegacy requirements and existing exchangeability groups are preserved.", "review_omitted_constraints": [], "review_geometry_diagnostics": {"known_floor": false, "floor_z_m": null, "tolerance_m": 0.0001, "below_known_floor": [], "minimum_bottom_minus_floor_m": null, "labels_modified": false}}}
diff --git a/code/fastfill/v2/tests/test_accumulation_tail.py b/code/fastfill/v2/tests/test_accumulation_tail.py
new file mode 100644
index 0000000000000000000000000000000000000000..989f574df6897f25870d007ed2ddb788d09d10b4
--- /dev/null
+++ b/code/fastfill/v2/tests/test_accumulation_tail.py
@@ -0,0 +1,206 @@
+"""Actual Accelerator training: every flushed window averages its microbatches."""
+from pathlib import Path
+from datetime import timedelta
+import json
+import os
+import sys
+
+import pytest
+import torch
+
+from fastfill.v2 import train
+from fastfill.v2.tests.test_active_objective_training import _data, _row, _yaw_config
+
+
+class _ScalarModel(torch.nn.Module):
+ def __init__(self):
+ super().__init__()
+ self.yaw_logits_head = torch.nn.Parameter(torch.zeros(()))
+
+ def forward(self, **kwargs):
+ return self.yaw_logits_head
+
+ def save_pretrained(self, directory):
+ directory = Path(directory)
+ directory.mkdir(parents=True)
+ torch.save(self.state_dict(), directory / "scalar.pt")
+
+
+class _ScalarCriterion:
+ def __init__(self, coefficients):
+ self.coefficients = coefficients
+
+ def __call__(self, prediction, batch):
+ values = [self.coefficients[int(row["scene_id"])] for row in batch["provenance"]]
+ coefficient = sum(values) / len(values)
+ loss = prediction * coefficient
+ zero = prediction * 0.
+ active = sum(value != 0. for value in values)
+ return {"loss": loss, "yaw_cls": loss, "counts": {"yaw": active},
+ "active_objective_count_local": active,
+ "term_sums": {**dict.fromkeys(train.TERMS, zero.detach()), "yaw_cls": loss.detach()},
+ "term_counts": {**dict.fromkeys(train.TERMS, 0), "yaw_cls": active},
+ **{key: zero for key in ("position", "size", "yaw_reg", "box", "collision", "boundary")}}
+
+
+def _patch_scalar_training(patches, coefficients, observed, *, scaled=False):
+ # Keep actual preflight, Accelerator.prepare/backward, accumulation, clipping,
+ # logging and optimizer dispatch. Replace only model/objective with a known
+ # constant derivative and use lr=0 to observe gradients without changing it.
+ original_loader = train.DataLoader
+ patches.setattr(train, "DataLoader", lambda rows, **kw:
+ original_loader(rows, **{**kw, "shuffle": False}))
+ patches.setattr(train, "build_model", lambda config: _ScalarModel())
+ patches.setattr(train, "GeometryCriterion", lambda config: _ScalarCriterion(coefficients))
+
+ class RecordingSGD(torch.optim.SGD):
+ def step(self, closure=None):
+ observed.append(float(self.param_groups[0]["params"][0].grad.detach()))
+ return super().step(closure)
+
+ patches.setattr(train.torch.optim, "AdamW", lambda params, **kw: RecordingSGD(list(params), lr=0.))
+ if scaled:
+ import accelerate
+ original_accelerator = accelerate.Accelerator
+
+ class CpuScaledAccelerator(original_accelerator):
+ def __init__(self, **kwargs):
+ super().__init__(**kwargs)
+ # Real torch AMP scaler on CPU exercises scaled backward,
+ # Accelerate unscale/clip and GradScaler optimizer dispatch.
+ self.scaler = torch.amp.GradScaler("cpu", init_scale=16.)
+
+ def unscale_gradients(self, optimizer=None):
+ # Accelerate enables fp16 unscale on accelerator devices only;
+ # adapt that device gate to the real CPU GradScaler test.
+ optimizers = self._optimizers if optimizer is None else [optimizer]
+ for prepared in optimizers:
+ self.scaler.unscale_(prepared.optimizer)
+
+ patches.setattr(accelerate, "Accelerator", CpuScaledAccelerator)
+
+
+def _run_scalar(tmp_path, monkeypatch, coefficients, *, accumulation=2, clip=100., scaled=False, steps=None):
+ observed = []
+ _patch_scalar_training(monkeypatch, coefficients, observed, scaled=scaled)
+ config = _yaw_config()
+ updates = sum(any(coefficients[i:i + accumulation])
+ for i in range(0, len(coefficients), accumulation))
+ config["training"] = {**config["training"], "gradient_accumulation_steps": accumulation,
+ "steps": updates if steps is None else steps, "clip_grad_norm": clip}
+ rows = [_row(str(index)) for index in range(len(coefficients))]
+ logs = train.run_training(config, _data(tmp_path, rows), tmp_path / "run")
+ manifest = json.loads((tmp_path / "run/run_manifest.json").read_text())
+ return observed, logs, manifest
+
+
+@pytest.mark.parametrize("count,accumulation,expected", [
+ (3, 2, [1., 1.]), (4, 2, [1., 1.]), (3, 1, [1., 1., 1.]),
+ (1, 3, [1.]), (5, 3, [1., 1.]),
+])
+def test_actual_run_training_averages_complete_and_tail_windows(tmp_path, monkeypatch, count, accumulation, expected):
+ observed, logs, manifest = _run_scalar(tmp_path, monkeypatch, [1.] * count, accumulation=accumulation)
+ assert observed == pytest.approx(expected)
+ assert len(logs) == manifest["steps_completed"] == len(expected)
+
+
+def test_actual_tail_counts_empty_label_microbatches_in_declared_mean(tmp_path, monkeypatch):
+ observed, logs, _ = _run_scalar(tmp_path, monkeypatch, [2., 0., 2., 2., 0.], accumulation=3)
+ # Both windows count every microbatch, including those with no labels.
+ assert observed == pytest.approx([4. / 3., 1.])
+ assert [record["active_objective_count"] for record in logs] == [2, 1]
+
+
+def test_actual_tail_retains_prior_supervision_and_counts_empty_last_batch(tmp_path, monkeypatch):
+ observed, logs, _ = _run_scalar(tmp_path, monkeypatch, [1., 1., 1., 2., 0.], accumulation=3)
+ assert observed == pytest.approx([1., 1.])
+ assert [record["active_objective_count"] for record in logs] == [3, 1]
+
+
+def test_actual_empty_complete_window_does_not_reweight_later_tail(tmp_path, monkeypatch):
+ observed, logs, manifest = _run_scalar(tmp_path, monkeypatch, [0., 0., 1.])
+ assert observed == pytest.approx([1.])
+ assert logs[0]["active_objective_count"] == 1
+ assert manifest["skipped_no_objective_windows"] == 1
+
+
+@pytest.mark.parametrize("scaled", [False, True])
+def test_actual_tail_rescale_precedes_clipping_and_amp_unscale(tmp_path, monkeypatch, scaled):
+ observed, _, _ = _run_scalar(tmp_path, monkeypatch, [.8, .8, .8], clip=.75, scaled=scaled)
+ assert observed == pytest.approx([.75, .75], rel=2e-6)
+
+
+def test_amp_overflow_does_not_count_as_completed_optimizer_step(tmp_path, monkeypatch):
+ # The first finite scalar loss has an overflowing *scaled* derivative, so
+ # GradScaler skips its optimizer update. The following window is finite and
+ # must become completed step 1; an attempted overflow is not a training step.
+ observed, logs, manifest = _run_scalar(
+ tmp_path, monkeypatch, [1e38, 1.], accumulation=1, scaled=True, steps=1)
+ assert observed == pytest.approx([1.])
+ assert [record["step"] for record in logs] == [1]
+ assert manifest["steps_completed"] == 1
+ assert manifest["skipped_gradient_overflow_windows"] == 1
+
+
+def test_actual_partial_window_is_flushed_each_epoch(tmp_path, monkeypatch):
+ observed, logs, _ = _run_scalar(tmp_path, monkeypatch, [1., 1., 3.], steps=4)
+ # Each epoch ends with its own one-microbatch mean. No tail is combined
+ # with the next epoch or used to reweight the previous complete window.
+ assert observed == pytest.approx([1., 3., 1., 3.])
+ assert [record["step"] for record in logs] == [1, 2, 3, 4]
+
+
+def _distributed_tail_worker(rank, rendezvous, directory, mode):
+ from torch import distributed as dist
+ os.environ.update(RANK=str(rank), WORLD_SIZE="2", LOCAL_RANK=str(rank), LOCAL_WORLD_SIZE="2",
+ OMP_NUM_THREADS="1", MASTER_ADDR="127.0.0.1", MASTER_PORT="29514")
+ dist.init_process_group("gloo", init_method=rendezvous, rank=rank, world_size=2,
+ timeout=timedelta(seconds=20))
+ patches = pytest.MonkeyPatch()
+ try:
+ if sys.platform == "darwin":
+ patches.setattr(torch._C, "_get_accelerator", lambda: torch.device("cpu"))
+ root = Path(directory)
+ coefficients = {"all_valid": [1.] * 6, "peer_empty": [0., 2.] * 3,
+ "tail_one_rank_empty": [1., 1., 1., 1., 2., 0.],
+ "empty_tail": [1., 1., 1., 1., 0., 0.]}[mode]
+ observed = []
+ _patch_scalar_training(patches, coefficients, observed)
+ if rank == 0:
+ _data(root, [_row(str(index)) for index in range(6)])
+ dist.barrier()
+ config = _yaw_config()
+ config["training"] = {**config["training"], "gradient_accumulation_steps": 2,
+ "steps": 2, "clip_grad_norm": 100.}
+ logs = train.run_training(config, root / "train.jsonl", root / "run")
+ # Real prepared loader has three microbatches per rank: one complete
+ # K=2 window and one single-batch tail. DDP averages rank derivatives.
+ assert observed == pytest.approx([1., 1.])
+ assert [record["accumulation_microbatches"] for record in logs] == (
+ [2, 2] if mode == "empty_tail" else [2, 1])
+ expected_counts = {"all_valid": [4, 2], "peer_empty": [2, 1],
+ "tail_one_rank_empty": [4, 1], "empty_tail": [4, 4]}[mode]
+ assert [record["active_objective_count"] for record in logs] == expected_counts
+ if rank == 0:
+ manifest = json.loads((root / "run/run_manifest.json").read_text())
+ assert manifest["world_size"] == 2
+ assert manifest["skipped_no_objective_windows"] == int(mode == "empty_tail")
+ (root / f"rank-{rank}.json").write_text(json.dumps({
+ "mode": mode, "optimizer_gradients": observed,
+ "microbatches": [record["accumulation_microbatches"] for record in logs],
+ "global_objective_counts": expected_counts}, indent=2) + "\n")
+ finally:
+ patches.undo()
+ if dist.is_initialized():
+ dist.destroy_process_group()
+
+
+@pytest.mark.skipif(not torch.distributed.is_available() or not torch.distributed.is_gloo_available(),
+ reason="requires Gloo")
+@pytest.mark.parametrize("mode", ["all_valid", "peer_empty", "tail_one_rank_empty", "empty_tail"])
+def test_actual_two_rank_complete_and_tail_means(tmp_path, monkeypatch, mode):
+ monkeypatch.setenv("GLOO_SOCKET_IFNAME", "lo0" if sys.platform == "darwin" else "lo")
+ monkeypatch.setenv("CUDA_VISIBLE_DEVICES", "") # CPU Gloo ranks: with one visible GPU, rank 1 would ask for cuda:1
+ torch.multiprocessing.spawn(_distributed_tail_worker,
+ args=("file://" + str(tmp_path / "rendezvous"), str(tmp_path), mode), nprocs=2, join=True)
+ assert all((tmp_path / f"rank-{rank}.json").exists() for rank in range(2))
diff --git a/code/fastfill/v2/tests/test_active_objective_distributed.py b/code/fastfill/v2/tests/test_active_objective_distributed.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ff48923e88986974683ccf97bfcfc848dad7572
--- /dev/null
+++ b/code/fastfill/v2/tests/test_active_objective_distributed.py
@@ -0,0 +1,103 @@
+"""Real Accelerator/Gloo entry-point tests with empty ranks and windows."""
+from copy import deepcopy
+from datetime import timedelta
+import json
+import os
+from pathlib import Path
+import sys
+
+import pytest
+import torch
+
+from fastfill.v2.tests.test_active_objective_training import _row, _yaw_config
+
+
+def _worker(rank, rendezvous, directory, mode):
+ from torch import distributed as dist
+ from fastfill.v2 import train
+ os.environ.update(RANK=str(rank), WORLD_SIZE="2", LOCAL_RANK=str(rank), LOCAL_WORLD_SIZE="2",
+ OMP_NUM_THREADS="1", MASTER_ADDR="127.0.0.1", MASTER_PORT="29513")
+ dist.init_process_group("gloo", init_method=rendezvous, rank=rank, world_size=2,
+ timeout=timedelta(seconds=20))
+ patches = pytest.MonkeyPatch()
+ try:
+ if sys.platform == "darwin":
+ # torch 2.13 barrier(device_ids=[...]) chooses the available MPS
+ # accelerator even for a CPU/Gloo group. This CPU test explicitly
+ # selects CPU discovery; collectives/backward remain real Gloo.
+ patches.setattr(torch._C, "_get_accelerator", lambda: torch.device("cpu"))
+ root = Path(directory)
+ rows_count = {"peer_empty": 2, "last_empty": 4, "empty_then_valid": 8, "all_empty": 4}[mode]
+ data = root / "train.jsonl"
+ if rank == 0:
+ data.write_text("".join(json.dumps(_row(str(i))) + "\n" for i in range(rows_count)))
+ dist.barrier()
+ original_collate, original_preflight, original_loader = train.collate_samples, train._preflight, train.DataLoader
+ state = {"after_preflight": False}
+ def collate(rows, *args, **kwargs):
+ batch = original_collate(rows, *args, **kwargs)
+ if state["after_preflight"]:
+ yaw = batch["validity"]["yaw"].clone()
+ for i, row in enumerate(rows):
+ identity = int(row["provenance"]["scene_id"])
+ empty = (mode == "all_empty" or (mode == "peer_empty" and rank == 0)
+ or (mode == "last_empty" and identity >= 2)
+ or (mode == "empty_then_valid" and identity < 4))
+ if empty:
+ yaw[i] = False
+ batch = {**batch, "validity": {**batch["validity"], "yaw": yaw}}
+ return batch
+ def preflight(*args, **kwargs):
+ state["after_preflight"] = False
+ result = original_preflight(*args, **kwargs)
+ state["after_preflight"] = True
+ return result
+ def loader(rows, **kwargs):
+ return original_loader(rows, **{**kwargs, "shuffle": False})
+ models, before = [], []
+ original_build = train.build_model
+ def build(config):
+ model = original_build(config)
+ models.append(model)
+ before.append(deepcopy(model.state_dict()))
+ return model
+ patches.setattr(train, "collate_samples", collate)
+ patches.setattr(train, "_preflight", preflight)
+ patches.setattr(train, "DataLoader", loader)
+ patches.setattr(train, "build_model", build)
+ config = _yaw_config()
+ config["training"]["gradient_accumulation_steps"] = 1 if mode == "peer_empty" else 2
+ if mode == "all_empty":
+ with pytest.raises(RuntimeError, match="no active objective"):
+ train.run_training(config, data, root / "run")
+ changed = sum(not torch.equal(before[0][name], value) for name, value in models[0].state_dict().items())
+ assert changed == 0
+ observation = {"optimizer_updates": 0, "changed_parameter_tensors": changed}
+ else:
+ logs = train.run_training(config, data, root / "run")
+ assert len(logs) == 1 and logs[0]["step"] == 1
+ assert logs[0]["active_objective_count"] == {"peer_empty": 1, "last_empty": 2, "empty_then_valid": 4}[mode]
+ assert logs[0]["gradient_norms"]["yaw_logits_head"] > 0
+ changed = sum(not torch.equal(before[0][name], value) for name, value in models[0].state_dict().items())
+ assert changed > 0
+ observation = {"optimizer_updates": 1, "active_objective_count": logs[0]["active_objective_count"],
+ "changed_parameter_tensors": changed}
+ if rank == 0:
+ manifest = json.loads((root / "run/run_manifest.json").read_text())
+ assert manifest["world_size"] == 2
+ assert manifest["skipped_no_objective_windows"] == int(mode == "empty_then_valid")
+ (root / f"rank-{rank}.json").write_text(json.dumps(observation, indent=2) + "\n")
+ finally:
+ patches.undo()
+ if dist.is_initialized():
+ dist.destroy_process_group()
+
+
+@pytest.mark.skipif(not torch.distributed.is_available() or not torch.distributed.is_gloo_available(), reason="requires Gloo")
+@pytest.mark.parametrize("mode", ["peer_empty", "last_empty", "empty_then_valid", "all_empty"])
+def test_actual_two_rank_objective_windows(tmp_path, monkeypatch, mode):
+ monkeypatch.setenv("GLOO_SOCKET_IFNAME", "lo0" if sys.platform == "darwin" else "lo")
+ monkeypatch.setenv("CUDA_VISIBLE_DEVICES", "") # CPU Gloo ranks: with one visible GPU, rank 1 would ask for cuda:1
+ torch.multiprocessing.spawn(_worker, args=("file://" + str(tmp_path / "rendezvous"), str(tmp_path), mode),
+ nprocs=2, join=True)
+ assert all((tmp_path / f"rank-{rank}.json").exists() for rank in range(2))
diff --git a/code/fastfill/v2/tests/test_active_objective_training.py b/code/fastfill/v2/tests/test_active_objective_training.py
new file mode 100644
index 0000000000000000000000000000000000000000..4dab2ab4801489587a6530581832cbe33b49c116
--- /dev/null
+++ b/code/fastfill/v2/tests/test_active_objective_training.py
@@ -0,0 +1,201 @@
+"""Actual objective eligibility and optimizer-window regressions."""
+from copy import deepcopy
+import json
+
+import pytest
+import torch
+
+from fastfill.v2 import train
+from fastfill.v2.batch import TinyTokenizer
+from fastfill.v2.losses import LossConfig
+from fastfill.v2.model import ModelConfig
+from fastfill.v2.tests.test_execution import sample
+
+
+def _yaw_config():
+ return {"model": {"backbone": "tiny", "decoder_dim": 16, "decoder_heads": 2,
+ "decoder_layers": 1, "tiny_hidden_size": 16, "lora_rank": 0},
+ "loss": {"position": 0., "size": 0., "yaw_cls": 1., "yaw_reg": 1., "hungarian": False},
+ "training": {"cpu": True, "steps": 1, "batch_size": 1, "learning_rate": .01,
+ "max_length": 4096, "checkpoint_every": 0, "validate_every": 0, "cpu_threads": 1},
+ "augmentation": NO_AUGMENTATION}
+
+
+NO_AUGMENTATION = {"rotate90": False, "mirror": False, "shuffle_objects": False,
+ "drop_constraints_p": 0., "drop_support_p": 0., "category_only_description_p": 0., "minimal_form_p": 0.,
+ "given_pose_p": 0., "given_pose_corrupt_p": 0., "given_pose_noise_m": 0., "given_pose_yaw_noise_deg": 0.,
+ "given_pose_size_noise": 0.}
+
+
+def _row(identity, yaw=True):
+ row = sample()
+ return {**row, "validity": {**row["validity"], "yaw": [yaw]},
+ "provenance": {**row["provenance"], "scene_id": identity}}
+
+
+def _data(tmp_path, rows):
+ path = tmp_path / "train.jsonl"
+ path.write_text("".join(json.dumps(row) + "\n" for row in rows))
+ return path
+
+
+def test_actual_yaw_only_no_labels_fails_before_model_or_run_publication(tmp_path, monkeypatch):
+ data = _data(tmp_path, [_row("empty", False)])
+ builds = []
+ monkeypatch.setattr(train, "build_model", lambda cfg: builds.append(cfg))
+ with pytest.raises(ValueError, match="supervised|objective"):
+ train.run_training(_yaw_config(), data, tmp_path / "run")
+ assert not builds and not (tmp_path / "run").exists()
+
+
+def test_actual_mixed_density_preflight_records_objective_exclusion(tmp_path):
+ data = _data(tmp_path, [_row("empty", False), _row("valid")])
+ train.run_training(_yaw_config(), data, tmp_path / "run")
+ manifest = json.loads((tmp_path / "run/run_manifest.json").read_text())
+ assert manifest["supervised_samples"] == 1
+ assert manifest["rejected"][0]["reason"] == "no_active_objective"
+ assert manifest["rejected"][0]["provenance"]["scene_id"] == "empty"
+
+
+def test_default_four_terms_still_accept_position_size_without_yaw(tmp_path):
+ cfg = {**_yaw_config(), "loss": {"hungarian": False}}
+ train.run_training(cfg, _data(tmp_path, [_row("without-yaw", False)]), tmp_path / "run")
+ manifest = json.loads((tmp_path / "run/run_manifest.json").read_text())
+ assert manifest["supervised_samples"] == 1 and manifest["steps_completed"] == 1
+ log = json.loads((tmp_path / "run/training_log.json").read_text())[0]
+ assert log["active_objective_count"] == 2
+
+
+@pytest.mark.parametrize("kind", ["fixed_size", "partial_position", "box_without_yaw", "collision_singleton"])
+def test_preflight_uses_enabled_terms_and_complete_learning_coordinates(kind):
+ row = _row(kind, False)
+ weights = {key: 0. for key in ("position", "size", "yaw_cls", "yaw_reg")}
+ if kind == "fixed_size":
+ weights["size"] = 1.
+ row["condition"]["objects"][0]["fixed_size_local_m"] = [1., 1., 1.]
+ elif kind == "partial_position":
+ weights["position"] = 1.
+ row["validity"]["position"] = [[True, True, False]]
+ elif kind == "box_without_yaw":
+ weights["box"] = 1.
+ else:
+ weights["collision"] = 1.
+ with pytest.raises(ValueError, match="supervised|objective"):
+ train._preflight([row], TinyTokenizer(), ModelConfig(backbone="tiny", lora_rank=0),
+ {"max_length": 4096}, LossConfig(**weights))
+
+
+def _dynamic_batches(monkeypatch, invalid_ids):
+ """Inject missing labels after preflight, exercising the defensive loop guard."""
+ original_collate, original_preflight, original_loader = train.collate_samples, train._preflight, train.DataLoader
+ state = {"after_preflight": False}
+ def collate(rows, *args, **kwargs):
+ batch = original_collate(rows, *args, **kwargs)
+ if state["after_preflight"]:
+ batch = {**batch, "validity": {**batch["validity"], "yaw": batch["validity"]["yaw"].clone()}}
+ for index, row in enumerate(rows):
+ if row["provenance"]["scene_id"] in invalid_ids:
+ batch["validity"]["yaw"][index] = False
+ return batch
+ def preflight(*args, **kwargs):
+ state["after_preflight"] = False
+ result = original_preflight(*args, **kwargs)
+ state["after_preflight"] = True
+ return result
+ def loader(rows, **kwargs):
+ return original_loader(rows, **{**kwargs, "shuffle": False})
+ monkeypatch.setattr(train, "collate_samples", collate)
+ monkeypatch.setattr(train, "_preflight", preflight)
+ monkeypatch.setattr(train, "DataLoader", loader)
+
+
+def test_accumulation_preserves_prior_valid_gradient_when_last_microbatch_empty(tmp_path, monkeypatch):
+ _dynamic_batches(monkeypatch, {"last-empty"})
+ cfg = _yaw_config()
+ cfg["training"]["gradient_accumulation_steps"] = 2
+ logs = train.run_training(cfg, _data(tmp_path, [_row("first-valid"), _row("last-empty")]), tmp_path / "run")
+ assert logs[0]["active_objective_count"] == 1
+ assert logs[0]["gradient_norms"]["yaw_logits_head"] > 0
+ assert logs[0]["step"] == 1
+
+
+def test_globally_empty_window_does_not_decay_or_increment_update(tmp_path, monkeypatch):
+ _dynamic_batches(monkeypatch, {"empty-a", "empty-b"})
+ cfg = _yaw_config()
+ cfg["training"]["gradient_accumulation_steps"] = 2
+ models, before = [], []
+ original_build = train.build_model
+ def build(config):
+ model = original_build(config)
+ models.append(model)
+ before.append(deepcopy(model.state_dict()))
+ return model
+ monkeypatch.setattr(train, "build_model", build)
+ data = _data(tmp_path, [_row("empty-a"), _row("empty-b")])
+ with pytest.raises(RuntimeError, match="no active objective|no supervised optimizer"):
+ train.run_training(cfg, data, tmp_path / "run")
+ assert all(torch.equal(before[0][name], value) for name, value in models[0].state_dict().items())
+ assert not (tmp_path / "run/model").exists()
+
+
+def test_empty_window_is_skipped_before_later_valid_window(tmp_path, monkeypatch):
+ _dynamic_batches(monkeypatch, {"empty-a", "empty-b"})
+ cfg = _yaw_config()
+ cfg["training"]["gradient_accumulation_steps"] = 2
+ rows = [_row(name) for name in ("empty-a", "empty-b", "valid-a", "valid-b")]
+ logs = train.run_training(cfg, _data(tmp_path, rows), tmp_path / "run")
+ assert len(logs) == 1 and logs[0]["step"] == 1
+ assert logs[0]["active_objective_count"] == 2
+ manifest = json.loads((tmp_path / "run/run_manifest.json").read_text())
+ assert manifest["skipped_no_objective_windows"] == 1
+
+
+def test_zero_numeric_loss_with_eligible_supervision_still_updates(tmp_path, monkeypatch):
+ # With K=1, CE is exactly zero. Zero residual weights predict the real GT
+ # residual exactly, so the unmodified criterion returns zero numeric loss.
+ original_build = train.build_model
+ models, before = [], []
+ def build(config):
+ model = original_build(config)
+ with torch.no_grad():
+ model.yaw_residual_head.weight.zero_()
+ model.yaw_residual_head.bias.zero_()
+ models.append(model)
+ before.append(deepcopy(model.state_dict()))
+ return model
+ monkeypatch.setattr(train, "build_model", build)
+ config = _yaw_config()
+ config["model"]["yaw_bins"] = 1
+ logs = train.run_training(config, _data(tmp_path, [_row("valid")]), tmp_path / "run")
+ assert logs[0]["loss"] == 0 and logs[0]["active_objective_count"] == 1
+ assert logs[0]["step"] == 1
+ assert any(not torch.equal(before[0][name], value) for name, value in models[0].state_dict().items())
+
+
+def test_short_epoch_flushes_eligible_partial_accumulation_window(tmp_path):
+ config = _yaw_config()
+ config["training"]["gradient_accumulation_steps"] = 3
+ logs = train.run_training(config, _data(tmp_path, [_row("valid")]), tmp_path / "run")
+ assert len(logs) == 1 and logs[0]["step"] == 1
+ assert logs[0]["active_objective_count"] == 1
+ assert logs[0]["gradient_norms"]["yaw_logits_head"] > 0
+
+
+@pytest.mark.parametrize("term", ["boundary", "collision"])
+def test_regularizer_only_preflight_keeps_label_free_eligible_geometry(term):
+ row = _row("label-free", False)
+ row["validity"] = {"position": [[False] * 3], "size": [[False] * 3], "yaw": [False]}
+ if term == "collision":
+ row["condition"]["objects"].append({"id": "b", "category": "chair", "description": "a chair"})
+ config = LossConfig(position=0., size=0., yaw_cls=0., yaw_reg=0., **{term: 1.})
+ kept, rejected = train._preflight([row], TinyTokenizer(), ModelConfig(backbone="tiny", lora_rank=0),
+ {"max_length": 4096}, config)
+ assert kept == [row] and not rejected
+
+
+def test_collision_only_support_exempt_pair_has_no_eligible_term():
+ row = _row("supported", False)
+ row["condition"]["objects"].append({"id": "b", "category": "cup", "description": "a cup", "support_parent": "a"})
+ config = LossConfig(position=0., size=0., yaw_cls=0., yaw_reg=0., collision=1.)
+ with pytest.raises(ValueError, match="supervised|objective"):
+ train._preflight([row], TinyTokenizer(), ModelConfig(backbone="tiny", lora_rank=0), {"max_length": 4096}, config)
diff --git a/code/fastfill/v2/tests/test_asset_attribute_contract.py b/code/fastfill/v2/tests/test_asset_attribute_contract.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e9b4a15383a75ffac14cb06fd6cd3559126e81e
--- /dev/null
+++ b/code/fastfill/v2/tests/test_asset_attribute_contract.py
@@ -0,0 +1,97 @@
+"""Unverified typed attributes cannot be satisfied by a reference catalog."""
+from copy import deepcopy
+
+import pytest
+
+from fastfill.v2.runtime import Asset, AtomicMemoryHost, CatalogResolver, run_pipeline
+from fastfill.v2.schema import validate_condition
+from fastfill.v2.tests.test_runtime import condition, desk, layout
+from fastfill.v2.validation import validate_scene
+
+
+def attribute_condition(attributes):
+ return condition([{"id": "desk", "category": "desk", "support_parent": "floor",
+ "attributes": attributes, "description": "a red wooden work desk"}])
+
+
+@pytest.mark.parametrize("attributes", [
+ {"color": "red"}, {"material": "wood"}, {"has_drawers": False},
+ {"finish": {"coating": "matte"}}, {"color": None},
+])
+def test_reference_catalog_requires_evidence_for_every_nonempty_typed_attribute_request(attributes):
+ request = attribute_condition(attributes)
+ validate_condition(request)
+ asset = desk(description="a red wooden work desk", capabilities=("work_surface",))
+ before = deepcopy((request, layout()))
+ resolver = CatalogResolver((asset,))
+ assert resolver.resolve(request["objects"][0], layout()["objects"][0]) is None
+ assert (request, layout()) == before
+
+
+@pytest.mark.parametrize("claimed_metadata", [{}, {"attributes": {"color": "red", "material": "wood"}},
+ {"verified_attributes": {"color": "red", "material": "wood"}}])
+def test_direct_actual_validator_has_hard_unknown_without_an_attribute_evidence_contract(claimed_metadata):
+ request = attribute_condition({"color": "red", "material": "wood"})
+ actual = {**layout()["objects"][0], "actual_size_local_m": [.9, 1., .7],
+ "capabilities": ["work_surface"], **claimed_metadata}
+ before = deepcopy((request, actual))
+ report = validate_scene(request, [actual], stage="actual")
+ assert not report["ok"]
+ check = next(check for check in report["checks"] if check["code"] == "attributes_unverified")
+ assert check["hard"] is True and check["status"] == "unknown"
+ assert check["object_ids"] == ["desk"]
+ assert "attributes_unverified" in report["unknown_checks"]
+ assert (request, actual) == before
+
+
+def test_external_resolver_cannot_bypass_required_attributes_or_partially_commit():
+ request = condition([
+ {"id": "plain", "category": "desk", "support_parent": "floor"},
+ {"id": "required", "category": "desk", "support_parent": "floor",
+ "attributes": {"color": "red", "material": "wood"}},
+ ])
+ prediction = layout([{**layout()["objects"][0], "id": "plain", "bottom_center_m": [1, 2, 0]},
+ {**layout()["objects"][0], "id": "required", "bottom_center_m": [3, 2, 0]}])
+ before = deepcopy((request, prediction))
+
+ class IgnoringResolver:
+ def resolve(self, request_object, _prediction, *, excluded_refs=()):
+ return Asset(request_object["id"], "desk", (.9, 1., .7), description="blue plastic desk")
+
+ host = AtomicMemoryHost()
+ report = run_pipeline(request, prediction, IgnoringResolver(), host=host,
+ expected_world_version=0, idempotency_key="attribute-request")
+ assert not report["ok"] and not report["committed"]
+ assert report["metrics"]["model"]["schema_success"] is True
+ assert report["diagnostics"][0]["code"] == "asset_unavailable"
+ assert report["diagnostics"][0]["object_id"] == "required"
+ assert host.snapshot() == {"version": 0, "objects": [], "commits": 0}
+ assert report["raw_model_output"] == prediction
+ assert (request, prediction) == before
+
+
+@pytest.mark.parametrize("attributes", [None, {}])
+def test_plain_or_empty_attributes_keep_existing_size_and_capability_contract(attributes):
+ request_object = {"id": "desk", "category": "desk", "support_parent": "floor",
+ "required_capabilities": ["work_surface"],
+ "fixed_size_local_m": [None, 1., None]}
+ if attributes is not None:
+ request_object = {**request_object, "attributes": attributes}
+ request, prediction = condition([request_object]), layout()
+ resolver = CatalogResolver((desk("no_capability"),
+ desk("eligible", (.9, 1., .7), capabilities=("work_surface",))))
+ host = AtomicMemoryHost()
+ report = run_pipeline(request, prediction, resolver, host=host,
+ expected_world_version=0, idempotency_key="plain-request")
+ assert report["ok"] and report["committed"]
+ assert report["final_objects"][0]["asset_ref"] == "eligible"
+ assert report["final_objects"][0]["target_size_local_m"] == [1., 1., .75]
+ assert report["final_objects"][0]["actual_size_local_m"] == [.9, 1., .7]
+ assert "attributes_unverified" not in report["validation"]["final"]["unknown_checks"]
+ assert host.snapshot()["commits"] == 1
+
+
+def test_target_geometry_stage_does_not_claim_asset_attribute_verification():
+ report = validate_scene(attribute_condition({"color": "red"}), layout()["objects"])
+ assert report["ok"]
+ assert report["stage"] == "target" and report["geometry_level"] == "bbox"
diff --git a/code/fastfill/v2/tests/test_audit_c2_uniform_yaw.py b/code/fastfill/v2/tests/test_audit_c2_uniform_yaw.py
new file mode 100644
index 0000000000000000000000000000000000000000..a363df6324ca8b877062d46614cf967d5f10828d
--- /dev/null
+++ b/code/fastfill/v2/tests/test_audit_c2_uniform_yaw.py
@@ -0,0 +1,37 @@
+"""Audit C2: the uniform-yaw baseline is the model's own yaw metric (``reference_metrics``) applied to a uniformly
+random yaw with a perfect size, also for swap-allowed objects (box-equivalent joint minimum), in evaluate and stratify."""
+import math
+
+import numpy as np
+import pytest
+
+from fastfill.v2.evaluate import baseline_metrics, fit_baselines, reference_metrics
+from fastfill.v2.tests.test_evaluate_fix20261006 import row
+
+
+def _sample(size, swap, size_valid, n=1):
+ sample = row([("chair", [2., 2., 0.], size, .3)] * n, orders=[4] * n)
+ sample["validity"].update(size_axis_swap_allowed=[swap] * n, size=[[size_valid] * 3] * n)
+ return sample
+
+
+@pytest.mark.parametrize("size, swap, size_valid", [
+ ([10., 1., 1.], True, True), # the audit's box: before pi / 8 = 0.3927, the model's metric gives 0.7677
+ ([2., 1., 1.], True, True), ([1., 1., 1.], True, True), ([10., 1., 1.], True, False), ([10., 1., 1.], False, True)])
+def test_uniform_yaw_baseline_is_the_model_metric_of_a_uniform_yaw(monkeypatch, size, swap, size_valid):
+ monkeypatch.setenv("OMP_NUM_THREADS", "1") # stratify's import-time setdefault must not leak into later tests
+ from fastfill.v2.ops.stratify import object_baselines
+ # reference_metrics itself, label copies predicted with their own size on a fixed midpoint grid of yaws; the joint
+ # minimum's yaw error jumps where the quarter turn wins, so the grid mean is only O(1 / n) close
+ n = 10000
+ grid = _sample(size, swap, size_valid, n)
+ yaws = .3 + 2 * math.pi * (np.arange(n) + .5) / n
+ layout = {"objects": [{**t, "yaw_rad": float(y)} for t, y in zip(grid["target"]["objects"], yaws)]}
+ expected = reference_metrics(layout, grid, hungarian=False, include_iou=False)["yaw_error_rad"]["mean"]
+ if size == [10., 1., 1.] and swap and size_valid:
+ assert expected == pytest.approx(.7677, abs=4e-4)
+ sample = _sample(size, swap, size_valid)
+ baseline = baseline_metrics(sample, fit_baselines([sample]))["uniform_yaw"]
+ assert baseline["yaw_error_rad"]["mean"] == pytest.approx(expected, abs=4e-4)
+ per_object, summary = object_baselines(sample, fit_baselines([sample]), None) # stratify reproduces it exactly
+ assert per_object["obj_0000"]["uniform_yaw"] == baseline["yaw_error_rad"]["mean"] and summary["uniform_yaw"] == baseline
diff --git a/code/fastfill/v2/tests/test_augment_repair_fix20261006.py b/code/fastfill/v2/tests/test_augment_repair_fix20261006.py
new file mode 100644
index 0000000000000000000000000000000000000000..3dca8db882289d447f35ed7320d0cd6c1595e23f
--- /dev/null
+++ b/code/fastfill/v2/tests/test_augment_repair_fix20261006.py
@@ -0,0 +1,125 @@
+"""Review repairs 2026-10-06 (H): augmentation float noise, regrouping after drops, loader budget fallback."""
+from copy import deepcopy
+
+import pytest
+import torch
+
+from fastfill.v2 import train
+from fastfill.v2.batch import TinyTokenizer, _rigid_xy, augment_sample, collate_samples, tokenize_condition
+from fastfill.v2.matching import certify_group, match_batch
+from fastfill.v2.tests.test_batch_augment_fix20261006 import NO_AUGMENT, sample
+
+
+def chairs(descriptions, support=None, constraints=()):
+ objects = [{"id": f"obj_{i:04d}", "category": "chair", "description": d, **({"support_parent": s} if s else {})}
+ for i, (d, s) in enumerate(zip(descriptions, support or [None] * len(descriptions)))]
+ return {"schema_version": "fastfill.v2",
+ "condition": {"schema_version": "fastfill.v2", "room": {
+ "frame": "right_handed_z_up", "floor_polygon_xy_m": [[0, 0], [6, 0], [6, 4], [0, 4]], "floor_z_m": 0.,
+ "height_m": 3., "floor_known": True, "boundary_known": True}, "objects": objects, "constraints": list(constraints)},
+ "target": {"objects": [{"id": o["id"], "target_size_local_m": [.5, .5, .9], "bottom_center_m": [1. + 4. * i, 1., 0.], "yaw_rad": 0.}
+ for i, o in enumerate(objects)]},
+ "validity": {"position": [[True] * 3] * len(objects), "size": [[True] * 3] * len(objects), "yaw": [True] * len(objects),
+ "yaw_symmetry_order": [2] * len(objects), "exchangeable_group": [None] * len(objects)},
+ "provenance": {"split": "train", "source": "offline_test", "house_id": "h", "scene_id": "s"}}
+
+
+def tokens(condition):
+ return len(tokenize_condition(condition, TinyTokenizer())[0])
+
+
+# --- R1 / R-leak-2: float noise and the context budget ------------------------------
+
+def test_rotation_does_not_grow_rendered_numbers_from_float_noise():
+ s = chairs(["a", "b"])
+ s["condition"]["room"]["floor_polygon_xy_m"] = [[0., 0.], [8.8, 0.], [8.8, 4.4], [0., 4.4]]
+ # Source yaws are full-precision floats (k * pi/2 never lengthens this one); positions are mostly short decimals.
+ s["condition"]["room"]["fixed_objects"] = [{"id": "fixed_0000", "category": "door", "size_local_m": [.1, .9, 2.1],
+ "bottom_center_m": [1.1, 2.2, 0.], "yaw_rad": -0.3141592653589793}]
+ s["target"]["objects"][0]["bottom_center_m"] = [3.3, 0.1 + 0.2, 0.] # 0.30000000000000004 in the source
+ base = tokens(s["condition"])
+ for k in (1, 2, 3):
+ a = deepcopy(s)
+ _rigid_xy(a["condition"], a["target"], a["validity"], k, False)
+ numbers = [v for p in a["condition"]["room"]["floor_polygon_xy_m"] for v in p]
+ numbers += a["condition"]["room"]["fixed_objects"][0]["bottom_center_m"][:2] + a["target"]["objects"][0]["bottom_center_m"][:2]
+ assert all(len(repr(v)) <= 4 for v in numbers), numbers # 8.8 - 1.1 renders as 7.7, not 7.699999999999999
+ assert tokens(a["condition"]) <= base
+ collate_samples([a], TinyTokenizer())
+ # Full-precision source coordinates are kept, not shortened to six decimals.
+ s["target"]["objects"][1]["bottom_center_m"] = [1.697421321709293, 2.5, 0.]
+ a = deepcopy(s)
+ _rigid_xy(a["condition"], a["target"], a["validity"], 2, False)
+ assert a["target"]["objects"][1]["bottom_center_m"][0] == pytest.approx(8.8 - 1.697421321709293, abs=1e-12)
+ assert len(repr(a["target"]["objects"][1]["bottom_center_m"][0])) >= 15
+
+
+def test_training_rows_fall_back_to_the_preflighted_row_when_augmentation_exceeds_the_budget():
+ row = chairs(["red chair", "blue chair"])
+ for obj, target in zip(row["condition"]["objects"], row["target"]["objects"]):
+ obj["id"] = target["id"] = obj["id"][-1] # short IDs: shuffling renames them to the longer obj_%04d
+ budget = tokens(row["condition"])
+ tight = train._Rows([row], {**NO_AUGMENT, "shuffle_objects": True}, 7, TinyTokenizer(), budget)
+ assert tight[0] == row and tight.fallbacks == 1
+ collate_samples([tight[0]], TinyTokenizer(), max_length=budget)
+ roomy = train._Rows([row], {**NO_AUGMENT, "shuffle_objects": True}, 7, TinyTokenizer(), budget + 64)
+ assert roomy[0]["condition"]["objects"][0]["id"] == "obj_0000" and roomy.fallbacks == 0
+ unchecked = train._Rows([row], {**NO_AUGMENT, "shuffle_objects": True}, 7)
+ assert unchecked[0] == roomy[0] # no tokenizer: legacy behaviour, caller owns the budget
+
+
+# --- R2 / R-leak-1: identical requests after drops are regrouped ---------------------
+
+def test_category_only_descriptions_regroup_identical_chairs_and_match_them_by_geometry():
+ s = chairs(["a red armchair", "a blue dining chair"])
+ a = augment_sample(s, {**NO_AUGMENT, "category_only_description_p": 1.}, torch.Generator().manual_seed(0))
+ assert a["validity"]["exchangeable_group"] == ["anonymous_0", "anonymous_0"]
+ b = collate_samples([a], TinyTokenizer())
+ assert b["exchangeable_group"] == [["anonymous_0", "anonymous_0"]]
+ certify_group(b["objects"][0], b["conditions"][0]["constraints"], [0, 1])
+ swapped = {"position_normalized": b["targets"]["position_normalized"].flip(1), "size": torch.ones(1, 2, 3), "slot_mask": b["slot_mask"]}
+ assert match_batch(swapped, b, True)[0].tolist() == [1, 0]
+ # Without the drop the descriptions still differ: fixed identity as before.
+ plain = collate_samples([augment_sample(s, NO_AUGMENT)], TinyTokenizer())
+ assert plain["exchangeable_group"] == [[None, None]] and match_batch({**swapped, "slot_mask": plain["slot_mask"]}, plain, True)[0].tolist() == [0, 1]
+
+
+def test_drop_support_regroups_nightstands_that_differed_only_by_support_parent():
+ s = chairs(["nightstand", "nightstand"], support=(None, "floor"))
+ assert augment_sample(s, NO_AUGMENT)["validity"]["exchangeable_group"] == [None, None]
+ a = augment_sample(s, {**NO_AUGMENT, "drop_support_p": 1.}, torch.Generator().manual_seed(0))
+ assert a["validity"]["exchangeable_group"] == ["anonymous_0", "anonymous_0"]
+ assert all("support_parent" not in o for o in a["condition"]["objects"])
+
+
+def test_regrouping_requires_complete_position_labels_and_follows_target_order_after_shuffle():
+ s = chairs(["a", "b", "c"])
+ s["validity"]["position"][2] = [True, True, False]
+ for seed in range(6):
+ a = augment_sample(s, {**NO_AUGMENT, "shuffle_objects": True, "category_only_description_p": 1.}, torch.Generator().manual_seed(seed))
+ labels = a["validity"]["exchangeable_group"]
+ incomplete = [i for i, row in enumerate(a["validity"]["position"]) if not all(row)]
+ assert len(incomplete) == 1 and labels[incomplete[0]] is None
+ assert [l for i, l in enumerate(labels) if i != incomplete[0]] == ["anonymous_0", "anonymous_0"]
+ b = collate_samples([a], TinyTokenizer())
+ members = [i for i, g in enumerate(b["exchangeable_group"][0]) if g]
+ certify_group(b["objects"][0], b["conditions"][0]["constraints"], members)
+ assert b["validity"]["position"][0, members].all()
+
+
+def test_build_time_group_survives_when_the_wider_candidate_fails_certification():
+ s = chairs(["red", "red", "blue"], constraints=[{"type": "against_wall", "object_id": "obj_0002"}])
+ s["validity"]["exchangeable_group"] = ["anonymous_0", "anonymous_0", None]
+ a = augment_sample(s, {**NO_AUGMENT, "category_only_description_p": 1.}, torch.Generator().manual_seed(0))
+ labels = a["validity"]["exchangeable_group"]
+ assert labels[0] == labels[1] and labels[0] is not None and labels[2] is None # the constrained chair cannot join; the pair stays
+ b = collate_samples([a], TinyTokenizer())
+ certify_group(b["objects"][0], b["conditions"][0]["constraints"], [0, 1])
+ match_batch({"position_normalized": b["targets"]["position_normalized"], "size": torch.ones(1, 3, 3), "slot_mask": b["slot_mask"]}, b, True)
+
+
+def test_rigid_only_augmentation_keeps_build_time_labels_verbatim():
+ s = sample()
+ for seed in range(4):
+ a = augment_sample(s, {**NO_AUGMENT, "rotate90": True}, torch.Generator().manual_seed(seed))
+ assert a["validity"]["exchangeable_group"] == s["validity"]["exchangeable_group"]
diff --git a/code/fastfill/v2/tests/test_autorun.py b/code/fastfill/v2/tests/test_autorun.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf9cec9a27094cf03b0d0aa81e4d73a37ca3ec69
--- /dev/null
+++ b/code/fastfill/v2/tests/test_autorun.py
@@ -0,0 +1,407 @@
+"""Autopilot decisions: selection score, the rescaled next-run configuration and the final acceptance."""
+import json
+from pathlib import Path
+
+import pytest
+
+from fastfill.v2.autorun import next_config, score
+
+
+def _baselines(room_centre=2.5):
+ return {"category_median_size": {"log_size_error": {"mean": .4}}, "uniform_yaw": {"yaw_error_rad": {"mean": .7}},
+ "room_center_position": {"bottom_center_error_m": {"mean": room_centre}}}
+
+
+def _report(size, yaw, pos, central=.17, wall=.66, overlap=.01, out=0.):
+ collapse = lambda c, w, o, r: {"central_quarter_fraction": c, "mean_nearest_wall_distance_m": w,
+ "bev_overlap_rate_iou_gt_0.3": o, "out_of_room_fraction": r}
+ return {"model": {"reference": {"log_size_error": {"mean": size}, "yaw_error_rad": {"mean": yaw},
+ "bottom_center_error_m": {"mean": pos}}},
+ "baselines": _baselines(), "baselines_paired": _baselines(),
+ "collapse": {"predicted_matched": collapse(central, wall, overlap, out), "ground_truth": collapse(.17, .66, .01, 0.)}}
+
+
+def test_score_divides_by_the_baselines_of_the_same_requests():
+ # before: the all-row baselines, whose room-centre error includes requests the model never answered
+ report = {**_report(.3, .6, 2.), "baselines": _baselines(room_centre=4.), "baselines_paired": _baselines(room_centre=2.)}
+ assert score(report) == pytest.approx(score(_report(.3, .6, 2.)) + 1. - .8) # 2 / 2 instead of 2 / 2.5
+ with pytest.raises(KeyError): # a report without the paired baselines is not scorable (evaluated again)
+ score({key: value for key, value in report.items() if key != "baselines_paired"})
+
+
+def test_summary_shows_validator_collisions_clean_rooms_and_their_ground_truth(tmp_path):
+ import argparse
+ from fastfill.v2.autorun import Autopilot
+ pilot = Autopilot(argparse.Namespace(repo=str(tmp_path), model_repo="mock/model"))
+ checked = lambda rooms, pairs, clean, unknown: {"rooms": 10, "rooms_with_collision": rooms, "collision_pairs": pairs,
+ "fixed_collision_pairs": 1, "clean_rooms": clean, "clean_room_rate": clean / 10,
+ "clean_rooms_with_hard_unknown": unknown}
+ report = {**_report(.3, .6, 2.), "requests": 10, "inference_failed_requests": 0, "hard_violation_requests": 6,
+ "failed_requests_strict": 9, "validation": {"model": checked(5, 12, 4, 3), "ground_truth": checked(2, 3, 8, 6)}}
+ report["collapse"]["predicted_matched"]["bev_overlap_rate_iou_gt_0.3_room_mean"] = .05
+ report["baselines_paired"]["room_center_position"]["bottom_center_error_m"]["mean"] = 1.25
+ (tmp_path / "test").mkdir()
+ (tmp_path / "test" / "report.json").write_text(json.dumps(report))
+ pilot.summary({"checkpoint": "c", "score": 1., "report": str(tmp_path / "test" / "report.json")}, tmp_path / "test", None)
+ text = (pilot.dir / "SUMMARY.md").read_text()
+ # before: only the IoU > 0.3 overlap rate (0.010), which hid these collisions
+ assert "| 0.010 / 0.050 | 5/10 rooms, 12 pairs (+1 fixed) | 0.400 (3 with unknown) | 6 / 9 |" in text
+ assert "2/10 rooms, 3 pairs (+1 fixed); clean 0.800 (6 with unknown)" in text # the labels under the same validator
+ assert "| 1.250 / 0.400 / 0.700 |" in text # the paired baselines
+ table = [line for line in text.splitlines() if line.startswith("|")]
+ # before: a 16-cell delimiter row under 15 header cells, which GFM does not render as a table
+ assert len(table) == 4 and {len(line.strip("|").split("|")) for line in table} == {15}
+
+
+def test_a_cached_report_without_paired_baselines_is_evaluated_again(tmp_path, monkeypatch):
+ from fastfill.v2 import autorun
+ pilot, data, runs, calls, report = _candidates(tmp_path, monkeypatch, alive=False)
+ report(pilot.runs / "select-a-model-step-400", tmp_path / "a/model-step-400", projection="minimal", grid_decode="spread",
+ implementation_sha256=autorun.implementation_sha256(tmp_path), baselines_paired=None)
+ pilot.evaluate_candidates(runs[:1], "select", newest=1, data=data)
+ assert len(calls) == len(list(pilot.runs.glob("select-a-model-step-400.stale-*"))) == 1
+
+
+def test_score_prefers_accurate_layouts_and_punishes_collapse_or_leaving_the_room():
+ good, worse = score(_report(.3, .6, 2.0)), score(_report(.35, .6, 2.0))
+ assert good < worse
+ assert score(_report(.3, .6, 2.0, central=.9, wall=2.0)) > good + .7 # everything in the centre
+ assert score(_report(.3, .6, 2.0, overlap=.2)) > good + .85 # stacked
+ assert score(_report(.3, .6, 2.0, out=.5)) > good + 2 # pushed out of the room
+
+
+def test_next_config_keeps_the_model_and_loss_and_rescales_to_seven_or_four_gpus():
+ config = {"model": {"position_head": "grid_residual"}, "loss": {"position_cell": .5},
+ "training": {"steps": 3887, "batch_size": 1, "gradient_accumulation_steps": 32, "resume": "x",
+ "checkpoint_every": 500, "validate_every": 500},
+ "optimizer": {"warmup_steps": 117}}
+ out = next_config(config, world_size=7, train_rows=124584, epochs=5)
+ assert out["model"] == config["model"] and out["loss"] == config["loss"]
+ t = out["training"]
+ assert t["gradient_accumulation_steps"] == 14 and t["batch_size"] == 1 and t["resume"] is None
+ assert t["steps"] == -(-5 * 124584 // (7 * 14)) and out["optimizer"]["warmup_steps"] == round(.03 * t["steps"])
+ assert json.loads(json.dumps(config))["training"]["steps"] == 3887 # input untouched
+ four = next_config(config, world_size=4, train_rows=124584, epochs=5)["training"]
+ assert four["gradient_accumulation_steps"] == 24 and four["steps"] == 6489 # global batch 96
+
+
+def test_adopt_restores_the_resume_chain(tmp_path):
+ import argparse
+ from fastfill.v2.autorun import Autopilot
+ pilot = Autopilot(argparse.Namespace(repo=str(tmp_path)))
+ base = tmp_path / "runs" / "job"
+ for suffix in ("", "-resume1", "-resume2"):
+ (tmp_path / "runs" / f"job{suffix}").mkdir(parents=True, exist_ok=True)
+ (tmp_path / "runs" / f"job{suffix}.log").write_text("")
+ job = pilot.adopt({"outputs": [str(base)], "output": str(base)})
+ assert job["resumes"] == 2 and job["output"].endswith("job-resume2") and len(job["outputs"]) == 3
+
+
+def _phase_f(tmp_path, *, failed=0, over=0, upload_error=False, rows_late=False, **extra):
+ """Autopilot.run end to end with stubbed processes: a finished prior run, data unchanged, phase F checks."""
+ import argparse
+ from unittest.mock import patch
+ from fastfill.v2 import autorun
+
+ class Proc:
+ def __init__(self, code=0): self.code = code
+ def wait(self): return self.code
+ def poll(self): return self.code
+
+ class Thread: # the LLM thread: never finishes when rows arrive late, else nothing to do
+ def __init__(self, *a, **k): pass
+ def start(self): pass
+ def join(self, timeout): pass
+
+ old = tmp_path / "old"
+ old.mkdir(parents=True, exist_ok=True)
+ for name in ("train.jsonl", "validation.jsonl", "test.jsonl"):
+ (old / name).write_text("{}\n")
+ (tmp_path / "data").mkdir(exist_ok=True)
+ (tmp_path / "data" / "NEXT_DATA.json").write_text(json.dumps({"name": "../old", "repo": "r", "revision": "v", "folder": "f", "commit": "c",
+ "sha256": {n: autorun.sha256(old / n) for n in ("train.jsonl", "validation.jsonl", "test.jsonl")}}))
+ a = argparse.Namespace(repo=str(tmp_path), current_data=str(old), data_root=str(tmp_path / "data"), data_wait_h=0., epochs=5,
+ eval_rows="3000", model_repo="mock/model", llm_env=str(tmp_path / "noenv"), llm_rows="300",
+ current=[("prior", "c", "1,2,3,4")], **extra)
+ pilot = autorun.Autopilot(a)
+ checkpoint = tmp_path / "runs/prior/model-step-10"
+ checkpoint.mkdir(parents=True, exist_ok=True)
+ (checkpoint.parent / "run_manifest_start.json").write_text(json.dumps({"config": {"training": {}, "optimizer": {}, "loss": {"position_cell": .5}}}))
+ pilot.supervise = lambda *a: None
+ pilot.evaluate_candidates = lambda *a, **k: {"checkpoint": str(checkpoint), "report": "x", "score": 1.}
+ pilot.launch = lambda *a: Proc()
+ if upload_error:
+ def upload(*a): raise OSError("403 Forbidden")
+ pilot.upload = upload
+ else:
+ pilot.upload = lambda *a: None
+ commands, pilot.gpus_used = [], set()
+ def sh(args, **kw):
+ args = list(map(str, args))
+ commands.append(args)
+ pilot.gpus_used.add(kw.get("gpus"))
+ if args[0] == "bash":
+ for i in range(5):
+ room = pilot.runs / "roomgenbench" / args[-1] / f"r{i}.layout_boxes"
+ room.mkdir(parents=True, exist_ok=True)
+ (room / "receipt.json").write_text("{}")
+ if "fastfill.v2.evaluate" in args:
+ if rows_late: # the LLM rows land while the test set is evaluated
+ rows = pilot.runs / "llm-prompt-300/rows.jsonl"
+ rows.parent.mkdir(parents=True, exist_ok=True)
+ rows.write_text("{}\n")
+ out = Path(args[args.index("--output") + 1])
+ out.mkdir(parents=True) # evaluate refuses an existing output
+ (out / "report.json").write_text(json.dumps({**_report(.3, .6, 2.), "requests": 10, "inference_failed_requests": failed,
+ "over_capacity_requests": over}))
+ return Proc()
+ pilot.sh = sh
+ with patch.object(autorun.threading, "Thread", Thread):
+ pilot.run()
+ return pilot, commands
+
+
+
+def test_phase_f_reports_over_capacity_requests_without_failing_on_them(tmp_path):
+ pilot, commands = _phase_f(tmp_path, failed=5, over=5)
+ assert pilot.status["phase"] == "done" and pilot.status["accepted"]
+ decodes = {a[a.index("--grid-decode") + 1] for a in commands if "--grid-decode" in a}
+ assert decodes == {"spread", "argmax"} # the model alone is evaluated even without LLM rows
+ assert "5 (5 over capacity)" in (pilot.dir / "SUMMARY.md").read_text()
+ pilot, _ = _phase_f(tmp_path / "real", failed=6, over=5)
+ assert pilot.status["failures"] == ["test: 1 of 10 requests without a layout"]
+
+
+def test_phase_f_survives_an_upload_error_and_late_llm_rows(tmp_path):
+ pilot, commands = _phase_f(tmp_path, upload_error=True, rows_late=True)
+ assert pilot.status["phase"] == "done-with-failures" and pilot.status["failures"] == ["upload failed: OSError: 403 Forbidden"]
+ assert "upload failed" in (pilot.dir / "SUMMARY.md").read_text()
+ assert any("test-" in " ".join(a) for a in commands) # the test set was still evaluated after the failed upload
+
+
+def test_phase_f_can_run_again_after_a_failed_upload(tmp_path):
+ _phase_f(tmp_path, upload_error=True)
+ pilot, commands = _phase_f(tmp_path) # e.g. after fixing the credentials
+ assert pilot.status["phase"] == "done" and pilot.status["uploaded_model"] == "mock/model/main7-cell05-old-e5"
+ assert not any(a[0] == "bash" for a in commands) # the same checkpoint's five rooms are not exported twice
+
+
+def test_a_start_config_skips_phases_a_to_d_and_uses_the_given_gpus(tmp_path):
+ (tmp_path / "start.json").write_text(json.dumps({"training": {}, "optimizer": {}, "loss": {"position_cell": .5}}))
+ pilot, commands = _phase_f(tmp_path, start_config=str(tmp_path / "start.json"), gpus="0,1,2,3", autorun_dir="autorun-a")
+ assert pilot.status["phase"] == "done" and pilot.status["run"] == "main4-cell05-old-e5"
+ assert not any(a[0] == "git" for a in commands) # no runs to finish, no data to wait for
+ assert pilot.gpus_used <= {None, "0", "1", "2", "3"} and (tmp_path / "runs/autorun-a/SUMMARY.md").is_file()
+
+
+def test_no_gpu_evaluation_starts_beside_a_running_training(tmp_path, monkeypatch):
+ import argparse
+ import pytest
+ from fastfill.v2 import autorun
+ pilot = autorun.Autopilot(argparse.Namespace(repo=str(tmp_path), current_data=str(tmp_path), eval_rows="3000"))
+ (tmp_path / "run/model-step-5").mkdir(parents=True)
+ monkeypatch.setattr(autorun, "any_training_alive", lambda: True)
+ monkeypatch.setattr(pilot, "sh", lambda *a, **k: pytest.fail("a GPU job was started"))
+ with pytest.raises(RuntimeError, match="while a fastfill.v2.train process runs"):
+ pilot.evaluate_candidates([[str(tmp_path / "run")]], "select1", data=tmp_path / "sample.jsonl")
+
+
+def _candidates(tmp_path, monkeypatch, *, alive, implementation=None):
+ """An autopilot over runs a and b (steps 100-400, the newest three compete) whose evaluate writes a scorable
+ spread/minimal report of ``implementation`` (default: the code on disk under its repo)."""
+ import argparse
+ from fastfill.v2 import autorun
+ pilot = autorun.Autopilot(argparse.Namespace(repo=str(tmp_path), eval_rows="3000"))
+ data = tmp_path / "sample.jsonl"
+ data.write_text("{}\n")
+ for run in "ab":
+ for step in (100, 200, 300, 400):
+ (tmp_path / run / f"model-step-{step}").mkdir(parents=True)
+ calls = []
+
+ class Proc:
+ def wait(self): return 0
+
+ def sh(args, **kw):
+ args = list(map(str, args))
+ calls.append(args)
+ out = Path(args[args.index("--output") + 1])
+ report(out, args[args.index("--checkpoint") + 1], projection=args[args.index("--projection") + 1],
+ grid_decode=args[args.index("--grid-decode") + 1], implementation_sha256=implementation or autorun.implementation_sha256(tmp_path))
+ return Proc()
+
+ def report(out, checkpoint, pos=2., **fields):
+ out.mkdir(parents=True)
+ (out / "report.json").write_text(json.dumps({**_report(.3, .6, pos), "requests": 10, "inference_failed_requests": 0,
+ "data_sha256": autorun.sha256(data), "checkpoint": str(Path(checkpoint).resolve()), **fields}))
+ pilot.sh = sh
+ monkeypatch.setattr(autorun, "any_training_alive", lambda: alive)
+ runs = [[str(tmp_path / "a")], [str(tmp_path / "b")]]
+ return pilot, data, runs, calls, report
+
+
+def test_a_restart_beside_the_formal_training_reuses_the_six_select1_reports_without_evaluating(tmp_path, monkeypatch):
+ """The live server's shape: six cached select1 reports of one older implementation, the data sample unchanged."""
+ pilot, data, runs, calls, report = _candidates(tmp_path, monkeypatch, alive=True) # any evaluation would raise
+ old = "1c97878e" + "0" * 56
+ for i, (run, step) in enumerate((r, s) for r in "ab" for s in (200, 300, 400)):
+ report(pilot.runs / f"select1-{run}-model-step-{step}", tmp_path / run / f"model-step-{step}", pos=2. - i / 10,
+ projection="minimal", grid_decode="spread", implementation_sha256=old)
+ winner = pilot.evaluate_candidates(runs, "select1", data=data)
+ assert calls == [] and winner["checkpoint"] == str(tmp_path / "b/model-step-400")
+ selection = json.loads((pilot.dir / "select1-selection.json").read_text())
+ assert len(selection) == 6 and {r["implementation_sha256"] for r in selection} == {old}
+ assert not list(pilot.runs.glob("*.stale-*"))
+
+
+def test_cached_reports_of_another_command_or_implementation_are_evaluated_again(tmp_path, monkeypatch):
+ import pytest
+ from fastfill.v2 import autorun
+ # the audit's case: a cached argmax / full-projection report of old code is not what this method runs
+ pilot, data, runs, calls, report = _candidates(tmp_path / "decode", monkeypatch, alive=False)
+ report(pilot.runs / "select-a-model-step-400", tmp_path / "decode/a/model-step-400", projection="full", grid_decode="argmax",
+ implementation_sha256="WRONG_OLD_CODE")
+ winner = pilot.evaluate_candidates(runs[:1], "select", newest=1, data=data)
+ assert len(calls) == 1 and calls[0][calls[0].index("--grid-decode") + 1] == "spread" and "minimal" in calls[0]
+ assert json.loads(Path(winner["report"]).read_text())["grid_decode"] == "spread"
+ assert len(list(pilot.runs.glob("select-a-model-step-400.stale-*"))) == 1
+ # one report missing: fresh ones carry the code on disk, so a cached report of other code is evaluated again too
+ pilot, data, runs, calls, report = _candidates(tmp_path / "mixed", monkeypatch, alive=False)
+ report(pilot.runs / "select-a-model-step-400", tmp_path / "mixed/a/model-step-400", projection="minimal", grid_decode="spread",
+ implementation_sha256="1c97878e")
+ pilot.evaluate_candidates(runs[:1], "select", newest=2, data=data)
+ assert len(calls) == 2 and {r["implementation_sha256"] for r in json.loads((pilot.dir / "select-selection.json").read_text())} \
+ == {autorun.implementation_sha256(tmp_path / "mixed")}
+ # cached reports that disagree: only those of other code than the one on disk are evaluated again
+ pilot, data, runs, calls, report = _candidates(tmp_path / "split", monkeypatch, alive=False)
+ for step, implementation in ((300, "1c97878e"), (400, autorun.implementation_sha256(tmp_path / "split"))):
+ report(pilot.runs / f"select-a-model-step-{step}", tmp_path / f"split/a/model-step-{step}", projection="minimal",
+ grid_decode="spread", implementation_sha256=implementation)
+ pilot.evaluate_candidates(runs[:1], "select", newest=2, data=data)
+ assert [c[c.index("--checkpoint") + 1] for c in calls] == [str(tmp_path / "split/a/model-step-300")]
+ # the same beside a running training: refused before any GPU job
+ pilot, data, runs, calls, report = _candidates(tmp_path / "alive", monkeypatch, alive=True)
+ report(pilot.runs / "select-a-model-step-400", tmp_path / "alive/a/model-step-400", projection="minimal", grid_decode="spread",
+ implementation_sha256="1c97878e")
+ with pytest.raises(RuntimeError, match="while a fastfill.v2.train process runs"):
+ pilot.evaluate_candidates(runs[:1], "select", newest=2, data=data)
+ assert calls == []
+ # evaluations that come back from other code than the one on disk never enter one selection
+ pilot, data, runs, calls, report = _candidates(tmp_path / "moved", monkeypatch, alive=False, implementation="changed-meanwhile")
+ with pytest.raises(RuntimeError, match="were not evaluated by implementation"):
+ pilot.evaluate_candidates(runs[:1], "select", newest=1, data=data)
+
+
+@pytest.mark.parametrize("field,value", [(None, None), ("projection", "full"), ("grid_decode", "argmax"),
+ ("data_sha256", "0" * 64)])
+def test_a_cached_report_is_evaluated_again_when_any_single_command_field_differs(tmp_path, monkeypatch, field, value):
+ from fastfill.v2 import autorun
+ pilot, data, runs, calls, report = _candidates(tmp_path, monkeypatch, alive=False)
+ fields = {"projection": "minimal", "grid_decode": "spread", "implementation_sha256": autorun.implementation_sha256(tmp_path)}
+ report(pilot.runs / "select-a-model-step-400", tmp_path / "a/model-step-400", **{**fields, **({field: value} if field else {})})
+ pilot.evaluate_candidates(runs[:1], "select", newest=1, data=data)
+ assert len(calls) == len(list(pilot.runs.glob("select-a-model-step-400.stale-*"))) == (field is not None)
+
+
+def test_the_implementation_hash_is_the_one_evaluate_records():
+ from fastfill.v2 import autorun, io
+ repo = Path(autorun.__file__).resolve().parents[2]
+ assert autorun.implementation_sha256(repo) == io.run_metadata(autorun.__file__)["implementation_sha256"]
+
+
+def test_llm_baselines_rerun_unless_rows_code_parameters_and_predictions_match(tmp_path):
+ """Real llm_baseline.run / run_evaluation outputs with a stubbed LLM; the API key never reaches the log."""
+ import argparse
+ import shutil
+ from fastfill.v2 import autorun, llm_baseline
+ from fastfill.v2.evaluate import project_minimal, run_evaluation
+ from fastfill.v2.tests.test_evaluate_fix20261006 import real_rows
+ from fastfill.v2.tests.test_llm_baseline import _answer_from_targets
+ (tmp_path / "fastfill/v2").mkdir(parents=True)
+ for code in Path(llm_baseline.__file__).parent.glob("*.py"): # the code the subprocesses would run
+ shutil.copy(code, tmp_path / "fastfill/v2" / code.name)
+ rows = [p for p in map(project_minimal, real_rows()) if p is not None]
+ answers = {llm_baseline.prompt(r["condition"]): _answer_from_targets(r) for r in rows}
+ sample, env = tmp_path / "sample.jsonl", tmp_path / "api.env"
+ sample.write_text("".join(json.dumps(r) + "\n" for r in rows))
+ env.write_text("OPENAI_BASE_URL=x\nOPENAI_API_KEY=secret-test-key\nOPENAI_MODEL=m\n")
+ pilot = autorun.Autopilot(argparse.Namespace(repo=str(tmp_path), llm_env=str(env), llm_rows="300"))
+ calls = []
+
+ def sh(args, **kw):
+ args = list(map(str, args))
+ get = lambda flag: args[args.index(flag) + 1]
+ calls.append(args[2])
+ if args[2] == "fastfill.v2.llm_baseline":
+ llm_baseline.run(get("--data"), get("--output"), get("--env"), mode=get("--mode"), max_samples=int(get("--max-samples")),
+ ask=lambda env, model, messages: answers[messages[0]["content"]])
+ else:
+ run_evaluation(get("--data"), get("--output"), predictions=get("--predictions"))
+ pilot.sh = sh
+ pilot.llm_baselines(sample)
+ assert calls == ["fastfill.v2.llm_baseline", "fastfill.v2.evaluate"] * 2 and len(pilot.llm_reports) == 2
+ calls.clear()
+ pilot.llm_baselines(sample) # a restart with the same inputs
+ assert calls == []
+ for change in ("rows", "parameters", "predictions", "evaluator", "old summary", "code"):
+ calls.clear()
+ if change == "rows":
+ sample.write_text("".join(json.dumps(r) + "\n" for r in rows[::-1]))
+ elif change == "parameters":
+ env.write_text(env.read_text() + "FASTFILL_LLM_REASONING_EFFORT=high\n")
+ elif change == "predictions": # e.g. an earlier autopilot's scored report next to regenerated predictions
+ predictions = pilot.runs / "llm-prompt-300/predictions.jsonl"
+ predictions.write_text(predictions.read_text() + "\n")
+ elif change == "evaluator": # scored by another evaluate implementation (same rows and predictions)
+ report = pilot.runs / "llm-prompt-300-eval/report.json"
+ report.write_text(json.dumps({**json.loads(report.read_text()), "implementation_sha256": "OLD_EVALUATOR"}))
+ elif change == "old summary": # the c750c31 summary: no identity recorded
+ summary = pilot.runs / "llm-harness-300/summary.json"
+ summary.write_text(json.dumps({k: v for k, v in json.loads(summary.read_text()).items()
+ if k not in ("data_sha256", "implementation_sha256")}))
+ else: # llm_baseline.py on disk changed (the stub still records the imported module's hash: no convergence check)
+ code = tmp_path / "fastfill/v2/llm_baseline.py"
+ code.write_text(code.read_text() + "\n")
+ pilot.llm_baselines(sample)
+ both = ["fastfill.v2.llm_baseline", "fastfill.v2.evaluate"]
+ assert calls == {"predictions": ["fastfill.v2.evaluate"], "evaluator": ["fastfill.v2.evaluate"],
+ "old summary": both}.get(change, both * 2), change
+ if change != "code":
+ calls.clear()
+ pilot.llm_baselines(sample) # converged: nothing left to redo
+ assert calls == [], change
+ assert "secret-test-key" not in (pilot.dir / "autorun.log").read_text()
+
+
+def test_set_overrides_an_existing_config_key_only():
+ import pytest
+ from fastfill.v2.autorun import override
+ config = {"model": {"max_objects": 128}, "training": {"steps": 10}}
+ override(config, "model.max_objects=256")
+ assert config["model"]["max_objects"] == 256
+ with pytest.raises(ValueError, match="no existing config key"):
+ override(config, "model.max_object=256")
+
+
+def test_a_roomgenbench_hand_off_is_reused_only_for_the_same_checkpoint_and_code(tmp_path, monkeypatch):
+ import argparse
+ from fastfill.v2 import autorun
+ pilot = autorun.Autopilot(argparse.Namespace(repo=str(tmp_path)))
+ launched = []
+ def sh(args, **kw):
+ launched.append(args)
+ tag = list(map(str, args))[-1]
+ for i in range(5):
+ room = pilot.runs / "roomgenbench" / tag / f"r{i}.layout_boxes"
+ room.mkdir(parents=True, exist_ok=True)
+ (room / "receipt.json").write_text("{}")
+ return autorun.subprocess.Popen(["true"])
+ monkeypatch.setattr(pilot, "sh", sh)
+ monkeypatch.setattr(autorun, "implementation_sha256", lambda root: "code-A")
+ pilot.roomgenbench("/ck/step-1", "best").wait()
+ pilot.roomgenbench("/ck/step-1", "best").wait()
+ assert len(launched) == 1 # same checkpoint, same code: reused
+ monkeypatch.setattr(autorun, "implementation_sha256", lambda root: "code-B")
+ pilot.roomgenbench("/ck/step-1", "best").wait()
+ assert len(launched) == 2 # new code: exported again, the old directory set aside
+ assert any(p.name.startswith("best.stale-") for p in (pilot.runs / "roomgenbench").iterdir())
diff --git a/code/fastfill/v2/tests/test_batch.py b/code/fastfill/v2/tests/test_batch.py
new file mode 100644
index 0000000000000000000000000000000000000000..a974d611d0b113369fae2ee7e857ca8f3f6cff72
--- /dev/null
+++ b/code/fastfill/v2/tests/test_batch.py
@@ -0,0 +1,102 @@
+import copy
+
+import pytest
+import torch
+
+from fastfill.v2.batch import TinyTokenizer, collate_samples, condition_segments
+
+
+def sample(n=2):
+ objects = [{"id": f"chair_{i}", "category": "chair", "description": "plain chair"} for i in range(n)]
+ return {
+ "schema_version": "fastfill.v2",
+ "condition": {"schema_version": "fastfill.v2", "room": {
+ "frame": "right_handed_z_up", "floor_polygon_xy_m": [[2, 4], [7, 4], [7, 8], [2, 8]],
+ "floor_z_m": 1.0, "height_m": 3.0}, "objects": objects, "constraints": []},
+ "target": {"objects": [{"id": o["id"], "target_size_local_m": [0.6, 0.5, 1.0],
+ "bottom_center_m": [3 + i, 5, 1], "yaw_rad": 0.2} for i, o in enumerate(objects)]},
+ "validity": {"position": [[True] * 3 for _ in objects], "size": [[True] * 3 for _ in objects],
+ "yaw": [True for _ in objects]}, "provenance": {
+ "source": "offline_test", "house_id": "offline_house", "split": "train"}}
+
+
+def test_condition_memory_never_contains_targets():
+ s = sample()
+ other = copy.deepcopy(s)
+ other["target"]["objects"][0]["bottom_center_m"] = [987, 654, 321]
+ a = collate_samples([s], TinyTokenizer())
+ b = collate_samples([other], TinyTokenizer())
+ assert torch.equal(a["input_ids"], b["input_ids"])
+ assert torch.equal(a["origin"], torch.tensor([[2., 4., 1.]]))
+ assert torch.equal(a["scale"], torch.tensor([[5., 4., 3.]]))
+ assert torch.allclose(a["targets"]["position_normalized"][0, 0], torch.tensor([.2, .25, 0.]))
+
+
+def test_condition_token_spans_preserve_object_and_constraints():
+ s = sample(1)
+ s["condition"]["constraints"] = [{"type": "faces_direction", "object_id": "chair_0", "direction_xy": [1, 0]}]
+ tokenizer = TinyTokenizer()
+ batch = collate_samples([s, sample(3)], tokenizer)
+ start, end = batch["object_spans"][0, 0].tolist()
+ text = tokenizer.decode(batch["input_ids"][0, start:end])
+ assert "chair_0" in text and "plain chair" in text
+ assert "faces_direction" in tokenizer.decode(batch["input_ids"][0])
+ assert batch["slot_mask"].tolist() == [[True, False, False], [True, True, True]]
+ assert not batch["validity"]["position"][0, 1:].any()
+
+
+def test_overlong_condition_is_rejected_whole():
+ with pytest.raises(ValueError, match="context"):
+ collate_samples([sample()], TinyTokenizer(), max_length=30)
+
+
+def test_missing_validity_not_assumed_valid_and_unknown_stays_nan():
+ s = sample(1)
+ s["validity"] = {}
+ s["target"]["objects"][0]["yaw_rad"] = None
+ batch = collate_samples([s], TinyTokenizer())
+ assert not batch["validity"]["size"].any()
+ assert torch.isnan(batch["targets"]["yaw"][0, 0])
+
+
+def test_fixed_geometry_and_floor_masks_are_explicit():
+ s = sample(1)
+ s["condition"]["objects"][0].update(support_parent="floor", fixed_size_local_m=[.8, .7, 1.2])
+ s["target"]["objects"][0]["target_size_local_m"] = [.8, .7, 1.2]
+ b = collate_samples([s], TinyTokenizer())
+ assert b["fixed_position_mask"].tolist() == [[[False, False, True]]]
+ assert b["fixed_size_mask"].all()
+ assert torch.equal(b["fixed_size"][0, 0], torch.tensor([.8, .7, 1.2]))
+
+
+def test_partial_fixed_size_and_condition_demo_conflicts():
+ s = sample(1)
+ s["condition"]["objects"][0]["fixed_size_local_m"] = [.6, None, 1.]
+ b = collate_samples([s], TinyTokenizer())
+ assert b["fixed_size_mask"].tolist() == [[[True, False, True]]]
+ assert b["conditions"][0] == s["condition"]
+ s["condition"]["objects"][0]["fixed_size_local_m"] = [.8, None, 1.]
+ with pytest.raises(ValueError, match="conflicts"):
+ collate_samples([s], TinyTokenizer())
+ s = sample(1)
+ s["condition"]["objects"][0]["support_parent"] = "floor"
+ s["target"]["objects"][0]["bottom_center_m"][2] = 1.2
+ with pytest.raises(ValueError, match="floor support.*conflicts"):
+ collate_samples([s], TinyTokenizer())
+
+
+def test_validity_arrays_follow_reordered_target_ids():
+ s = sample(2)
+ s["target"]["objects"].reverse()
+ s["validity"]["position"] = [[False] * 3, [True] * 3]
+ s["validity"]["yaw_symmetry_order"] = [2, 1]
+ b = collate_samples([s], TinyTokenizer())
+ assert b["validity"]["position"].tolist() == [[[True] * 3, [False] * 3]]
+ assert b["yaw_symmetry_order"].tolist() == [[1, 2]]
+
+
+def test_schema_rejects_asset_geometry_leakage():
+ s = sample(1)
+ s["condition"]["objects"][0]["asset_id"] = "source-model-id"
+ with pytest.raises(ValueError, match="unknown fields"):
+ collate_samples([s], TinyTokenizer())
diff --git a/code/fastfill/v2/tests/test_batch_augment_fix20261006.py b/code/fastfill/v2/tests/test_batch_augment_fix20261006.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e8b5d1a36c1bd9924b596ed1b9e8e16bb3224be
--- /dev/null
+++ b/code/fastfill/v2/tests/test_batch_augment_fix20261006.py
@@ -0,0 +1,324 @@
+"""Audit fixes 2026-10-06 (owner B): group bookkeeping, rendering order, augmentation, flag filter."""
+import json
+import math
+from pathlib import Path
+
+import pytest
+from shapely.geometry import Polygon
+import torch
+
+from fastfill.v2.batch import TinyTokenizer, augment_sample, collate_samples, condition_segments
+from fastfill.v2.data import filter_rows_by_flags
+from fastfill.v2.direct_layout import request_to_condition
+from fastfill.v2.geometry import wrap_yaw
+from fastfill.v2.matching import certify_group
+from fastfill.v2.validation import _geometry, footprint
+
+REAL_ROWS = Path("/Volumes/harddisk/FastFill_v2_multisource_20261006/eligible-local/validation.jsonl")
+NO_AUGMENT = {"rotate90": False, "mirror": False, "shuffle_objects": False,
+ "drop_constraints_p": 0., "drop_support_p": 0., "category_only_description_p": 0., "minimal_form_p": 0.}
+L_SHAPE = [[0, 0], [6, 0], [6, 3], [3, 3], [3, 5], [0, 5]]
+
+
+def sample():
+ """L-shaped room, support chain, every constraint reference kind, one exchangeable pair."""
+ objects = [{"id": "chair_a", "category": "chair", "description": "chair"},
+ {"id": "chair_b", "category": "chair", "description": "chair"},
+ {"id": "table", "category": "table", "description": "oak table", "support_parent": "floor"},
+ {"id": "lamp", "category": "lamp", "description": "lamp", "support_parent": "table"},
+ {"id": "rug", "category": "rug", "description": "rug", "support_parent": "floor"}]
+ targets = {"chair_a": ([1., 1., 0.], .3), "chair_b": ([5., 1., 0.], 2.), "table": ([2., 2., 0.], 1.),
+ "lamp": ([2., 2., .7], -2.5), "rug": ([1.5, 4., 0.], 0.)}
+ return {
+ "schema_version": "fastfill.v2",
+ "condition": {"schema_version": "fastfill.v2", "room": {
+ "frame": "right_handed_z_up", "floor_polygon_xy_m": L_SHAPE, "floor_z_m": 0., "height_m": 2.8,
+ "floor_known": True, "boundary_known": True, "boundary_quality": "polygon", "room_type": "office",
+ "fixed_objects": [{"id": "fixed_0000", "category": "door", "size_local_m": [.1, .9, 2.1],
+ "bottom_center_m": [4.5, 0.05, 0.], "yaw_rad": 1.2}]},
+ "objects": objects, "constraints": [
+ {"type": "near", "object_id": "lamp", "target_id": "table", "max_distance_m": .5},
+ {"type": "between", "object_id": "rug", "target_ids": ["table", "fixed_0000"]},
+ {"type": "on", "object_id": "lamp", "parent_id": "table"},
+ {"type": "faces_direction", "object_id": "table", "direction_xy": [1, 0]},
+ {"type": "keepout", "polygon_xy_m": [[.1, .1], [.4, .1], [.4, .4], [.1, .4]]}]},
+ "target": {"objects": [{"id": o["id"], "target_size_local_m": [.6, .5, .7], "bottom_center_m": targets[o["id"]][0],
+ "yaw_rad": targets[o["id"]][1]} for o in objects]},
+ "validity": {"position": [[True] * 3 for _ in objects], "size": [[True] * 3 for _ in objects],
+ "yaw": [True for _ in objects], "yaw_symmetry_order": [2, 2, 2, 1, 2],
+ "size_axis_swap_allowed": [False] * len(objects),
+ "exchangeable_group": ["anonymous_0", "anonymous_0", None, None, None]},
+ "provenance": {"source": "offline_test", "house_id": "h", "split": "train", "legacy_flags": {}}}
+
+
+def rendered(condition):
+ text = "".join(segment for segment, _ in condition_segments(condition))
+ body = text[text.index("{"):text.rindex("}") + 1]
+ return text, json.loads(body)
+
+
+def boxes(s):
+ return [_geometry(obj, "target") for obj in s["target"]["objects"]]
+
+
+def real_row():
+ if not REAL_ROWS.is_file():
+ pytest.skip("external data disk is not mounted")
+ with REAL_ROWS.open() as stream:
+ for line in stream:
+ row = json.loads(line)
+ if sum(obj.get("exchangeable_group") is not None for obj in row["condition"]["objects"]) >= 2:
+ return row
+ pytest.skip("no legacy-group row in the first rows")
+
+
+# --- C1/C2: group bookkeeping -------------------------------------------------
+
+def test_collate_exposes_groups_per_request_slot_from_validity():
+ s = sample()
+ s["target"]["objects"].reverse()
+ s["validity"]["exchangeable_group"] = [None, None, None, "anonymous_0", "anonymous_0"]
+ b = collate_samples([s], TinyTokenizer())
+ assert b["exchangeable_group"] == [["anonymous_0", "anonymous_0", None, None, None]]
+ assert all("exchangeable_group" not in obj for obj in b["objects"][0])
+
+
+def test_legacy_condition_groups_migrate_into_validity_and_leave_the_text():
+ s = sample()
+ del s["validity"]["exchangeable_group"]
+ for obj in s["condition"]["objects"][:2]:
+ obj["exchangeable_group"] = "anonymous_7"
+ s["target"]["objects"].reverse()
+ b = collate_samples([s], TinyTokenizer())
+ assert b["exchangeable_group"] == [["anonymous_7", "anonymous_7", None, None, None]]
+ assert all("exchangeable_group" not in obj for obj in b["objects"][0] + b["conditions"][0]["objects"])
+ assert "exchangeable_group" not in TinyTokenizer().decode(b["input_ids"][0])
+ s["validity"]["exchangeable_group"] = [None] * 5
+ with pytest.raises(ValueError, match="both condition objects and validity"):
+ collate_samples([s], TinyTokenizer())
+ # Inference conditions have no target rows, hence no labels (never request slot 0's label for everyone).
+ inference = collate_samples([{"condition": s["condition"]}], TinyTokenizer())
+ assert inference["exchangeable_group"] == [[None] * 5] and not inference["validity"]["position"].any()
+
+
+def test_group_labels_must_be_nonempty_strings_or_null():
+ s = sample()
+ s["validity"]["exchangeable_group"][0] = ""
+ with pytest.raises(ValueError, match="exchangeable_group labels"):
+ collate_samples([s], TinyTokenizer())
+
+
+# --- C3: rendering --------------------------------------------------------------
+
+def test_rendered_condition_order_and_stripped_keys():
+ s = sample()
+ s["condition"]["objects"][0]["exchangeable_group"] = "anonymous_0"
+ s["condition"]["room"]["_debug"] = {"x": 1}
+ s["condition"]["objects"][1]["attributes"] = {"_hidden": 1, "colour": "red"}
+ text, body = rendered(s["condition"])
+ assert list(body) == ["schema_version", "room", "constraints", "objects"]
+ assert "boundary_quality" not in text and "exchangeable_group" not in text and "_debug" not in text and "_hidden" not in text
+ assert body["room"]["boundary_known"] is True and body["room"]["floor_known"] is True
+ assert body["objects"][1]["attributes"] == {"colour": "red"} and body["room"]["fixed_objects"]
+ assert body["objects"][0] == {"id": "chair_a", "category": "chair", "description": "chair"}
+ with pytest.raises(ValueError, match="unknown fields"):
+ condition_segments({**s["condition"], "target": {}})
+
+
+def test_direct_request_renders_the_same_room_fields_as_a_training_rectangle():
+ direct = request_to_condition({"room_type": "office", "room_size_m": [4., 3., 2.7], "furniture_list": ["chair"]})
+ training = {**sample()["condition"]}
+ training["room"] = {k: v for k, v in training["room"].items() if k != "fixed_objects"} # legacy rows omit an empty list
+ _, direct_body = rendered(direct)
+ _, training_body = rendered(training)
+ assert set(direct_body["room"]) == set(training_body["room"])
+ assert "boundary_quality" not in direct_body["room"]
+ assert "boundary_quality" in direct["room"] # only the rendering drops it; the schema field is untouched
+
+
+# --- C10: augmentation ----------------------------------------------------------
+
+def test_eval_path_and_zero_augmentation_are_identity():
+ s = sample()
+ plain = collate_samples([s], TinyTokenizer())
+ zero = collate_samples([s], TinyTokenizer(), augment=NO_AUGMENT, generator=torch.Generator().manual_seed(0))
+ assert torch.equal(plain["input_ids"], zero["input_ids"])
+ assert torch.equal(plain["targets"]["position_normalized"], zero["targets"]["position_normalized"])
+ assert plain["conditions"] == zero["conditions"] == [sample()["condition"]]
+ with pytest.raises(ValueError, match="unknown augmentation"):
+ augment_sample(s, {"rotate": True})
+ with pytest.raises(ValueError, match="probability"):
+ augment_sample(s, {"drop_support_p": 1.5})
+
+
+def test_augmentation_is_deterministic_given_a_generator():
+ s = sample()
+ runs = [augment_sample(s, {}, torch.Generator().manual_seed(3)) for _ in range(2)]
+ assert runs[0] == runs[1]
+ assert runs[0] != augment_sample(s, {}, torch.Generator().manual_seed(4))
+ assert s == sample() # input untouched
+
+
+@pytest.mark.parametrize("mirror", [False, True])
+def test_rigid_augmentation_preserves_containment_distances_and_relative_yaw(mirror):
+ s = sample()
+ before = boxes(s)
+ seen, mirrored_runs = set(), set()
+ for seed in range(12):
+ a = augment_sample(s, {**NO_AUGMENT, "rotate90": True, "mirror": mirror}, torch.Generator().manual_seed(seed))
+ polygon = a["condition"]["room"]["floor_polygon_xy_m"]
+ seen.add((tuple(map(tuple, polygon))))
+ room = Polygon(polygon)
+ mirrored = room.exterior.is_ccw != Polygon(L_SHAPE).exterior.is_ccw # a reflection flips winding, a rotation never does
+ mirrored_runs.add(mirrored)
+ assert room.is_valid and math.isclose(room.area, Polygon(L_SHAPE).area)
+ assert [min(p[q] for p in polygon) for q in range(2)] == [0, 0]
+ after = boxes(a)
+ assert all(room.buffer(1e-9).covers(footprint(obj)) for obj in after)
+ door = [_geometry(x["condition"]["room"]["fixed_objects"][0], "target") for x in (s, a)]
+ for i in range(len(after)):
+ for j in range(i + 1, len(after)):
+ assert math.isclose(math.dist(before[i]["_pos"], before[j]["_pos"]), math.dist(after[i]["_pos"], after[j]["_pos"]), abs_tol=1e-9)
+ relative = [rows[i]["_yaw"] - rows[j]["_yaw"] for rows in (before, after)]
+ assert abs(wrap_yaw(relative[1] - (-relative[0] if mirrored else relative[0]))) < 1e-9
+ assert math.isclose(math.dist(door[0]["_pos"], before[0]["_pos"]), math.dist(door[1]["_pos"], after[0]["_pos"]), abs_tol=1e-9)
+ assert abs(wrap_yaw((door[1]["_yaw"] - after[0]["_yaw"]) - (-1 if mirrored else 1) * (door[0]["_yaw"] - before[0]["_yaw"]))) < 1e-9
+ assert room.buffer(1e-9).covers(Polygon(a["condition"]["constraints"][4]["polygon_xy_m"]))
+ direction = a["condition"]["constraints"][3]["direction_xy"]
+ facing = after[2]["_yaw"] # the table faces +X before augmentation: the facing/direction angle is invariant
+ assert math.isclose(math.cos(facing) * direction[0] + math.sin(facing) * direction[1], math.cos(1.), abs_tol=1e-9)
+ assert all(obj["target_size_local_m"] == [.6, .5, .7] for obj in a["target"]["objects"])
+ collate_samples([a], TinyTokenizer())
+ assert len(seen) >= 3 and mirrored_runs == ({True, False} if mirror else {False})
+
+
+def test_rotation_swaps_null_coordinates_with_their_validity_flags():
+ s = sample()
+ s["target"]["objects"][2]["bottom_center_m"] = [2., None, 0.]
+ s["validity"]["position"][2] = [True, False, True]
+ s["target"]["objects"][3]["yaw_rad"] = None
+ s["validity"]["yaw"][3] = False
+ for seed in range(8):
+ a = augment_sample(s, {**NO_AUGMENT, "rotate90": True}, torch.Generator().manual_seed(seed))
+ position = a["target"]["objects"][2]["bottom_center_m"]
+ assert [v is not None for v in position] == a["validity"]["position"][2]
+ assert a["target"]["objects"][3]["yaw_rad"] is None
+ b = collate_samples([a], TinyTokenizer())
+ assert b["validity"]["position"][0, 2].tolist() == a["validity"]["position"][2]
+
+
+def test_shuffle_keeps_alignment_references_and_certifiable_groups():
+ s = sample()
+ s["validity"]["position"][1] = [False, False, False] # chair_b label becomes unusable
+ certify_group(s["condition"]["objects"], s["condition"]["constraints"], [0, 1])
+ original = {o["description"]: (t["bottom_center_m"], t["yaw_rad"]) for o, t in zip(s["condition"]["objects"], s["target"]["objects"])}
+ orders = set()
+ for seed in range(8):
+ a = augment_sample(s, {**NO_AUGMENT, "shuffle_objects": True}, torch.Generator().manual_seed(seed))
+ objects, targets = a["condition"]["objects"], a["target"]["objects"]
+ ids = [o["id"] for o in objects]
+ orders.add(tuple(o["description"] for o in objects))
+ assert ids == [f"obj_{i:04d}" for i in range(5)] and [t["id"] for t in targets] == ids
+ by_id = {o["id"]: o for o in objects}
+ for obj, target in zip(objects, targets):
+ if obj["category"] != "chair":
+ assert (target["bottom_center_m"], target["yaw_rad"]) == original[obj["description"]]
+ lamp = next(o for o in objects if o["category"] == "lamp")
+ assert by_id[lamp["support_parent"]]["category"] == "table"
+ assert next(o for o in objects if o["category"] == "table")["support_parent"] == "floor"
+ kinds = {c["type"]: c for c in a["condition"]["constraints"]}
+ assert by_id[kinds["near"]["object_id"]]["category"] == "lamp" and by_id[kinds["near"]["target_id"]]["category"] == "table"
+ assert by_id[kinds["on"]["parent_id"]]["category"] == "table"
+ assert by_id[kinds["between"]["object_id"]]["category"] == "rug"
+ assert by_id[kinds["between"]["target_ids"][0]]["category"] == "table" and kinds["between"]["target_ids"][1] == "fixed_0000"
+ assert by_id[kinds["faces_direction"]["object_id"]]["category"] == "table"
+ b = collate_samples([a], TinyTokenizer())
+ groups = b["exchangeable_group"][0]
+ members = [i for i, g in enumerate(groups) if g == "anonymous_0"]
+ assert [objects[i]["category"] for i in members] == ["chair", "chair"] and len(members) == 2
+ certify_group(b["objects"][0], b["conditions"][0]["constraints"], members)
+ invalid = [i for i, row in enumerate(a["validity"]["position"]) if not any(row)]
+ assert len(invalid) == 1 and objects[invalid[0]]["category"] == "chair"
+ assert a["validity"]["yaw_symmetry_order"] == [1 if o["category"] == "lamp" else 2 for o in objects]
+ assert b["yaw_symmetry_order"][0].tolist() == a["validity"]["yaw_symmetry_order"]
+ assert not b["validity"]["position"][0, invalid[0]].any()
+ assert len(orders) > 1
+
+
+def test_shuffle_rejects_misaligned_validity_and_colliding_ids():
+ s = sample()
+ s["validity"]["yaw"] = [True]
+ with pytest.raises(ValueError, match="aligned with target objects"):
+ augment_sample(s, {**NO_AUGMENT, "shuffle_objects": True}, torch.Generator().manual_seed(0))
+ s = sample()
+ s["condition"]["room"]["fixed_objects"][0]["id"] = "obj_0002"
+ with pytest.raises(ValueError, match="collide"):
+ augment_sample(s, {**NO_AUGMENT, "shuffle_objects": True}, torch.Generator().manual_seed(0))
+
+
+def test_drop_augmentations_follow_probabilities():
+ s = sample()
+ dropped = augment_sample(s, {**NO_AUGMENT, "drop_constraints_p": 1., "drop_support_p": 1., "category_only_description_p": 1.})
+ assert dropped["condition"]["constraints"] == []
+ assert all("support_parent" not in o and o["description"] == o["category"] for o in dropped["condition"]["objects"])
+ assert dropped["target"] == s["target"] and dropped["validity"] == s["validity"]
+ collate_samples([dropped], TinyTokenizer())
+ kept = augment_sample(s, NO_AUGMENT)
+ assert kept["condition"] == s["condition"]
+
+
+def test_mirror_refuses_asset_local_metadata_and_rotate90_leaves_openings_unturned():
+ s = sample()
+ s["condition"]["room"]["fixed_objects"][0]["semantic_front_local"] = [1., 0., 0.]
+ with pytest.raises(ValueError, match="mirror cannot transform"):
+ for seed in range(8):
+ augment_sample(s, {**NO_AUGMENT, "mirror": True}, torch.Generator().manual_seed(seed))
+ s = sample()
+ s["condition"]["room"]["openings"] = [{"kind": "door"}]
+ for seed in range(8): # round 10 review: left unturned instead of raising inside the DataLoader
+ out = augment_sample(s, {**NO_AUGMENT, "rotate90": True}, torch.Generator().manual_seed(seed))
+ assert out["condition"]["room"] == s["condition"]["room"] and out["target"] == s["target"]
+
+
+# --- C9 loader side ---------------------------------------------------------------
+
+def test_filter_rows_by_flags_uses_legacy_flag_truthiness():
+ rows = [{"provenance": {"legacy_flags": {"oob_objects": 5}}}, {"provenance": {"legacy_flags": {"oob_objects": 0}}},
+ {"provenance": {"legacy_flags": {"fixed_collision": 1}}}, {"provenance": {}}]
+ assert filter_rows_by_flags(rows, ["oob_objects", "fixed_collision", "overlapping_furniture"]) == rows[1:2] + rows[3:]
+ assert filter_rows_by_flags(rows, []) == rows
+ with pytest.raises(ValueError, match="exclude_flags"):
+ filter_rows_by_flags(rows, "oob_objects")
+
+
+# --- Real multisource row ----------------------------------------------------------
+
+def test_real_legacy_row_collates_renders_and_augments():
+ row = real_row()
+ tokenizer = TinyTokenizer()
+ b = collate_samples([row], tokenizer, max_length=10**7, max_objects=10**4)
+ text = tokenizer.decode(b["input_ids"][0])
+ assert "exchangeable_group" not in text and "boundary_quality" not in text
+ assert text.index('"room":') < text.index('"constraints":') < text.index('"objects":')
+ assert text.rstrip().endswith("]}\nAssistant:")
+ expected = [obj.get("exchangeable_group") for obj in row["condition"]["objects"]]
+ assert b["exchangeable_group"] == [expected] and sum(g is not None for g in expected) >= 2
+ assert all("exchangeable_group" not in obj for obj in b["objects"][0])
+ before = boxes(row)
+ for seed in range(3):
+ a = augment_sample(row, {}, torch.Generator().manual_seed(seed))
+ ab = collate_samples([a], tokenizer, max_length=10**7, max_objects=10**4)
+ groups = ab["exchangeable_group"][0]
+ # Drops may merge requests into wider recomputed groups but never lose a build-time member.
+ assert sum(g is not None for g in groups) >= sum(g is not None for g in expected)
+ for label in {g for g in groups if g}:
+ members = [i for i, g in enumerate(groups) if g == label]
+ certify_group(ab["objects"][0], ab["conditions"][0]["constraints"], members)
+ assert ab["validity"]["position"].sum() == b["validity"]["position"].sum()
+ assert ab["validity"]["size"].sum() == b["validity"]["size"].sum()
+ rigid = augment_sample(row, {"shuffle_objects": False, "mirror": False}, torch.Generator().manual_seed(seed))
+ after = boxes(rigid)
+ assert all(math.isclose(math.dist(before[i]["_pos"], before[j]["_pos"]), math.dist(after[i]["_pos"], after[j]["_pos"]), abs_tol=1e-6)
+ for i in range(len(after)) for j in range(i + 1, len(after)))
+ assert all(abs(wrap_yaw((after[i]["_yaw"] - after[0]["_yaw"]) - (before[i]["_yaw"] - before[0]["_yaw"]))) < 1e-9 for i in range(len(after)))
+ assert all(x["target_size_local_m"] == y["target_size_local_m"] for x, y in zip(row["target"]["objects"], rigid["target"]["objects"]))
diff --git a/code/fastfill/v2/tests/test_batch_handoff_r2.py b/code/fastfill/v2/tests/test_batch_handoff_r2.py
new file mode 100644
index 0000000000000000000000000000000000000000..74f82516dd908c565941b53251aabd5695999657
--- /dev/null
+++ b/code/fastfill/v2/tests/test_batch_handoff_r2.py
@@ -0,0 +1,247 @@
+"""Round 2 (owner B2): shared three-field projection (K5), box-symmetry collate tier (K1), hand-off place inference (K6)."""
+import json
+import os
+from pathlib import Path
+
+import pytest
+import torch
+
+from fastfill.v2.batch import (AUGMENT_DEFAULTS, TinyTokenizer, augment_sample, collate_samples, condition_segments,
+ is_axis_aligned_rectangle, render_minimal_condition, tokenize_condition)
+from fastfill.v2.direct_layout import export_handoff, infer_support, layout_to_roomgenbench, request_to_condition
+
+REAL_ROWS = Path("/Volumes/harddisk/FastFill_v2_multisource_20261006/eligible-local/validation.jsonl")
+REFERENCE = Path(os.environ.get("FASTFILL_ROOMGENBENCH_ROOT") or Path(__file__).resolve().parents[3] / "RoomGenBench")
+ONLY_MINIMAL = {"rotate90": False, "mirror": False, "shuffle_objects": False, "drop_constraints_p": 0.,
+ "drop_support_p": 0., "category_only_description_p": 0., "minimal_form_p": 1.}
+
+
+def row():
+ """Rectangular training row (polygon starts at another corner) with everything the projection must drop."""
+ objects = [{"id": "obj_0000", "category": "chair", "description": "wooden chair", "support_parent": "floor"},
+ {"id": "obj_0001", "category": "chair", "description": "wooden chair"},
+ {"id": "obj_0002", "category": "desk", "description": "oak desk", "support_parent": "floor"},
+ {"id": "obj_0003", "category": "lamp", "description": "lamp", "support_parent": "obj_0002"}]
+ targets = [([1., 1., 0.], .3), ([3., 1., 0.], 2.), ([2., 2., 0.], 1.), ([2., 2., .75], -2.5)]
+ return {"schema_version": "fastfill.v2",
+ "condition": {"schema_version": "fastfill.v2", "room": {
+ "frame": "right_handed_z_up", "floor_polygon_xy_m": [[4.5, 3.25], [0., 3.25], [0., 0.], [4.5, 0.]],
+ "floor_z_m": 0., "height_m": 2.8, "floor_known": True, "boundary_known": True,
+ "boundary_quality": "polygon", "room_type": "office",
+ "fixed_objects": [{"id": "fixed_0000", "category": "door", "size_local_m": [.1, .9, 2.1],
+ "bottom_center_m": [4.45, 1., 0.], "yaw_rad": 0.}]},
+ "objects": objects,
+ "constraints": [{"type": "near", "object_id": "obj_0003", "target_id": "obj_0002", "max_distance_m": .5}]},
+ "target": {"objects": [{"id": o["id"], "target_size_local_m": [.6, .5, .7], "bottom_center_m": p, "yaw_rad": y}
+ for o, (p, y) in zip(objects, targets)]},
+ "validity": {"position": [[True] * 3] * 4, "size": [[True] * 3] * 4, "yaw": [True] * 4,
+ "yaw_symmetry_order": [4, 2, 2, 1], "size_axis_swap_allowed": [True, False, False, False],
+ "exchangeable_group": [None] * 4},
+ "provenance": {"source": "offline_test", "house_id": "h", "split": "train", "legacy_flags": {}}}
+
+
+def request_for(condition):
+ """The direct three-field request a user would send for this room (origin at the lower corner)."""
+ room = condition["room"]
+ size = [max(p[q] for p in room["floor_polygon_xy_m"]) for q in range(2)]
+ return {"room_type": room["room_type"], "room_size_m": size + ([room["height_m"]] if room.get("height_m") else []),
+ "furniture_list": [{key: obj[key] for key in ("id", "category", "description")} for obj in condition["objects"]]}
+
+
+# --- K5: one renderer for the direct request, the augmentation and minimal evaluation -------------
+
+def test_minimal_projection_renders_byte_identical_to_the_direct_request():
+ condition = row()["condition"]
+ minimal = render_minimal_condition(condition)
+ direct = request_to_condition(request_for(condition))
+ assert condition_segments(minimal) == condition_segments(direct)
+ assert tokenize_condition(minimal, TinyTokenizer()) == tokenize_condition(direct, TinyTokenizer())
+ assert minimal["room"]["floor_polygon_xy_m"] == [[0., 0.], [4.5, 0.], [4.5, 3.25], [0., 3.25]]
+ assert minimal["room"]["boundary_known"] is direct["room"]["boundary_known"] is True
+ assert minimal["room"]["floor_known"] is direct["room"]["floor_known"] is True
+ assert minimal["constraints"] == [] and "fixed_objects" not in minimal["room"]
+ assert all(set(obj) == {"id", "category", "description"} for obj in minimal["objects"])
+ text = "".join(segment for segment, _ in condition_segments(direct))
+ assert "boundary_quality" not in text and direct["room"]["boundary_quality"] == "explicit_rectangular_request"
+ assert row()["condition"] == condition # the projection never mutates its input
+
+
+def test_minimal_projection_matches_direct_requests_on_real_rectangular_rows():
+ if not REAL_ROWS.is_file():
+ pytest.skip("external data disk is not mounted")
+ compared = 0
+ with REAL_ROWS.open() as stream:
+ for line in stream:
+ condition = json.loads(line)["condition"]
+ room = condition["room"]
+ if (not is_axis_aligned_rectangle(room["floor_polygon_xy_m"]) or not room.get("room_type")
+ or room.get("floor_z_m") != 0 or room.get("floor_known") is not True):
+ continue
+ assert condition_segments(render_minimal_condition(condition)) == condition_segments(request_to_condition(
+ request_for(condition), max_objects=max(128, len(condition["objects"]))))
+ compared += 1
+ if compared == 50:
+ break
+ assert compared == 50
+
+
+def test_axis_aligned_rectangle_test_uses_a_one_centimetre_tolerance():
+ assert is_axis_aligned_rectangle([[0, 0], [4, 0.009], [4, 3], [0.01, 3]])
+ assert not is_axis_aligned_rectangle([[0, 0], [4, 0.02], [4, 3], [0, 3]])
+ assert not is_axis_aligned_rectangle([[0, 0], [4, 0], [4, 3], [2, 3.5], [0, 3]])
+ assert not is_axis_aligned_rectangle([[2, 0], [4, 2], [2, 4], [0, 2]]) # rotated square
+ assert not is_axis_aligned_rectangle([[0, 0], [4, 0], [3, 3], [1, 3]]) # trapezoid
+
+
+def test_minimal_form_replaces_the_condition_drops_floor_fixed_z_and_regroups():
+ s = row()
+ full = collate_samples([augment_sample(s, {**ONLY_MINIMAL, "minimal_form_p": 0.})], TinyTokenizer())
+ a = augment_sample(s, ONLY_MINIMAL, torch.Generator().manual_seed(0))
+ minimal = collate_samples([a], TinyTokenizer())
+ assert a["condition"] == render_minimal_condition(s["condition"]) and a["target"] == s["target"]
+ # The floor declaration fixed obj_0000/obj_0002 z in the full form; the minimal form learns it.
+ assert full["fixed_position_mask"][0, :, 2].tolist() == [True, False, True, False]
+ assert not minimal["fixed_position_mask"].any()
+ # Removing support_parent made the two wooden chairs identical requests: matched by geometry now.
+ assert full["exchangeable_group"] == [[None] * 4]
+ assert minimal["exchangeable_group"] == [["anonymous_0", "anonymous_0", None, None]]
+ assert torch.equal(full["targets"]["position_normalized"], minimal["targets"]["position_normalized"])
+ assert s == row()
+
+
+def test_minimal_form_needs_a_rectangle_and_defaults_to_one_half():
+ s = row()
+ s["condition"]["room"]["floor_polygon_xy_m"] = [[0., 0.], [4.5, 0.], [4.5, 3.25], [2., 3.25], [2., 4.], [0., 4.]]
+ assert augment_sample(s, ONLY_MINIMAL)["condition"] == s["condition"]
+ assert AUGMENT_DEFAULTS["minimal_form_p"] == .5
+ with pytest.raises(ValueError, match="probability"):
+ augment_sample(row(), {"minimal_form_p": 2.})
+ minimal_runs = {"fixed_objects" not in augment_sample(row(), {}, torch.Generator().manual_seed(seed))["condition"]["room"]
+ for seed in range(16)}
+ assert minimal_runs == {True, False}
+
+
+# --- K1 collate side ------------------------------------------------------------------------
+
+def test_collate_exposes_size_axis_swap_per_request_slot_and_defaults_old_rows_to_false():
+ s = row()
+ s["target"]["objects"].reverse()
+ s["validity"] = {key: list(reversed(value)) for key, value in s["validity"].items()}
+ batch = collate_samples([s], TinyTokenizer())
+ assert batch["size_axis_swap_allowed"].dtype == torch.bool
+ assert batch["size_axis_swap_allowed"].tolist() == [[True, False, False, False]]
+ old = row()
+ del old["validity"]["size_axis_swap_allowed"]
+ assert collate_samples([old], TinyTokenizer())["size_axis_swap_allowed"].tolist() == [[False] * 4]
+ short = {**old, "condition": {**old["condition"], "objects": old["condition"]["objects"][:2], "constraints": []},
+ "target": {"objects": old["target"]["objects"][:2]}, "validity": {k: v[:2] for k, v in old["validity"].items()}}
+ padded = collate_samples([row(), short], TinyTokenizer())
+ assert padded["size_axis_swap_allowed"].tolist() == [[True, False, False, False], [False] * 4]
+ bad = row()
+ bad["validity"]["size_axis_swap_allowed"][1] = 1
+ with pytest.raises(ValueError, match="size_axis_swap_allowed"):
+ collate_samples([bad], TinyTokenizer())
+
+
+def test_shuffle_carries_size_axis_swap_with_its_object():
+ for seed in range(6):
+ a = augment_sample(row(), {**ONLY_MINIMAL, "minimal_form_p": 0., "shuffle_objects": True},
+ torch.Generator().manual_seed(seed))
+ batch = collate_samples([a], TinyTokenizer())
+ swapped = [obj.get("support_parent") == "floor" and obj["category"] == "chair" for obj in a["condition"]["objects"]]
+ assert batch["size_axis_swap_allowed"][0].tolist() == swapped
+
+
+# --- K6: hand-off place inference ---------------------------------------------------------------
+
+def scene(*boxes, declared=None):
+ """Known 4 x 3 x 2.8 m room; boxes are (id, category, size, bottom_center, yaw)."""
+ condition = request_to_condition({"room_type": "living_room", "room_size_m": [4., 3., 2.8], "furniture_list": [
+ {"id": ident, "category": category} for ident, category, *_ in boxes]})
+ for obj in condition["objects"]:
+ if obj["id"] in (declared or {}):
+ obj["support_parent"] = declared[obj["id"]]
+ layout = {"schema_version": "fastfill.v2", "objects": [
+ {"id": ident, "target_size_local_m": size, "bottom_center_m": bottom, "yaw_rad": yaw}
+ for ident, _, size, bottom, yaw in boxes]}
+ return condition, layout
+
+
+def placement(condition, layout):
+ return {o["id"]: (o["place"], o["place_id"], o["support_status"]) for o in layout_to_roomgenbench(condition, layout)["objects"]}
+
+
+def test_shelf_middle_tier_objects_are_never_declared():
+ shelf = ("shelf", "shelf", [1., .4, 2.], [1., .2, 0.], 0.)
+ condition, layout = scene(shelf, ("book_mid", "book", [.2, .15, .25], [1., .2, 1.], 0.),
+ ("book_low", "book", [.2, .15, .25], [.8, .2, .01], 0.),
+ ("book_top", "book", [.2, .15, .25], [1.2, .2, 2.01], 0.), declared={"shelf": "floor"})
+ places = placement(condition, layout)
+ assert places["shelf"] == ("floor", "floor", "declared")
+ assert places["book_mid"] == ("unknown", None, "unknown") # middle tier: neither floor nor the shelf top
+ assert places["book_low"] == ("floor", "floor", "inferred") # bottom tier rests within 2 cm of the floor
+ assert places["book_top"] == ("on_object", "shelf", "inferred") # top surface: a candidate, never a declaration
+
+
+def test_painting_near_plant_is_an_inferred_candidate_or_unknown_never_wall():
+ plant = ("plant", "plant", [.5, .5, 1.2], [.3, .3, 0.], 0.)
+ painting = ("painting", "painting", [.04, .8, .6], [.3, .3, 1.22], 0.)
+ places = placement(*scene(plant, painting))
+ assert places["painting"] == ("on_object", "plant", "inferred")
+ assert places["plant"] == ("floor", "floor", "inferred")
+ beside = ("painting", "painting", [.04, .8, .6], [.3, .6, 1.22], 0.) # centre outside the plant footprint
+ assert placement(*scene(plant, beside))["painting"] == ("unknown", None, "unknown")
+ hung = ("painting", "painting", [.04, .8, .6], [.3, .3, 1.5], 0.)
+ assert placement(*scene(plant, hung))["painting"] == ("unknown", None, "unknown")
+ assert placement(*scene(plant, hung, declared={"painting": "wall"}))["painting"] == ("wall", "wall", "declared")
+
+
+def test_on_object_candidate_is_the_highest_strictly_lower_box_inside_a_rotated_footprint():
+ table = ("table", "table", [1.6, .8, .75], [2., 1.5, 0.], .6)
+ tray = ("tray", "tray", [.4, .3, .02], [2., 1.5, .75], 0.)
+ cup = ("cup", "cup", [.08, .08, .1], [2., 1.5, .77], 0.)
+ places = placement(*scene(table, tray, cup))
+ assert places["cup"] == ("on_object", "tray", "inferred") and places["tray"] == ("on_object", "table", "inferred")
+ # Two papers on the same plane never point at each other; both rest on the table.
+ papers = [("paper_a", "paper", [.3, .2, .001], [1.9, 1.45, .75], 0.), ("paper_b", "paper", [.3, .2, .001], [2., 1.5, .75], 0.)]
+ places = placement(*scene(table, *papers))
+ assert places["paper_a"][1:] == places["paper_b"][1:] == ("table", "inferred")
+ # Inside the table's axis-aligned extent but outside its yawed footprint: no candidate.
+ corner = ("cup", "cup", [.08, .08, .1], [2.75, 1.85, .75], 0.)
+ assert placement(*scene(table, corner))["cup"] == ("unknown", None, "unknown")
+ assert infer_support({"id": "x", "bottom_center_m": [0., 0., 3.]}, None, [], None) == (None, "unknown")
+
+
+def test_an_inference_never_closes_a_cycle_with_a_declared_parent():
+ # Audit C4: chair declared on table, table undeclared and predicted on the chair's top; before: table -> chair.
+ chair = ("chair", "chair", [1., 1., 1.], [2., 1.5, 0.], 0.)
+ table = ("table", "table", [1., 1., 1.], [2., 1.5, 1.], 0.)
+ for boxes in ((chair, table), (table, chair)):
+ places = placement(*scene(*boxes, declared={"chair": "table"}))
+ assert places == {"chair": ("on_object", "table", "declared"), "table": ("unknown", None, "unknown")}
+ # The cycle-closing box is skipped, not the rule: the next lower candidate (the table) still supports the cup.
+ desk = ("desk", "desk", [1.6, .8, .75], [2., 1.5, 0.], 0.)
+ tray = ("tray", "tray", [.4, .3, .02], [2., 1.5, .75], 0.)
+ cup = ("cup", "cup", [.08, .08, .1], [2., 1.5, .77], 0.)
+ assert placement(*scene(desk, tray, cup, declared={"tray": "cup"}))["cup"] == ("on_object", "desk", "inferred")
+
+
+def test_place_and_support_status_reach_assets_and_the_real_assembler_receipt(tmp_path):
+ shelf = ("shelf", "shelf", [1., .4, 2.], [1., .2, 0.], 0.)
+ plant = ("plant", "plant", [.5, .5, 1.2], [3., .3, 0.], 0.)
+ condition, layout = scene(shelf, ("book", "book", [.2, .15, .25], [1., .2, 1.], 0.), plant,
+ ("painting", "painting", [.04, .8, .6], [3., .3, 1.22], 0.), declared={"shelf": "floor"})
+ handoff = export_handoff(tmp_path / "handoff", condition, layout)
+ expected = {"shelf": ("floor", "declared"), "book": ("unknown", "unknown"), "plant": ("floor", "inferred"),
+ "painting": ("on_object", "inferred")}
+ registry = {entry["type"]: entry for entry in map(json.loads, (handoff / "assets.jsonl").read_text().splitlines())}
+ assert {key: (entry["place"], entry["support_status"]) for key, entry in registry.items()} == expected
+ downstream = json.loads((handoff / "roomgenbench_scene.json").read_text())
+ assert {o["id"]: (o["place"], o["support_status"]) for o in downstream["objects"]} == expected
+ if not (REFERENCE / "bench" / "assemble.py").is_file():
+ pytest.skip("RoomGenBench reference assembler is unavailable; set FASTFILL_ROOMGENBENCH_ROOT")
+ from fastfill.v2.roomgenbench import assemble_handoff
+ receipt = assemble_handoff(handoff, tmp_path / "assembled", roomgenbench_root=REFERENCE)
+ assert {o["id"]: (o["place"], o["support_status"]) for o in receipt["objects"]} == expected
+ assert {o["id"]: o["support_parent"] for o in receipt["objects"]} == {"shelf": "floor", "book": None, "plant": "floor", "painting": "plant"}
+ assert receipt["assembly_complete"] and (tmp_path / "assembled" / f'{receipt["scene_key"]}.glb').is_file()
diff --git a/code/fastfill/v2/tests/test_bounded_io.py b/code/fastfill/v2/tests/test_bounded_io.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa420ecfc29c79a7d09fc8431e4f003edb2b02d0
--- /dev/null
+++ b/code/fastfill/v2/tests/test_bounded_io.py
@@ -0,0 +1,20 @@
+import json
+import pytest
+
+from fastfill.v2.io import read_samples
+from fastfill.v2.tests.test_execution import sample
+
+
+def test_bounded_read_does_not_parse_unrequested_tail(tmp_path):
+ path = tmp_path / "train.jsonl"
+ path.write_text(json.dumps(sample()) + "\nnot-json-outside-selected-prefix\n")
+ rows = read_samples(path, training=True, max_samples=1)
+ assert len(rows) == 1
+ with pytest.raises(json.JSONDecodeError):
+ read_samples(path)
+
+
+@pytest.mark.parametrize("limit", [0, -1, True, 1.5])
+def test_bounded_read_requires_positive_integer(tmp_path, limit):
+ with pytest.raises(ValueError, match="positive integer"):
+ read_samples(tmp_path / "absent", max_samples=limit)
diff --git a/code/fastfill/v2/tests/test_cell_costs.py b/code/fastfill/v2/tests/test_cell_costs.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ac76855d0f25e0a82b8c742908c6d4e94fedef4
--- /dev/null
+++ b/code/fastfill/v2/tests/test_cell_costs.py
@@ -0,0 +1,179 @@
+"""Expected-geometry loss: per-cell boundary / door-clearance costs and their gradient on the cell logits."""
+import numpy as np
+import pytest
+import torch
+
+from fastfill.v2.batch import AUGMENT_DEFAULTS, augment_sample, collate_samples
+from fastfill.v2.cell_costs import cell_cost_maps, cell_positions, door_zones
+from fastfill.v2.evaluate import _collapse_counts
+from fastfill.v2.losses import GeometryCriterion, LossConfig
+from fastfill.v2.schema import validate_condition
+
+GRID = 16
+L_ROOM = [[0., 0.], [8., 0.], [8., 4.], [4., 4.], [4., 8.], [0., 8.]] # L-shape: the x > 4, y > 4 quarter is outside
+SQUARE = [[0., 0.], [4., 0.], [4., 4.], [0., 4.]]
+DOOR = {"type": "door", "wall_index": 0, "position_on_wall": .5, "width_m": 1., "height_m": 2.}
+
+
+def room(polygon, openings=()):
+ return {"frame": "right_handed_z_up", "floor_polygon_xy_m": polygon, "floor_z_m": 0., "floor_known": True,
+ "boundary_known": True, "height_m": 2.7, "openings": list(openings)}
+
+
+def cell(x_index, y_index):
+ return x_index * GRID + y_index
+
+
+def test_cells_outside_an_l_shaped_room_cost_one_and_inside_cost_zero():
+ costs, mask = cell_cost_maps(room(L_ROOM), [([.4, .4], 0., True)], {"grid": GRID})
+ assert mask.tolist() == [True]
+ assert costs[0, cell(14, 14)] == pytest.approx(1.) # missing quarter of the L
+ assert costs[0, cell(4, 4)] == 0. and costs[0, cell(14, 2)] == 0.
+
+
+def test_a_wall_flush_label_cell_is_free_although_its_centre_would_cross_the_wall():
+ # 4 m room, 0.25 m cells: a 2 m deep bed flush with the y=0 wall has its centre at y=1.0, on a cell border;
+ # the y index 3 cell centre (0.875) would push it 0.125 m through the wall.
+ r = room(SQUARE)
+ costs, _ = cell_cost_maps(r, [([1.6, 2.], 0., True)], {"grid": GRID})
+ centre_only, _ = cell_cost_maps(r, [([1.6, 2.], 0., True)], {"grid": GRID, "sub": 1})
+ assert costs[0, cell(8, 3)] == 0.
+ assert centre_only[0, cell(8, 3)] > 0.
+
+
+def test_door_zone_costs_floor_objects_only():
+ r = room(SQUARE, [DOOR])
+ zone, = door_zones(r, .8)
+ assert sorted(map(tuple, np.round(zone, 6))) == [(1.5, 0.), (1.5, .8), (2.5, 0.), (2.5, .8)]
+ costs, _ = cell_cost_maps(r, [([.5, .5], 0., True), ([.5, .5], 0., False)], {"grid": GRID})
+ in_front = cell(8, 1) # x 2.0-2.25, y 0.25-0.5: right in front of the door
+ assert costs[0, in_front] > .5 and costs[1, in_front] == 0.
+ assert costs[0, cell(2, 12)] == 0.
+
+
+def test_windows_and_clockwise_rooms():
+ window = {**DOOR, "type": "window", "sill_height_m": .9}
+ assert door_zones(room(SQUARE, [window]), .8) == []
+ clockwise, = door_zones(room(SQUARE[::-1], [{**DOOR, "wall_index": 2}]), .8) # same y=0 wall, reversed order
+ assert clockwise[:, 1].min() == pytest.approx(0.) and clockwise[:, 1].max() == pytest.approx(.8)
+
+
+def test_cell_positions_follow_the_grid_encoding():
+ from fastfill.v2.geometry import encode_grid_position
+ r = room(L_ROOM)
+ centres = cell_positions(r, {"grid": GRID, "sub": 1})[:, 0]
+ normalized = torch.tensor(centres / 8.)
+ index, _ = encode_grid_position(normalized, GRID)
+ assert index.tolist() == list(range(GRID * GRID))
+
+
+def sample(r):
+ condition = {"schema_version": "fastfill.v2", "room": r, "constraints": [],
+ "objects": [{"id": "bed", "category": "bed", "description": "bed", "support_parent": "floor"},
+ {"id": "art", "category": "painting", "description": "painting", "support_parent": "wall"}]}
+ target = {"schema_version": "fastfill.v2",
+ "objects": [{"id": "bed", "target_size_local_m": [1.6, 2., .5], "bottom_center_m": [2., 3., 0.], "yaw_rad": 0.},
+ {"id": "art", "target_size_local_m": [.8, .05, .6], "bottom_center_m": [2., 3.97, 1.2],
+ "yaw_rad": 0.}]}
+ validity = {"position": [[True] * 3] * 2, "size": [[True] * 3] * 2, "yaw": [True, True], "yaw_symmetry_order": [2, 2],
+ "size_axis_swap_allowed": [False, False], "exchangeable_group": [None, None]}
+ return {"schema_version": "fastfill.v2", "condition": validate_condition(condition), "target": target, "validity": validity}
+
+
+def collated(s, **kw):
+ from fastfill.v2.batch import TinyTokenizer
+ return collate_samples([s], TinyTokenizer(), max_length=10 ** 8, **kw)
+
+
+def test_collate_adds_cost_rows_only_when_asked():
+ s = sample(room(SQUARE, [DOOR]))
+ plain = collated(s)
+ assert "cell_costs" not in plain
+ batch = collated(s, cell_costs={"grid": GRID})
+ assert batch["cell_costs"].shape == (1, 2, GRID * GRID) and batch["cell_cost_mask"].tolist() == [[True, True]]
+ in_front = cell(8, 1)
+ assert batch["cell_costs"][0, 0, in_front] > 0 and batch["cell_costs"][0, 1, in_front] == 0 # the painting is not on the floor
+
+
+def test_rotated_sample_costs_turn_with_the_room():
+ s = sample(room(L_ROOM, [DOOR]))
+ base = collated(s, cell_costs={"grid": GRID})["cell_costs"][0, 0].reshape(GRID, GRID)
+ options = {**AUGMENT_DEFAULTS, "shuffle_objects": False, "minimal_form_p": 0., "drop_constraints_p": 0.,
+ "drop_support_p": 0., "category_only_description_p": 0.}
+ seen = set()
+ for seed in range(12):
+ turned = augment_sample(s, options, torch.Generator().manual_seed(seed))
+ costs = collated(turned, cell_costs={"grid": GRID})["cell_costs"][0, 0].reshape(GRID, GRID)
+ # raster pixels are aligned to the turned room's corner, so a few border footprint samples may flip (2/16 each)
+ matches = [k for k in range(4) if torch.allclose(torch.rot90(base, k), costs, atol=.2)]
+ assert matches, "costs must be a quarter turn of the unturned map"
+ seen.update(matches)
+ assert len(seen) > 1
+
+
+def predictions_for(batch, logits):
+ b, n = batch["slot_mask"].shape
+ residuals = torch.zeros((b, n, GRID * GRID, 2))
+ k = logits.detach().argmax(-1)
+ xy = torch.stack((torch.div(k, GRID, rounding_mode="floor"), k % GRID), -1).float().add(.5).div(GRID)
+ return {"position_normalized": torch.cat((xy, torch.zeros((b, n, 1))), -1), "size": batch["targets"]["size"].nan_to_num(1.),
+ "yaw_logits": torch.zeros((b, n, 12)), "yaw_residuals": torch.zeros((b, n, 12)), "slot_mask": batch["slot_mask"],
+ "position_cell_logits": logits, "position_cell_residuals": residuals}
+
+
+def test_default_weight_leaves_the_objective_unchanged():
+ batch = collated(sample(room(SQUARE, [DOOR])), cell_costs={"grid": GRID})
+ logits = torch.randn((1, 2, GRID * GRID), generator=torch.Generator().manual_seed(0))
+ off = GeometryCriterion(LossConfig())(predictions_for(batch, logits), batch)
+ plain = collated(sample(room(SQUARE, [DOOR])))
+ base = GeometryCriterion(LossConfig())(predictions_for(plain, logits), plain)
+ assert torch.equal(off["loss"], base["loss"]) and "expected_geometry" not in off["term_sums"]
+
+
+def test_gradient_lowers_the_probability_of_costly_cells():
+ batch = collated(sample(room(L_ROOM, [DOOR])), cell_costs={"grid": GRID})
+ logits = torch.zeros((1, 2, GRID * GRID), requires_grad=True)
+ result = GeometryCriterion(LossConfig(position=0., size=0., yaw_cls=0., yaw_reg=0., expected_geometry=1.))(
+ predictions_for(batch, logits), batch)
+ result["loss"].backward()
+ grad = logits.grad[0, 0]
+ assert grad[cell(14, 14)] > 0 > grad[cell(4, 12)] # descent lowers the outside cell, raises a free one
+ expected = float(batch["cell_costs"][0, :2].mean(-1).mean()) # uniform logits: mean cost per object
+ assert float(result["expected_geometry"]) == pytest.approx(expected, rel=1e-5)
+
+
+def test_expected_geometry_requires_cost_rows():
+ batch = collated(sample(room(SQUARE)))
+ logits = torch.zeros((1, 2, GRID * GRID))
+ with pytest.raises(ValueError, match="cell_costs"):
+ GeometryCriterion(LossConfig(expected_geometry=1.))(predictions_for(batch, logits), batch)
+
+
+def test_collapse_counts_report_footprint_and_door_violations():
+ r = room(SQUARE, [DOOR])
+ boxes = [([2., .3, 0.], [1., .5, .8], 0.), # floor, in front of the door
+ ([2., 3., 0.], [1., .5, .8], 0.), # floor, free
+ ([3.9, 2., 0.], [1., .5, .8], 0.), # half its footprint through the x=4 wall
+ ([2., .3, 1.5], [.5, .05, .5], 0.)] # on the wall above the door zone: not a floor object
+ counts = _collapse_counts(boxes, ["a", "b", "c", "d"], r, [0., 0., 0.], [4., 4., 2.7])
+ assert counts["floor_objects"] == 3 and counts["door_blocking_objects"] == 1
+ assert counts["footprint_outside_objects"] == 1
+
+
+def test_thin_floor_objects_never_block_doors():
+ s = sample(room(SQUARE, [DOOR]))
+ s["target"]["objects"][0]["target_size_local_m"] = [.5, .5, .02] # a carpet
+ in_front = cell(8, 1)
+ assert collated(s, cell_costs={"grid": GRID})["cell_costs"][0, 0, in_front] == 0
+ r = room(SQUARE, [DOOR])
+ carpet = [([2., .3, 0.], [1., .5, .02], 0.)]
+ assert _collapse_counts(carpet, ["carpet"], r, [0., 0., 0.], [4., 4., 2.7])["door_blocking_objects"] == 0
+
+
+def test_a_label_already_in_a_costly_cell_gets_no_cost_row():
+ s = sample(room(SQUARE, [DOOR]))
+ s["target"]["objects"][0].update(target_size_local_m=[.6, .6, .8], bottom_center_m=[2., .4, 0.]) # blocks the door
+ batch = collated(s, cell_costs={"grid": GRID})
+ assert batch["cell_cost_mask"].tolist() == [[False, True]] and batch["cell_costs"][0, 0].abs().sum() == 0
+ kept = collated(s, cell_costs={"grid": GRID, "drop_label_conflicts": False})
+ assert kept["cell_cost_mask"].tolist() == [[True, True]] and kept["cell_costs"][0, 0, cell(8, 1)] > 0
diff --git a/code/fastfill/v2/tests/test_complete_cohort.py b/code/fastfill/v2/tests/test_complete_cohort.py
new file mode 100644
index 0000000000000000000000000000000000000000..00d710ffebbeb3b50b68b61a8a1ee2886204e059
--- /dev/null
+++ b/code/fastfill/v2/tests/test_complete_cohort.py
@@ -0,0 +1,190 @@
+"""A fair complete-label comparison cohort must not rewrite parent samples."""
+from copy import deepcopy
+import hashlib
+import json
+from pathlib import Path
+from unittest.mock import patch
+
+import pytest
+
+from fastfill.v2 import cohort
+
+
+SPLITS = ("train", "validation", "test")
+
+
+def sample(uid, split="train", *, complete=True):
+ return {"schema_version": "fastfill.v2", "condition": {
+ "schema_version": "fastfill.v2", "room": {
+ "frame": "right_handed_z_up", "floor_polygon_xy_m": [[0, 0], [4, 0], [4, 3], [0, 3]],
+ "floor_z_m": 0., "height_m": None},
+ "objects": [{"id": "chair", "category": "chair", "description": "chair 🪑"}],
+ "constraints": []}, "target": {"schema_version": "fastfill.v2", "objects": [{
+ "id": "chair", "target_size_local_m": [0.5, 0.6, 0.8],
+ "bottom_center_m": [1.0, 2.0, 0.0], "yaw_rad": 0.2}]},
+ "validity": {"position": [[True] * 3], "size": [[True] * 3], "yaw": [complete]},
+ "provenance": {"legacy_uid": uid, "source": "MultiScan", "split": split}}
+
+
+def line(record):
+ return (" \t" + json.dumps(record, ensure_ascii=False, separators=(", ", ": ")) + " \r\n").encode()
+
+
+def dataset(root, rows=None):
+ root.mkdir()
+ rows = rows or {split: [sample(split, split)] for split in SPLITS}
+ for split in SPLITS:
+ (root / f"{split}.jsonl").write_bytes(b"".join(line(r) for r in rows.get(split, [])))
+ (root / "manifest.json").write_text(json.dumps({"schema_version": "fastfill.v2",
+ "builder": "selected-v3.2-bridge", "front_policy": "strict", "split_samples": {
+ split: len(rows.get(split, [])) for split in SPLITS}}) + "\n")
+ return root
+
+
+def assert_absent(output):
+ assert not output.exists()
+ assert not tuple(output.parent.glob(f".{output.name}-*"))
+
+
+def test_preserves_eligible_bytes_order_and_original_split(tmp_path):
+ rows = {split: [sample(split + "-first", split), sample(split + "-partial", split, complete=False),
+ sample(split + "-last", split)] for split in SPLITS}
+ source = dataset(tmp_path / "parent", rows)
+ before = {p.name: p.read_bytes() for p in source.iterdir()}
+ output = tmp_path / "complete"
+ result = cohort.build_complete_cohort(source, output)
+ for split in SPLITS:
+ assert (output / f"{split}.jsonl").read_bytes() == line(rows[split][0]) + line(rows[split][2])
+ assert result["split_samples"] == dict.fromkeys(SPLITS, 2)
+ assert result["split_skipped"] == dict.fromkeys(SPLITS, 1)
+ assert result["samples_written"] == 6
+ assert result["parent_hashes"] == {str((source / name).resolve()): hashlib.sha256(blob).hexdigest()
+ for name, blob in before.items()}
+ assert {p.name: p.read_bytes() for p in source.iterdir()} == before
+ assert json.loads((output / "manifest.json").read_text()) == result
+
+
+def test_one_incomplete_object_rejects_whole_scene_not_only_that_slot(tmp_path):
+ partial = sample("two-objects")
+ partial["condition"]["objects"].append({"id": "table", "category": "table", "description": "table"})
+ partial["target"]["objects"].append({"id": "table", "target_size_local_m": None,
+ "bottom_center_m": [2., 2., 0.], "yaw_rad": 0.})
+ partial["validity"] = {"position": [[True] * 3] * 2,
+ "size": [[True] * 3, [False] * 3], "yaw": [True, True]}
+ source = dataset(tmp_path / "parent", {"train": [partial, sample("complete")]})
+ result = cohort.build_complete_cohort(source, tmp_path / "complete")
+ assert result["split_samples"]["train"] == 1
+ assert result["split_skipped"]["train"] == 1
+ assert (tmp_path / "complete/train.jsonl").read_bytes() == line(sample("complete"))
+
+
+@pytest.mark.parametrize("corruption", ["missing-id", "extra-id", "duplicate-id", "zero-size", "unwrapped-yaw"])
+def test_invalid_complete_geometry_aborts_without_output(tmp_path, corruption):
+ invalid = sample("invalid")
+ targets = invalid["target"]["objects"]
+ if corruption == "missing-id":
+ targets.clear()
+ elif corruption == "extra-id":
+ targets.append({**targets[0], "id": "extra"})
+ elif corruption == "duplicate-id":
+ targets.append(deepcopy(targets[0]))
+ elif corruption == "zero-size":
+ targets[0]["target_size_local_m"][0] = 0.
+ else:
+ targets[0]["yaw_rad"] = 10.
+ source = dataset(tmp_path / "parent", {"train": [sample("good"), invalid]})
+ output = tmp_path / "complete"
+ with pytest.raises(ValueError):
+ cohort.build_complete_cohort(source, output)
+ assert_absent(output)
+
+
+def test_floor_condition_conflict_is_rejected_by_geometry_preflight(tmp_path):
+ invalid = sample("floating-floor")
+ invalid["condition"]["objects"][0]["support_parent"] = "floor"
+ invalid["target"]["objects"][0]["bottom_center_m"][2] = .2
+ source = dataset(tmp_path / "parent", {"train": [invalid]})
+ output = tmp_path / "complete"
+ with pytest.raises(ValueError, match="floor support condition conflicts"):
+ cohort.build_complete_cohort(source, output)
+ assert_absent(output)
+
+
+def test_no_eligible_training_rows_aborts_even_with_valid_holdout(tmp_path):
+ source = dataset(tmp_path / "parent", {"train": [sample("partial", complete=False)],
+ "validation": [sample("validation", "validation")], "test": [sample("test", "test")]})
+ output = tmp_path / "complete"
+ with pytest.raises(ValueError, match="no eligible training"):
+ cohort.build_complete_cohort(source, output)
+ assert_absent(output)
+
+
+def test_existing_output_is_never_replaced(tmp_path):
+ source = dataset(tmp_path / "parent")
+ output = tmp_path / "complete"
+ output.mkdir()
+ (output / "mine.txt").write_text("preserve")
+ with pytest.raises(FileExistsError):
+ cohort.build_complete_cohort(source, output)
+ assert (output / "mine.txt").read_text() == "preserve"
+
+
+@pytest.mark.parametrize("destination", ["child", "ancestor", "protected-source"])
+def test_output_location_guards(tmp_path, destination):
+ source = dataset(tmp_path / "parent")
+ output = source / "child" if destination == "child" else tmp_path
+ if destination == "protected-source":
+ output = Path("/Volumes/harddisk/3D_Room_Collections/new-v2-cohort")
+ with pytest.raises(ValueError, match="outside source data"):
+ cohort.build_complete_cohort(source, output)
+
+
+@pytest.mark.parametrize("filename", ["train.jsonl", "manifest.json"])
+def test_parent_change_during_filter_aborts_atomic_publication(tmp_path, filename):
+ source = dataset(tmp_path / "parent")
+ original = cohort._geometry_rows
+ def concurrent_change(*args):
+ value = original(*args)
+ with (source / filename).open("ab") as handle:
+ handle.write(b"\n")
+ return value
+ output = tmp_path / "complete"
+ with patch.object(cohort, "_geometry_rows", concurrent_change), pytest.raises(ValueError, match="changed"):
+ cohort.build_complete_cohort(source, output)
+ assert_absent(output)
+
+
+def test_missing_parent_manifest_fails_before_creating_staging(tmp_path):
+ source = dataset(tmp_path / "parent")
+ (source / "manifest.json").unlink()
+ output = tmp_path / "complete"
+ with pytest.raises(FileNotFoundError, match="manifest.json"):
+ cohort.build_complete_cohort(source, output)
+ assert_absent(output)
+
+
+def test_manifest_snapshot_hash_matches_the_decoded_parent_metadata(tmp_path):
+ source = dataset(tmp_path / "parent")
+ original = cohort._digest
+ manifest_path = source / "manifest.json"
+ changed = False
+ def concurrent_manifest_read(path):
+ nonlocal changed
+ if Path(path) == source / "train.jsonl" and not changed:
+ changed = True
+ replacement = json.loads(manifest_path.read_text())
+ replacement["builder"] = "changed-parent"
+ manifest_path.write_text(json.dumps(replacement))
+ return original(path)
+ output = tmp_path / "complete"
+ with patch.object(cohort, "_digest", concurrent_manifest_read), pytest.raises(ValueError, match="changed"):
+ cohort.build_complete_cohort(source, output)
+ assert_absent(output)
+
+
+def test_cli_builds_same_controlled_cohort(tmp_path, capsys):
+ source = dataset(tmp_path / "parent")
+ output = tmp_path / "complete"
+ cohort.main(["--data-root", str(source), "--output", str(output)])
+ assert json.loads(capsys.readouterr().out)["split_samples"] == dict.fromkeys(SPLITS, 1)
+ assert (output / "train.jsonl").read_bytes() == (source / "train.jsonl").read_bytes()
diff --git a/code/fastfill/v2/tests/test_constraint_schema.py b/code/fastfill/v2/tests/test_constraint_schema.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef6fb850e317b65f971b23a59a88268e0a773546
--- /dev/null
+++ b/code/fastfill/v2/tests/test_constraint_schema.py
@@ -0,0 +1,26 @@
+from copy import deepcopy
+import pytest
+
+from fastfill.v2.schema import validate_condition
+from fastfill.v2.tests.test_execution import sample
+
+
+@pytest.mark.parametrize("targets", [None, ["b"], ["b", "b"], ["b", "missing"], [["b"], "c"], [1, "c"], ["a", "c"]])
+def test_between_rejects_malformed_or_invalid_reference_list(targets):
+ condition = deepcopy(sample()["condition"])
+ condition["objects"] += [{"id": "b", "category": "chair", "description": "chair"},
+ {"id": "c", "category": "chair", "description": "chair"}]
+ condition["constraints"] = [{"type": "between", "object_id": "a", "target_ids": targets}]
+ with pytest.raises(ValueError, match="between"):
+ validate_condition(condition)
+
+
+def test_between_and_against_wall_validate_without_rewriting_condition():
+ condition = deepcopy(sample()["condition"])
+ condition["objects"] += [{"id": "b", "category": "chair", "description": "chair"},
+ {"id": "c", "category": "chair", "description": "chair"}]
+ condition["constraints"] = [{"type": "between", "object_id": "a", "target_ids": ["b", "c"]},
+ {"type": "against_wall", "object_id": "b", "tolerance_m": .1}]
+ before = deepcopy(condition)
+ validate_condition(condition)
+ assert condition == before
diff --git a/code/fastfill/v2/tests/test_data.py b/code/fastfill/v2/tests/test_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca9b3f066d459e88a229ac103ebac76040f6c421
--- /dev/null
+++ b/code/fastfill/v2/tests/test_data.py
@@ -0,0 +1,232 @@
+"""Tests for source geometry rather than inherited v1 interpretation."""
+import csv
+import json
+import math
+from pathlib import Path
+import sys
+
+import numpy as np
+import pytest
+
+from fastfill.v2.adapters import canonical_obb, multiscan_sample
+from fastfill.v2.data import build_dataset, grouped_split
+from fastfill.v2.audit import audit_sources, main as audit_main
+from fastfill.v2.data import main as build_main
+from fastfill.v2.data import _output_path
+
+
+def row(object_id="1", **overrides):
+ return {"object_id": object_id, "scan_id": "scan_00", "category": "chair",
+ "obb_center": "[2, 3, 1]", "obb_half_extents": "[0.4, 0.5, 0.2]",
+ "obb_axes": "[1,0,0, 0,0,1, 0,-1,0]", "front": "[0,-1,0]",
+ "up": "[0,0,1]", "is_architectural": "False", "is_opening": "False",
+ **overrides}
+
+
+def region(**overrides):
+ return {"scan_id": "scan_00", "poly_loop": "[[0,0],[4,0],[4,4],[0,4]]",
+ "floor_height": "0.25", "ceiling_height": "3.25",
+ "height_reliable": "True", "room_type": "office", **overrides}
+
+
+def test_half_extents_axis_permutation_and_bottom_center():
+ box = canonical_obb([2, 3, 1], [.4, .5, .2],
+ [[1, 0, 0], [0, 0, 1], [0, -1, 0]], [0, -1, 0], [0, 0, 1])
+ assert box["target_size_local_m"] == pytest.approx([.4, .8, 1])
+ assert box["bottom_center_m"] == pytest.approx([2, 3, .5])
+ assert box["yaw_rad"] == pytest.approx(-math.pi / 2)
+
+
+def test_full_lengths_and_millimeters_are_not_halved_twice():
+ box = canonical_obb([2000, 3000, 1000], [400, 800, 1000], np.eye(3),
+ [1, 0, 0], [0, 0, 1], half_extents=False, unit_scale=.001)
+ assert box["target_size_local_m"] == pytest.approx([.4, .8, 1])
+ assert box["bottom_center_m"] == pytest.approx([2, 3, .5])
+
+
+def test_yaw_does_not_change_local_size_and_corners_round_trip():
+ for angle in [0, .7, math.pi - .01, -math.pi]:
+ c, s = math.cos(angle), math.sin(angle)
+ axes = [[c, s, 0], [-s, c, 0], [0, 0, 1]]
+ box = canonical_obb([2, 3, 1], [.2, .4, .5], axes, axes[0], axes[2])
+ assert box["target_size_local_m"] == pytest.approx([.4, .8, 1])
+ source = np.array([[x, y, z] for x in [-.2, .2] for y in [-.4, .4]
+ for z in [-.5, .5]]) @ np.asarray(axes) + [2, 3, 1]
+ local = (source - box["bottom_center_m"]) @ np.asarray(axes).T
+ assert local.min(0) == pytest.approx([-.2, -.4, 0])
+ assert local.max(0) == pytest.approx([.2, .4, 1])
+
+
+@pytest.mark.parametrize("extents", [[0, .4, .5], [-.2, .4, .5], [float('nan'), .4, .5]])
+def test_invalid_size_rejected_without_epsilon_or_absolute_value(extents):
+ with pytest.raises(ValueError, match="positive"):
+ canonical_obb([0, 0, 1], extents, np.eye(3), [1, 0, 0], [0, 0, 1])
+
+
+def test_tilt_and_unknown_front_fail_closed():
+ with pytest.raises(ValueError, match="upright"):
+ canonical_obb([0, 0, 1], [.2, .4, .5], np.eye(3), [1, 0, 0], [0, .1, .99])
+ with pytest.raises(ValueError, match="front"):
+ canonical_obb([0, 0, 1], [.2, .4, .5], np.eye(3), [0, 0, 1], [0, 0, 1])
+
+
+def test_condition_never_contains_target_geometry_or_asset_identity():
+ sample = multiscan_sample(region(), [row()], {"scene_id": "house", "scan_id": "scan_00"})
+ condition = sample["condition"]
+ assert condition["objects"] == [{"id": "object_0000", "category": "chair", "description": "a chair"}]
+ assert sample["target"]["objects"][0]["bottom_center_m"] == pytest.approx([2, 3, .5])
+ assert condition["room"]["floor_z_m"] == .25
+ assert condition["room"]["boundary_known"] is False
+ assert condition["room"]["floor_known"] is False
+ assert sample["validity"]["yaw"] == [True]
+ assert sample["provenance"]["source_object_ids"] == ["1"]
+
+
+def test_missing_height_is_unknown_not_fabricated():
+ sample = multiscan_sample(region(height_reliable="False"), [row()], {"scene_id": "house"})
+ assert sample["condition"]["room"]["height_m"] is None
+
+
+def test_fixed_obstacles_preserved_without_becoming_requested_objects():
+ sample = multiscan_sample(region(), [row(), row("2", category="pillar", is_architectural="True")],
+ {"scene_id": "house"})
+ assert len(sample["condition"]["objects"]) == 1
+ assert len(sample["condition"]["room"]["fixed_objects"]) == 1
+
+
+def test_anonymous_identical_requests_are_exchangeable_without_using_target_sizes():
+ sample = multiscan_sample(region(), [row(), row("2", obb_half_extents="[0.2,0.6,0.3]")],
+ {"scene_id": "house"})
+ requests, groups = sample["condition"]["objects"], sample["validity"]["exchangeable_group"]
+ assert groups[0] == groups[1] is not None
+ assert all("exchangeable_group" not in request for request in requests)
+ assert "support_parent" not in requests[0]
+
+
+def test_audit_inventories_all_expected_parents_and_records_missing_sources(tmp_path):
+ audit = audit_sources(tmp_path)
+ assert len(audit["top_directories"]) == 5
+ assert len(audit["sources"]) == 16
+ assert all(not source["present"] for source in audit["sources"])
+ for source in audit["sources"]:
+ assert source["units"] and source["size_semantics"] and source["position_reference"]
+ assert source["semantic_front"] and source["eligibility"]
+
+
+def test_audit_reads_bounded_real_fields_and_measures_csv_rows(tmp_path):
+ base = tmp_path / "XXXpilar__multiscan-clean"
+ base.mkdir()
+ write_csv(base / "objects.csv", [row(), row("2")])
+ (base / "README.md").write_text("Actual source fixture: local OBB half extents.")
+ exported = tmp_path / "BillLin66__3D_Room_Collections" / "SpatialGen_exported"
+ exported.mkdir(parents=True)
+ (exported / "spatialgen.jsonl").write_text(json.dumps({"rooms": [{"furniture": [{"position": [0, 0, 1]}]}]}) + "\n")
+ report = audit_sources(tmp_path)
+ multi = next(source for source in report["sources"] if source["name"] == "MultiScan")
+ assert multi["actual_sample"]["measured_records"] == 2
+ assert "obb_axes" in multi["actual_sample"]["first_record_keys"]
+ assert multi["evidence"][0]["path"] == "README.md"
+ spatial = next(source for source in report["sources"] if source["name"] == "SpatialGen")
+ assert spatial["actual_sample"]["first_object_keys"] == ["position"]
+
+
+def test_audit_cli_rejects_source_output_and_existing_output(tmp_path, monkeypatch):
+ monkeypatch.setattr(sys, "argv", ["audit", "--source-root", str(tmp_path), "--output", str(tmp_path / "write.json")])
+ with pytest.raises(ValueError, match="read-only"):
+ audit_main()
+ output = tmp_path.parent / (tmp_path.name + "-audit.json")
+ monkeypatch.setattr(sys, "argv", ["audit", "--source-root", str(tmp_path), "--output", str(output)])
+ audit_main()
+ first = output.read_bytes()
+ with pytest.raises(FileExistsError):
+ audit_main()
+ assert output.read_bytes() == first
+
+
+def test_builder_cli_bounds_complete_scenes_and_keeps_source_failures_visible(tmp_path, monkeypatch):
+ source = tmp_path / "source"
+ base = source / "XXXpilar__multiscan-clean"
+ base.mkdir(parents=True)
+ write_csv(base / "objects.csv", [row(obb_half_extents="[0,1,1]"), row(scan_id="scan_01")])
+ write_csv(base / "regions.csv", [region(), region(scan_id="scan_01")])
+ write_csv(base / "scans.csv", [{"scan_id": "scan_00", "scene_id": "house"},
+ {"scan_id": "scan_01", "scene_id": "house"}])
+ output = tmp_path / "build"
+ monkeypatch.setattr(sys, "argv", ["data", "--source", "multiscan", "--source-root", str(source), "--output", str(output), "--max-scenes", "1"])
+ build_main()
+ manifest = json.loads((output / "manifest.json").read_text())
+ assert manifest["samples_written"] == 1
+ assert manifest["bounded_build"] is True
+ assert manifest["diagnostics"]["scenes_inspected"] == 2
+ assert manifest["diagnostics"]["rejections"][0]["scene_id"] == "scan_00"
+
+
+def test_target_geometry_cannot_expand_source_room_or_change_floor():
+ a = multiscan_sample(region(), [row()], {"scene_id": "house"})
+ b = multiscan_sample(region(), [row(obb_center="[200,300,20]")], {"scene_id": "house"})
+ assert a["condition"] == b["condition"]
+
+
+def test_related_scans_share_split_and_split_is_stable():
+ assert grouped_split("MultiScan", "house", 42) == grouped_split("MultiScan", "house", 42)
+ assert grouped_split("MultiScan", "house", 42) in {"train", "validation", "test"}
+
+
+def write_csv(path, records):
+ with path.open("w", newline="") as fh:
+ writer = csv.DictWriter(fh, fieldnames=list(records[0]))
+ writer.writeheader()
+ writer.writerows(records)
+
+
+def test_builder_preserves_sources_and_refuses_overwrite(tmp_path):
+ source = tmp_path / "source"
+ base = source / "XXXpilar__multiscan-clean"
+ base.mkdir(parents=True)
+ write_csv(base / "objects.csv", [row()])
+ write_csv(base / "regions.csv", [region()])
+ write_csv(base / "scans.csv", [{"scan_id": "scan_00", "scene_id": "house"}])
+ before = (base / "objects.csv").read_bytes()
+ output = tmp_path / "build"
+ manifest = build_dataset(source, output)
+ assert manifest["samples_written"] == 1
+ assert manifest["objects_written"] == 1
+ assert (base / "objects.csv").read_bytes() == before
+ assert sum(len(p.read_text().splitlines()) for p in output.glob("*.jsonl")) == 1
+ with pytest.raises(FileExistsError):
+ build_dataset(source, output)
+ with pytest.raises(ValueError, match="source"):
+ build_dataset(source, source / "new_output")
+
+
+def test_builder_rejects_source_through_symlink(tmp_path):
+ source = tmp_path / "source"
+ source.mkdir()
+ alias = tmp_path / "alias"
+ alias.symlink_to(source, target_is_directory=True)
+ with pytest.raises(ValueError, match="source"):
+ build_dataset(source, alias / "output")
+
+
+def test_alternate_source_cannot_bypass_canonical_external_root_guard(tmp_path):
+ canonical = Path("/Volumes/harddisk/3D_Room_Collections")
+ with pytest.raises(ValueError, match="source"):
+ _output_path(tmp_path / "alternate_source", canonical / "guard-test-build")
+
+
+def test_audit_alternate_source_cannot_write_canonical_external_root(tmp_path, monkeypatch):
+ import fastfill.v2.audit as audit_module
+ canonical = Path("/Volumes/harddisk/3D_Room_Collections")
+ monkeypatch.setattr(sys, "argv", ["audit", "--source-root", str(tmp_path),
+ "--output", str(canonical / "guard-test-audit.json")])
+ # Even during the expected RED run, never allow a protected-source write.
+ monkeypatch.setattr(audit_module, "audit_sources", lambda root: {"sources": []})
+ monkeypatch.setattr(Path, "mkdir", lambda *args, **kwargs: None)
+ original_open = Path.open
+ def read_only_open(path, mode="r", *args, **kwargs):
+ if any(flag in mode for flag in ("w", "a", "x")):
+ raise AssertionError("protected source write attempted")
+ return original_open(path, mode, *args, **kwargs)
+ monkeypatch.setattr(Path, "open", read_only_open)
+ with pytest.raises(ValueError, match="read-only"):
+ audit_main()
diff --git a/code/fastfill/v2/tests/test_data_builder_fix20261006.py b/code/fastfill/v2/tests/test_data_builder_fix20261006.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ff1309aa15e6f5501d66207373749c957359b46
--- /dev/null
+++ b/code/fastfill/v2/tests/test_data_builder_fix20261006.py
@@ -0,0 +1,261 @@
+"""Audit fixes 2026-10-06, data-builder side: C1 schema, C4 axis yaw, C5 groups, C6 descriptions, C7 qualification."""
+from contextlib import redirect_stdout
+from copy import deepcopy
+from io import StringIO
+import json
+from unittest.mock import patch
+
+import pytest
+
+from fastfill.v2 import review_data as review
+from fastfill.v2.legacy_build import build_selected_dataset, main as legacy_main
+from fastfill.v2.multisource_data import ROOMGENBENCH_HOLDOUT_GROUPS, project_sample
+from fastfill.v2.multisource_data import build_dataset as build_minimal
+from fastfill.v2.multisource_verify import verify_dataset, verify_full_pair
+from fastfill.v2.qualified_data import build_dataset as build_qualified, main as qualified_main, qualify_sample
+from fastfill.v2.schema import migrate_legacy_row, validate_condition
+from fastfill.v2.legacy_bridge import convert_selected_room
+from fastfill.v2.tests.test_legacy_bridge_geometry import by_source, convert, obj, room, saved_row
+from fastfill.v2.tests.test_legacy_build import miniature_release, rows
+from fastfill.v2.tests.test_multisource_data import dataset_files, source_pair
+from fastfill.v2.tests.test_qualified_data import sample as qualified_parent
+
+
+# --- C1: schema -------------------------------------------------------------
+
+def legacy_row():
+ return {"schema_version": "fastfill.v2", "condition": {"schema_version": "fastfill.v2",
+ "room": {"frame": "right_handed_z_up", "floor_polygon_xy_m": [[0, 0], [4, 0], [4, 3], [0, 3]]},
+ "objects": [{"id": "a", "category": "chair", "description": "chair", "exchangeable_group": "g"},
+ {"id": "b", "category": "chair", "description": "chair", "exchangeable_group": "g"},
+ {"id": "c", "category": "desk", "description": "desk"}], "constraints": []},
+ "target": {"schema_version": "fastfill.v2", "objects": [{"id": "c"}, {"id": "b"}, {"id": "a"}]},
+ "validity": {"position": [[True] * 3] * 3, "size": [[True] * 3] * 3, "yaw": [False] * 3}}
+
+
+def test_condition_objects_reject_exchangeable_group():
+ with pytest.raises(ValueError, match="exchangeable_group"):
+ validate_condition(legacy_row()["condition"])
+
+
+def test_migrate_legacy_row_moves_groups_into_target_ordered_validity():
+ row = legacy_row()
+ before = deepcopy(row)
+ migrated = migrate_legacy_row(row)
+ assert row == before
+ assert migrated["validity"]["exchangeable_group"] == [None, "g", "g"]
+ assert all("exchangeable_group" not in o for o in migrated["condition"]["objects"])
+ validate_condition(migrated["condition"])
+ assert migrate_legacy_row(migrated) is migrated
+ row["validity"]["exchangeable_group"] = [None] * 3
+ with pytest.raises(ValueError, match="both"):
+ migrate_legacy_row(row)
+
+
+# --- C4/C5/C6: legacy bridge ----------------------------------------------
+
+def test_axis_default_admits_front_known_yaw_with_symmetry_order_by_source():
+ raw = room("SAGE-10k", [obj("chair", yaw=1.2), obj("tilted", tilted=True), obj("blind", front_known=False)])
+ result = convert_selected_room(raw, {**deepcopy(raw), "fixed": []}, saved_row(), "train") # default policy
+ byid = by_source(result)
+ assert byid["chair"][1]["yaw"] is True and byid["chair"][1]["yaw_symmetry_order"] == 2
+ assert byid["tilted"][1]["yaw"] is False and not any(byid["tilted"][1]["position"])
+ assert byid["blind"][1]["yaw"] is False
+ assert result["provenance"]["field_evidence"][0]["front_policy"] == "axis"
+ scan = convert(room("MultiScan", [obj("chair")], floor_z=-1., floor_height=-1., height_reliable=True,
+ n_floor_objects_outside_structure=0), policy="axis")
+ assert scan["validity"]["yaw"] == [True] and scan["validity"]["yaw_symmetry_order"] == [1]
+ assert convert(room("SAGE-10k", [obj("chair")]), policy="strict")["validity"]["yaw"] == [False]
+ with pytest.raises(ValueError, match="front policy"):
+ convert(room("SAGE-10k", [obj("chair")]), policy="axis-free")
+
+
+def test_groups_need_only_complete_position_and_live_in_validity():
+ twins = [obj("a", "chair", desc="A red chair", pos=[1., 1., 0.]), obj("b", "chair", desc="A red chair ", pos=[3., 1., 0.])]
+ result = convert(room("MansionWorld", twins + [obj("c", "chair", desc="A blue chair")]), policy="axis")
+ assert result["validity"]["size"] == [[False] * 3] * 3 # footprint proxy: no size supervision
+ byid = by_source(result)
+ assert byid["a"][1]["exchangeable_group"] == byid["b"][1]["exchangeable_group"]
+ assert byid["a"][1]["exchangeable_group"].startswith("anonymous_")
+ assert byid["c"][1]["exchangeable_group"] is None
+ assert all("exchangeable_group" not in o for o in result["condition"]["objects"])
+ tilted = convert(room("MansionWorld", [{**twins[0], "tilted": True}, twins[1]]), policy="axis")
+ assert tilted["validity"]["exchangeable_group"] == [None, None]
+
+
+def test_descriptions_come_from_source_desc_or_fall_back_to_category():
+ result = convert(room("SAGE-10k", [obj("a", "Table", desc=" A wooden conference table "), obj("b", "chair", desc=""),
+ obj("c", "lamp", description="A brass lamp"), obj("d", "sofa")]), policy="axis")
+ descriptions = {source: result["condition"]["objects"][i]["description"]
+ for i, source in enumerate(result["provenance"]["target_source_ids"])}
+ assert descriptions == {"a": "A wooden conference table", "b": "chair", "c": "A brass lamp", "d": "sofa"}
+ assert result["provenance"]["descriptions"] == "source_desc_or_category"
+
+
+def test_build_manifest_records_yaw_policy_symmetry_and_group_counts(tmp_path):
+ release, evidence = miniature_release(tmp_path)
+ with redirect_stdout(StringIO()):
+ result = build_selected_dataset(release, tmp_path / "dataset", evidence_root=evidence)
+ assert result["front_policy"] == "axis"
+ assert "axis" in result["yaw_policy"]
+ assert result["source_yaw_valid_objects"] == {"SpatialLM": 6}
+ assert result["source_yaw_symmetry_order_counts"] == {"SpatialLM:2": 6}
+ assert result["exchangeable_group_counts"] == {"exchangeable_groups": 3, "exchangeable_members": 6} # two identical chairs per room
+ assert result["valid_label_counts"]["yaw"] == 6
+ for split in ("train", "validation", "test"):
+ for row in rows(tmp_path / "dataset" / f"{split}.jsonl"):
+ assert row["validity"]["yaw_symmetry_order"] == [2, 2]
+ assert row["validity"]["exchangeable_group"] == ["anonymous_0"] * 2
+ with redirect_stdout(StringIO()):
+ legacy_main(["--release-root", str(release), "--evidence-root", str(evidence),
+ "--output", str(tmp_path / "strict"), "--front-policy", "strict"])
+ assert json.loads((tmp_path / "strict/manifest.json").read_text())["valid_label_counts"].get("yaw", 0) == 0
+
+
+# --- review: facing trust needs a semantic front -------------------------
+
+def test_axis_only_yaw_does_not_certify_source_derived_facing():
+ parent = {"schema_version": "fastfill.v2", "condition": {"schema_version": "fastfill.v2",
+ "room": {"frame": "right_handed_z_up"}, "objects": [{"id": "chair", "category": "chair"}, {"id": "table", "category": "table"}],
+ "constraints": [{"type": "faces", "object_id": "chair", "target_id": "table"}]},
+ "target": {"schema_version": "fastfill.v2", "objects": [
+ {"id": "chair", "target_size_local_m": [.5, .6, .8], "bottom_center_m": [1., 1., 0.], "yaw_rad": .2},
+ {"id": "table", "target_size_local_m": [1., 1., .7], "bottom_center_m": [2., 1., 0.], "yaw_rad": 0.}]},
+ "validity": {"position": [[True] * 3] * 2, "size": [[True] * 3] * 2, "yaw": [True, True], "yaw_symmetry_order": [2, 2]},
+ "provenance": {"scene_id": "s", "legacy_uid": "s", "source": "SAGE-10k", "split": "train",
+ "constraint_source": "frozen_sparse_legacy_request",
+ "field_evidence": [{"recorded_source_evidence": {}}, {"recorded_source_evidence": {}}]}}
+ assert review.review_sample(parent)["condition"]["constraints"] == []
+ parent["validity"]["yaw_symmetry_order"] = [1, 2]
+ assert review.review_sample(parent)["condition"]["constraints"] == parent["condition"]["constraints"]
+
+
+# --- C7: qualification ------------------------------------------------------
+
+def test_degenerate_axis_masks_whole_size_with_its_own_reason():
+ parent = qualified_parent()
+ parent["target"]["objects"][0]["target_size_local_m"] = [1.4, .0029, .75]
+ row = qualify_sample(parent)
+ assert row["validity"]["size"] == [[False] * 3]
+ assert row["target"] == parent["target"]
+ change, = [c for c in row["provenance"]["qualification_changes"] if c["field"] == "size"]
+ assert change["reason"] == "degenerate_axis_lt_3mm" and change["value_m"] == [1.4, .0029, .75]
+ parent["target"]["objects"][0]["target_size_local_m"] = [1.4, .003, .75]
+ assert qualify_sample(parent)["validity"]["size"] == [[True] * 3]
+
+
+def test_target_above_declared_height_is_flagged_never_dropped():
+ parent = qualified_parent()
+ parent["target"]["objects"][0]["target_size_local_m"][2] = 2.84 # bottom 0 + 2.84 <= 2.8 + 0.05
+ assert "height_conflict" not in qualify_sample(parent)["provenance"]
+ parent["target"]["objects"][0]["target_size_local_m"][2] = 2.86
+ row = qualify_sample(parent)
+ assert row["condition"] == parent["condition"] # K2: height_m stays 2.8
+ assert row["target"] == parent["target"] and row["validity"]["size"] == [[True] * 3]
+ conflict = {"objects": ["desk"], "max_excess_m": pytest.approx(.06)}
+ assert row["provenance"]["height_conflict"] == conflict
+ assert {"field": "provenance.height_conflict", "before": None, "after": conflict,
+ "reason": "target_exceeds_declared_height_flag_only"} in row["provenance"]["qualification_changes"]
+ assert verify_full_pair(parent, row, "train")["objects"] == 1
+ dropped = deepcopy(row)
+ dropped["condition"]["room"]["height_m"] = None # the round-1 behaviour is now a verification failure
+ with pytest.raises(ValueError, match="condition"):
+ verify_full_pair(parent, dropped, "train")
+ parent["validity"]["size"] = [[False] * 3] # an unverified size never flags the room height
+ assert "height_conflict" not in qualify_sample(parent)["provenance"]
+
+
+def test_roomgenbench_rooms_leave_train_for_test_with_reason():
+ parent = qualified_parent("SAGE-10k")
+ parent["provenance"]["group"] = parent["provenance"]["house_id"] = "sage:layout_61ebde9f"
+ row = qualify_sample(parent, holdout_groups=ROOMGENBENCH_HOLDOUT_GROUPS)
+ assert row["provenance"]["split"] == "test"
+ assert row["provenance"]["holdout_reason"] == "roomgenbench_benchmark_room"
+ assert row["provenance"]["qualification_changes"][-1] == {
+ "field": "provenance.split", "before": "train", "after": "test", "reason": "roomgenbench_benchmark_room"}
+ assert verify_full_pair(parent, row, "train")["objects"] == 1
+ with pytest.raises(ValueError, match="provenance split"):
+ verify_full_pair(parent, row, "train", holdout_groups=())
+ assert qualify_sample(parent)["provenance"]["split"] == "train" # not listed: unchanged
+ sample, raw = source_pair("SAGE-10k")
+ sample["provenance"]["group"] = raw["group"] = sample["provenance"]["house_id"] = "sage:layout_6b049b06"
+ minimal = project_sample(sample, raw, split="train", holdout_groups=ROOMGENBENCH_HOLDOUT_GROUPS)
+ assert minimal["provenance"]["split"] == "test" and minimal["provenance"]["qualification_changes"][-1]["field"] == "provenance.split"
+
+
+def test_qualified_build_writes_holdout_to_test_and_verifier_accepts_it(tmp_path):
+ from fastfill.v2.io import fingerprint
+ parent = tmp_path / "parent"
+ parent.mkdir()
+ for split in ("train", "validation", "test"):
+ first = qualified_parent(split=split)
+ second = qualified_parent("SAGE-10k", split)
+ second["provenance"].update(scene_id="bench-" + split, group="sage:layout_fef15043" if split == "train" else "other-" + split,
+ house_id="sage:layout_fef15043" if split == "train" else "other-" + split)
+ (parent / (split + ".jsonl")).write_text("".join(json.dumps(r) + "\n" for r in (first, second)))
+ (parent / "manifest.json").write_text('{"schema_version":"fastfill.v2","front_policy":"axis","yaw_policy":"axis policy"}\n')
+ (parent / "rejections.jsonl").write_text("")
+ pins = {p.name: fingerprint(p) for p in parent.iterdir()}
+ with redirect_stdout(StringIO()):
+ manifest = build_qualified(parent, tmp_path / "new", expected_hashes=pins)
+ assert manifest["split_samples"] == {"train": 1, "validation": 2, "test": 3}
+ assert manifest["holdout_samples_by_parent_split"] == {"train": 1, "validation": 0, "test": 0}
+ assert manifest["roomgenbench_holdout_groups"] == sorted(ROOMGENBENCH_HOLDOUT_GROUPS)
+ assert manifest["qualification_change_counts"]["provenance.split"] == 1
+ assert manifest["parent_front_policy"] == "axis" and manifest["parent_yaw_policy"] == "axis policy"
+ assert manifest["yaw_policy"].startswith("inherit parent yaw validity")
+ moved = rows(tmp_path / "new/test.jsonl")[-1]
+ assert moved["provenance"]["scene_id"] == "bench-train" and moved["provenance"]["split"] == "test"
+ journal = rows(tmp_path / "new/changes.jsonl")
+ assert journal[-1]["split"] == "train" and journal[-1]["parent_line"] == 2 and journal[-1]["field"] == "provenance.split"
+ report = verify_dataset(parent, tmp_path / "new", tmp_path, expected_ir_sha256={}, full_condition=True)
+ assert report["ok"], report["errors_first_100"]
+ assert report["split_counts"]["test"]["samples"] == 3 and report["journal_entries_checked"] == 1
+ with pytest.raises(ValueError, match="holdout"):
+ verify_dataset(parent, tmp_path / "new", tmp_path, expected_ir_sha256={}, full_condition=True, holdout_groups=())
+ stub = {"split_samples": {}, "qualification_change_counts": {}}
+ with patch("fastfill.v2.qualified_data.build_dataset", return_value=stub) as build, redirect_stdout(StringIO()):
+ qualified_main(["--parent-root", "p", "--output", "o", "--ir-root", "i", "--frozen-manifest", "m",
+ "--holdout-group", "other-validation", "--holdout-group", "x"])
+ qualified_main(["--parent-root", "p", "--output", "o", "--ir-root", "i", "--frozen-manifest", "m"])
+ assert build.call_args_list[0].kwargs["holdout_groups"] == ("other-validation", "x")
+ assert build.call_args_list[1].kwargs["holdout_groups"] == ROOMGENBENCH_HOLDOUT_GROUPS
+
+
+def test_minimal_view_build_and_independent_verifier_round_trip_with_holdout(tmp_path):
+ from fastfill.v2.io import fingerprint
+ parent, ir = dataset_files(tmp_path)
+ row = json.loads((parent / "validation.jsonl").read_text())
+ kwargs = {"expected_spatiallm_sha256": fingerprint(ir / "SpatialLM.jsonl"), "frozen_manifest": tmp_path / "frozen-manifest.json",
+ "holdout_groups": (row["provenance"]["group"],)}
+ manifest = build_minimal(parent, ir, tmp_path / "view", **kwargs)
+ assert manifest["split_samples"] == {"train": 1, "test": 2}
+ assert manifest["roomgenbench_holdout_groups"] == [row["provenance"]["group"]]
+ seals = json.loads((tmp_path / "frozen-manifest.json").read_text())["ir_sha256"]
+ report = verify_dataset(parent, tmp_path / "view", ir, expected_ir_sha256=seals, holdout_groups=kwargs["holdout_groups"])
+ assert report["ok"], report["errors_first_100"]
+ assert report["split_counts"]["test"]["samples"] == 2
+
+
+def test_full_pipeline_bridge_review_qualify_verify(tmp_path):
+ """New-format rows flow through every builder and the independent verifier end to end."""
+ from fastfill.v2.io import fingerprint
+ release, evidence = miniature_release(tmp_path)
+ with redirect_stdout(StringIO()):
+ build_selected_dataset(release, tmp_path / "bridge", evidence_root=evidence)
+ review.build_reviewed(tmp_path / "bridge", tmp_path / "reviewed")
+ reviewed = json.loads((tmp_path / "reviewed/manifest.json").read_text())
+ assert reviewed["front_policy"] == "axis" and "axis" in reviewed["yaw_policy"]
+ pins = {p.name: fingerprint(p) for p in (tmp_path / "reviewed").iterdir()}
+ holdout = ("house:train-clean",)
+ manifest = build_qualified(tmp_path / "reviewed", tmp_path / "main", expected_hashes=pins, holdout_groups=holdout)
+ assert manifest["split_samples"] == {"validation": 1, "test": 2}
+ assert manifest["source_yaw_symmetry_order_counts"] == {"SpatialLM:2": 6}
+ report = verify_dataset(tmp_path / "reviewed", tmp_path / "main", tmp_path, expected_ir_sha256={}, full_condition=True,
+ holdout_groups=holdout)
+ assert report["ok"], report["errors_first_100"]
+ for split in ("validation", "test"):
+ for row in rows(tmp_path / "main" / f"{split}.jsonl"):
+ validate_condition(row["condition"])
+ assert row["validity"]["yaw_symmetry_order"] == [2, 2]
+ assert "exchangeable_group" in row["validity"]
diff --git a/code/fastfill/v2/tests/test_data_policy_r2.py b/code/fastfill/v2/tests/test_data_policy_r2.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ca0a588c629ddf43afe4cc16cb19b8505ed0190
--- /dev/null
+++ b/code/fastfill/v2/tests/test_data_policy_r2.py
@@ -0,0 +1,222 @@
+"""Round-2 data contract: K1 box-symmetry tier, K2 height-conflict flag, K3 source floor declarations."""
+from contextlib import redirect_stdout
+from copy import deepcopy
+from io import StringIO
+import json
+from unittest.mock import patch
+
+import pytest
+
+from fastfill.v2 import review_data as review
+from fastfill.v2.io import fingerprint
+from fastfill.v2.legacy_build import build_selected_dataset
+from fastfill.v2.legacy_verify import verify_selected_dataset
+from fastfill.v2.multisource_data import project_sample
+from fastfill.v2.multisource_verify import verify_dataset, verify_full_pair, verify_pair
+from fastfill.v2.qualified_data import build_dataset as build_qualified, qualify_sample
+from fastfill.v2.schema import migrate_legacy_row
+from fastfill.v2.tests import test_legacy_bridge
+from fastfill.v2.tests.test_legacy_bridge_geometry import by_source, convert, obj, room
+from fastfill.v2.tests.test_legacy_build import miniature_release, rows
+from fastfill.v2.tests.test_minimal_data import pair as minimal_pair
+from fastfill.v2.tests.test_qualified_data import sample as qualified_parent
+
+
+def multiscan(objects):
+ return room("MultiScan", objects, floor_z=-1., floor_height=-1., height_reliable=True, n_floor_objects_outside_structure=0)
+
+
+# --- K1: box-symmetry tier --------------------------------------------------
+
+@pytest.mark.parametrize("raw,ident,swap,order", [
+ (room("InternScenes_arkit", [obj("a", "table")]), "a", True, 4),
+ (room("InternScenes_scannet", [obj("a", "lamp")]), "a", True, 4),
+ (room("InteriorGS", [obj("a", "sofa")]), "a", True, 4),
+ (room("HSSD200", [obj("a", "semi_chair"), obj("b", "table")]), "a", True, 4),
+ (room("HSSD200", [obj("a", "semi_chair"), obj("b", "table")]), "b", False, 2),
+ (multiscan([obj("a", "bed"), obj("b", "table")]), "a", True, 4),
+ (multiscan([obj("a", "bed"), obj("b", "table")]), "b", False, 1),
+ (multiscan([obj("a", "bed net"), obj("b", "table")]), "a", False, 1),
+ (room("SAGE-10k", [obj("a", "chair")]), "a", False, 2),
+ (room("InternScenes_gen", [obj("a", "chair")]), "a", False, 2),
+])
+def test_axis_policy_marks_swap_tier_with_order_four(raw, ident, swap, order):
+ _, validity = by_source(convert(raw, policy="axis"))[ident]
+ assert validity["size_axis_swap_allowed"] is swap and validity["yaw_symmetry_order"] == order
+
+
+def test_swap_tier_is_axis_policy_only_and_old_rows_migrate_to_false():
+ strict = convert(room("InternScenes_arkit", [obj("a", "table")]), policy="strict")
+ assert strict["validity"]["size_axis_swap_allowed"] == [False] and strict["validity"]["yaw_symmetry_order"] == [1]
+ old = deepcopy(strict)
+ del old["validity"]["size_axis_swap_allowed"]
+ migrated = migrate_legacy_row(old)
+ assert migrated["validity"]["size_axis_swap_allowed"] == [False] and "size_axis_swap_allowed" not in old["validity"]
+ assert migrate_legacy_row(migrated) is migrated
+
+
+# --- K3: floor support from source annotation only --------------------------
+
+def test_source_floor_anchor_declares_and_snaps_within_two_cm_only():
+ raw = room("IL3D_3dfront", [obj("exact", anchor="floor"), obj("near", anchor="floor", pos=[2., 1., .015]),
+ obj("float", anchor="floor", pos=[3., 1., .05]),
+ obj("guess", anchor="floor", anchor_inferred=True, pos=[4., 1., .01]),
+ obj("none", pos=[4., 3., 0.])])
+ result = convert(raw, policy="axis")
+ requests = {source: result["condition"]["objects"][i] for i, source in enumerate(result["provenance"]["target_source_ids"])}
+ evidence = {source: result["provenance"]["field_evidence"][i] for i, source in enumerate(result["provenance"]["target_source_ids"])}
+ targets = {source: target for source, (target, _) in by_source(result).items()}
+ assert {k: v.get("support_parent") for k, v in requests.items()} == {
+ "exact": "floor", "near": "floor", "float": None, "guess": None, "none": None}
+ assert targets["near"]["bottom_center_m"] == [2., 1., 0.] and evidence["near"]["legacy_z_snap_applied_to_target"] is True
+ assert targets["exact"]["bottom_center_m"][2] == 0. and evidence["exact"]["legacy_z_snap_applied_to_target"] is False
+ assert targets["float"]["bottom_center_m"][2] == .05 and evidence["float"]["legacy_z_snap_applied_to_target"] is False
+ assert raw["objects"][1]["pos"][2] == .015 # source IR is never rewritten
+ unknown_floor = convert(multiscan([obj("a", anchor="floor")]), policy="axis")
+ assert "support_parent" not in unknown_floor["condition"]["objects"][0]
+
+
+def snapping_release(tmp_path):
+ """Miniature release whose scenes hold one snapped (1.5 cm) and one floating (5 cm) floor anchor."""
+ def fixture():
+ raw = original()
+ raw["objects"][0]["pos"] = [4, 3, .05]
+ raw["objects"][1]["pos"] = [1, 1, .015]
+ return raw
+ original = test_legacy_bridge.fixture
+ with patch("fastfill.v2.tests.test_legacy_build.fixture", fixture):
+ return miniature_release(tmp_path)
+
+
+def build(release, evidence, output, **kwargs):
+ with redirect_stdout(StringIO()):
+ return build_selected_dataset(release, output, evidence_root=evidence, **kwargs)
+
+
+def assert_failed(report, text):
+ assert not report["passed"] and any(text in e["message"] for e in report["errors"]), report["errors"]
+
+
+def mutate(path, change):
+ records = rows(path)
+ change(records[0])
+ path.write_text("".join(json.dumps(r) + "\n" for r in records))
+
+
+def test_bridge_manifest_counts_floor_declarations_and_verifier_recomputes_them(tmp_path):
+ release, evidence = snapping_release(tmp_path)
+ manifest = build(release, evidence, tmp_path / "dataset")
+ assert (manifest["floor_declarations_written"], manifest["floor_declaration_z_snapped"],
+ manifest["floor_declaration_skipped_floating"]) == (5, 3, 1) # flagged scenes move object 0 to z=0
+ assert manifest["source_size_axis_swap_allowed_objects"] == {}
+ report = verify_selected_dataset(tmp_path / "dataset")
+ assert report["passed"], report["errors"]
+ for row in rows(tmp_path / "dataset/train.jsonl"):
+ assert sorted(t["bottom_center_m"][2] for t in row["target"]["objects"]) == [0, .05]
+
+ def undeclare(row):
+ for request in row["condition"]["objects"]:
+ request.pop("support_parent", None)
+ for name, change, message in (
+ ("undeclared", undeclare, "snapped to the floor without a floor declaration"),
+ ("floating", lambda row: [r.update(support_parent="floor") for r in row["condition"]["objects"]],
+ "floor support"), # caught by the z-consistency check before the anchor check
+ ("anchorless", lambda row: [e.update(raw_anchor=None) for e in row["provenance"]["field_evidence"]],
+ "floor support declared without a source floor anchor"),
+ ("swap", lambda row: row["validity"].update(size_axis_swap_allowed=[True, True], yaw_symmetry_order=[4, 4]),
+ "size_axis_swap_allowed differs")):
+ release, evidence = snapping_release(tmp_path / name)
+ build(release, evidence, tmp_path / name / "dataset")
+ mutate(tmp_path / name / "dataset/train.jsonl", change)
+ assert_failed(verify_selected_dataset(tmp_path / name / "dataset"), message)
+
+
+def test_unsnapped_floor_anchor_within_tolerance_is_a_verifier_failure(tmp_path):
+ release, evidence = snapping_release(tmp_path)
+ build(release, evidence, tmp_path / "dataset")
+
+ def unsnap(row):
+ for request, target, item in zip(row["condition"]["objects"], row["target"]["objects"], row["provenance"]["field_evidence"]):
+ if item["legacy_z_snap_applied_to_target"]:
+ request.pop("support_parent")
+ target["bottom_center_m"][2], item["legacy_z_snap_applied_to_target"] = .015, False
+ mutate(tmp_path / "dataset/train.jsonl", unsnap)
+ assert_failed(verify_selected_dataset(tmp_path / "dataset"), "within the snap tolerance was not declared")
+
+
+def test_bounded_smoke_build_caps_each_source_exactly(tmp_path):
+ release, evidence = miniature_release(tmp_path)
+ manifest = build(release, evidence, tmp_path / "dataset", sources=["SpatialLM"], max_scenes_per_source=2)
+ assert manifest["samples_written"] == 2 and manifest["bounded_build"] is True
+ assert manifest["source_filter"] == ["SpatialLM"] and manifest["max_scenes_per_source"] == 2
+ assert verify_selected_dataset(tmp_path / "dataset")["passed"]
+ with pytest.raises(ValueError, match="unknown source filter"):
+ build(release, evidence, tmp_path / "other", sources=["NoSuchSource"])
+
+
+def test_snapped_rows_flow_through_review_qualify_and_full_verifier(tmp_path):
+ release, evidence = snapping_release(tmp_path)
+ build(release, evidence, tmp_path / "bridge")
+ with redirect_stdout(StringIO()):
+ review.build_reviewed(tmp_path / "bridge", tmp_path / "reviewed")
+ pins = {p.name: fingerprint(p) for p in (tmp_path / "reviewed").iterdir()}
+ manifest = build_qualified(tmp_path / "reviewed", tmp_path / "main", expected_hashes=pins, holdout_groups=())
+ assert manifest["source_size_axis_swap_allowed_objects"] == {"SpatialLM": 0}
+ report = verify_dataset(tmp_path / "reviewed", tmp_path / "main", tmp_path, expected_ir_sha256={},
+ full_condition=True, holdout_groups=())
+ assert report["ok"], report["errors_first_100"]
+
+
+# --- K2: height conflict is a flag only -------------------------------------
+
+def test_qualified_build_flags_height_conflict_in_journal_and_verifier_mirrors_it(tmp_path):
+ parent = tmp_path / "parent"
+ parent.mkdir()
+ for split in ("train", "validation", "test"):
+ row = qualified_parent(split=split)
+ row["provenance"]["field_evidence"] = [{"tilted": False}]
+ row["target"]["objects"][0]["target_size_local_m"][2] = 2.9 if split == "train" else .75
+ (parent / (split + ".jsonl")).write_text(json.dumps(row) + "\n")
+ (parent / "manifest.json").write_text('{"schema_version":"fastfill.v2"}\n')
+ (parent / "rejections.jsonl").write_text("")
+ pins = {p.name: fingerprint(p) for p in parent.iterdir()}
+ with redirect_stdout(StringIO()):
+ manifest = build_qualified(parent, tmp_path / "main", expected_hashes=pins, holdout_groups=())
+ assert manifest["qualification_change_counts"] == {"provenance.height_conflict": 1}
+ flagged = rows(tmp_path / "main/train.jsonl")[0]
+ assert flagged["condition"]["room"]["height_m"] == 2.8
+ assert flagged["provenance"]["height_conflict"]["objects"] == ["desk"]
+ journal, = rows(tmp_path / "main/changes.jsonl")
+ assert journal["field"] == "provenance.height_conflict" and journal["reason"] == "target_exceeds_declared_height_flag_only"
+ report = verify_dataset(parent, tmp_path / "main", tmp_path, expected_ir_sha256={}, full_condition=True, holdout_groups=())
+ assert report["ok"], report["errors_first_100"]
+ row = json.loads((parent / "train.jsonl").read_text())
+ derived = qualify_sample(row)
+ derived["provenance"]["height_conflict"]["max_excess_m"] += 1e-3
+ with pytest.raises(ValueError, match="height_conflict"):
+ verify_full_pair(row, derived, "train", holdout_groups=())
+
+
+# --- minimal view: snapped targets and the swap tier ------------------------
+
+def test_minimal_view_accepts_bridge_snap_and_keeps_swap_order_four():
+ sample, source = minimal_pair(floor=0.)
+ sample["provenance"]["source"] = source["source"] = "IL3D_3dfront" # a source with floor anchors
+ sample["target"]["objects"][0]["bottom_center_m"][2] = 0.
+ source["objects"][0]["pos"][2] = .015
+ sample["provenance"]["field_evidence"][0]["legacy_z_snap_applied_to_target"] = True
+ sample["validity"].update(size_axis_swap_allowed=[True], yaw_symmetry_order=[4])
+ result = project_sample(sample, source, split="train")
+ assert result["target"]["objects"][0]["bottom_center_m"][2] == 0.
+ assert result["validity"]["yaw_symmetry_order"] == [4] and result["validity"]["size_axis_swap_allowed"] == [True]
+ assert verify_pair(sample, result, "train", source)["objects"] == 1
+ far = deepcopy(source)
+ far["objects"][0]["pos"][2] = .03
+ with pytest.raises(ValueError, match="snap"):
+ project_sample(sample, far, split="train")
+ with pytest.raises(ValueError, match="snap"):
+ verify_pair(sample, result, "train", far)
+ unflagged = deepcopy(sample)
+ unflagged["provenance"]["field_evidence"][0]["legacy_z_snap_applied_to_target"] = False
+ with pytest.raises(ValueError, match="source_geometry"):
+ project_sample(unflagged, source, split="train")
diff --git a/code/fastfill/v2/tests/test_direct_layout.py b/code/fastfill/v2/tests/test_direct_layout.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d29bd961508895dbff970d27b6caa82f64b3c14
--- /dev/null
+++ b/code/fastfill/v2/tests/test_direct_layout.py
@@ -0,0 +1,469 @@
+"""Direct room/list -> bbox handoff, without requiring an asset resolver.
+
+GLB tests decode the published binary convention independently of the exporter.
+They intentionally do not use trimesh or treat a proxy box as a furniture mesh.
+"""
+from copy import deepcopy
+import hashlib
+import itertools
+import json
+import math
+from pathlib import Path
+import struct
+import xml.etree.ElementTree as ET
+
+import pytest
+
+from fastfill.v2.direct_layout import (
+ export_handoff,
+ layout_to_roomgenbench,
+ layout_to_scene,
+ request_to_condition,
+ write_bbox_glb,
+)
+from fastfill.v2.schema import validate_condition
+
+
+def request():
+ return {"room_type": "living_room", "room_size_m": [5., 4., 2.8],
+ "furniture_list": ["sofa", {"category": "chair", "count": 2},
+ {"id": "lamp", "category": "lamp", "description": "reading lamp"}]}
+
+
+def layout(condition, *, yaw=0.):
+ return {"schema_version": "fastfill.v2", "objects": [
+ {"id": obj["id"], "target_size_local_m": [1.4, .7, .5 + index],
+ "bottom_center_m": [2., 1.5, .2 * index], "yaw_rad": yaw}
+ for index, obj in enumerate(condition["objects"])]}
+
+
+def corners(position, size, yaw):
+ """Independent full-extent, bottom-center OBB reference."""
+ c, s = math.cos(yaw), math.sin(yaw)
+ w, d, h = size
+ return [[position[0] + c * x - s * y,
+ position[1] + s * x + c * y, position[2] + z]
+ for x, y, z in itertools.product((-w / 2, w / 2), (-d / 2, d / 2), (0, h))]
+
+
+def corner_set(values):
+ return sorted(tuple(round(float(coordinate), 6) for coordinate in point) for point in values)
+
+
+def read_glb(path):
+ data = Path(path).read_bytes()
+ assert len(data) >= 28
+ magic, version, length = struct.unpack_from(" .02)
+ assert all(obj["support_status"] != "declared" for obj in downstream["objects"])
+
+
+@pytest.mark.parametrize("yaw", [0., math.pi / 2, math.radians(97), -math.pi])
+def test_glb_is_self_contained_and_applies_full_extent_bottom_center_z_up_to_y_up(tmp_path, yaw):
+ condition = request_to_condition(request())
+ prediction = layout(condition, yaw=yaw)
+ scene = layout_to_scene(condition, prediction)
+ output = tmp_path / "layout.glb"
+ write_bbox_glb(output, scene)
+ document, binary = read_glb(output)
+ assert not document.get("images")
+ assert not document.get("textures")
+ for obj in prediction["objects"]:
+ actual = node_vertices(document, binary, obj["id"])
+ expected = [[x, z, -y] for x, y, z in corners(obj["bottom_center_m"], obj["target_size_local_m"], yaw)]
+ # Vertices may be duplicated for flat face normals; compare unique corners.
+ assert sorted(set(corner_set(actual))) == sorted(set(corner_set(expected)))
+ saved = output.read_bytes()
+ with pytest.raises(FileExistsError):
+ write_bbox_glb(output, scene)
+ assert output.read_bytes() == saved
+
+
+def test_handoff_exports_without_catalog_or_support_validation_and_preserves_raw_prediction(tmp_path):
+ condition = request_to_condition(request())
+ prediction = layout(condition)
+ # Deliberately outside the room: diagnostics may reject it, but never shrink/delete it.
+ prediction["objects"][0]["bottom_center_m"] = [-4., 1., .4]
+ saved_condition, saved_prediction = deepcopy(condition), deepcopy(prediction)
+ directory = tmp_path / "handoff"
+ export_handoff(directory, condition, prediction)
+ assert {"scene.json", "roomgenbench_scene.json", "layout.glb", "preview.svg", "diagnostics.json"} <= {p.name for p in directory.iterdir()}
+ scene = json.loads((directory / "scene.json").read_text())
+ assert scene == layout_to_scene(condition, prediction)
+ assert scene["objects"][0]["bottom_center_m"] == [-4., 1., .4]
+ assert len(scene["objects"]) == len(prediction["objects"])
+ json.loads((directory / "diagnostics.json").read_text())
+ assert ET.parse(directory / "preview.svg").getroot().tag == "{http://www.w3.org/2000/svg}svg"
+ read_glb(directory / "layout.glb")
+ assert condition == saved_condition and prediction == saved_prediction
+ snapshots = {p.name: p.read_bytes() for p in directory.iterdir()}
+ with pytest.raises(FileExistsError):
+ export_handoff(directory, condition, prediction)
+ assert {p.name: p.read_bytes() for p in directory.iterdir()} == snapshots
+
+
+def test_invalid_handoff_is_rejected_before_any_directory_is_created(tmp_path):
+ condition = request_to_condition(request())
+ prediction = layout(condition)
+ prediction["objects"][0]["target_size_local_m"] = [0., 1., 1.]
+ directory = tmp_path / "invalid"
+ with pytest.raises(ValueError):
+ export_handoff(directory, condition, prediction)
+ assert not directory.exists()
+
+
+def test_source_data_paths_are_protected_for_direct_exports():
+ condition = request_to_condition(request())
+ prediction = layout(condition)
+ with pytest.raises(ValueError):
+ export_handoff(Path("/Volumes/harddisk/3D_Room_Collections/new-handoff"), condition, prediction)
+ with pytest.raises(ValueError):
+ write_bbox_glb(Path("/Volumes/harddisk/3D_Room_Collections/new-layout.glb"), layout_to_scene(condition, prediction))
+
+
+def test_prediction_cli_accepts_minimal_request_and_direct_handoff_without_assets(tmp_path):
+ from fastfill.v2.batch import TinyTokenizer
+ from fastfill.v2.model import ModelConfig, build_model
+ from fastfill.v2.predict import main
+ checkpoint = tmp_path / "model"
+ build_model(ModelConfig(backbone="tiny", lora_rank=0, decoder_dim=16,
+ decoder_heads=2, decoder_layers=1, tiny_hidden_size=16)).save_pretrained(checkpoint)
+ TinyTokenizer().save_pretrained(tmp_path / "tokenizer")
+ request_path = tmp_path / "request.json"
+ request_path.write_text(json.dumps(request()))
+ output = tmp_path / "prediction.json"
+ handoff = tmp_path / "handoff"
+ main(["--checkpoint", str(checkpoint), "--request", str(request_path), "--output", str(output),
+ "--export-dir", str(handoff), "--device", "cpu"])
+ prediction = json.loads(output.read_text())
+ assert {obj["id"] for obj in prediction["objects"]} == {"obj_0000", "obj_0001", "obj_0002", "lamp"}
+ scene = json.loads((handoff / "scene.json").read_text())
+ assert scene == layout_to_scene(request_to_condition(request()), prediction)
+ read_glb(handoff / "layout.glb")
+
+
+def test_svg_escapes_markup_and_filters_xml_controls_without_changing_json_semantics(tmp_path):
+ source = {"room_type": "living_room\u0001 ",
+ "category": "chair\u0001 <>&", "description": "oak <>& chair"}]}
+ condition = request_to_condition(source)
+ prediction = layout(condition)
+ handoff = tmp_path / "xml-safe"
+ export_handoff(handoff, condition, prediction)
+ scene = json.loads((handoff / "scene.json").read_text())
+ assert scene["room"]["room_type"] == source["room_type"]
+ assert scene["objects"][0]["id"] == source["furniture_list"][0]["id"]
+ assert scene["objects"][0]["category"] == source["furniture_list"][0]["category"]
+ assert scene["objects"][0]["description"] == source["furniture_list"][0]["description"]
+ svg = (handoff / "preview.svg").read_text()
+ tree = ET.fromstring(svg)
+ assert "\u0001" not in svg
+ assert {element.tag.rsplit("}", 1)[-1] for element in tree.iter()} <= {"svg", "rect", "text", "polygon", "title"}
+ text = " ".join(element.text or "" for element in tree.iter())
+ assert "