diff --git a/.gitignore b/.gitignore index 68972f9..cc5aa39 100644 --- a/.gitignore +++ b/.gitignore @@ -10,7 +10,26 @@ benchmark/*/heatmap_cache/ .env view_dump/ .model_cache/ -analysis_out/ +# Ignore analysis_out/ CONTENTS rather than the directory itself: git cannot re-include a +# file whose parent directory is excluded, and one thing in here must be tracked. +analysis_out/* +# The low-floor detection caches (issue #54) are the exception. They are image-free and a +# few hundred KB total, and they are what makes every number in docs/operating_point.md +# re-derivable on CPU -- without a GPU and without the gitignored panos. Same reasoning as +# committing benchmark/*/records.jsonl. Galleries, PNGs and regenerable CSVs stay ignored. +!analysis_out/op_cache/ +analysis_out/op_cache/* +!analysis_out/op_cache/*.json +# The derived result tables too (~80 KB): these are the exact numbers quoted in +# docs/operating_point.md, so committing them means a reader can check a figure in the +# prose against the table it came from without re-running anything. The gallery crops +# under analysis_out/op/*_incremental_fp/ stay ignored -- 181 MB of PNGs that regenerate +# from the caches plus the (also gitignored) panos, and whose irreplaceable part, the +# human A/B tags, is committed under benchmark//incremental_fp_tags.json. +!analysis_out/op/ +analysis_out/op/* +!analysis_out/op/*.csv +!analysis_out/op/*.json # Claude Code local state. settings.local.json is per-machine (absolute paths, # session ids) and worktrees/ holds full repo checkouts -- gigabytes. 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0.12181217968463898]], "gt": {"gt_points": [], "ignore_points": [], "fn_confirmed": true}}, {"pano": "896345989301021", "preds": [], "gt": {"gt_points": [], "ignore_points": [], "fn_confirmed": true}}]} \ No newline at end of file diff --git a/benchmark/README.md b/benchmark/README.md index 30747e0..2c7e1c3 100644 --- a/benchmark/README.md +++ b/benchmark/README.md @@ -45,6 +45,11 @@ where both questions land on the same panos — neither repo "owns" a city. - *"How good is the model, and is it improving?"* → **RampNet**. Ground truth, scoring, and the benchmark itself. +**To add a city, follow [`docs/adding_a_benchmark_city.md`](../docs/adding_a_benchmark_city.md)** — +the full protocol with a checklist. The table below is the two-repo summary; the protocol covers +what the table does not, which is everything a new split invalidates downstream (pooled numbers, +per-tier curves, the #55 correction, both figures, four documents). + Per-step, for adding a city to this benchmark: | Step | Repo | Tool | @@ -131,8 +136,9 @@ sits below confidence 0.75, so precision is a clean **1.000** at that threshold recall 0.730 → 0.524). Its imagery is the newest and most uniform in the benchmark: 2024-era Mapillary 360 shot entirely on a **GoPro Max** by a single contributor across 80 sequences, uniformly 4096×2048 (3 panos at 5760×2880), median Mapillary quality score 0.882 — -legible enough that the reviewer abstained on only 4.3% of detections and 21.7% of missed marks, -the lowest abstention rate of any split. Recall is the middling part of the story: 0.730 all-125 / +legible enough that the reviewer abstained on only 4.3% of detections and 21.7% of missed marks — +the lowest missed-mark abstention of any split (on detections, bend's 1.9% and annapolis's 2.2% +are lower). Recall is the middling part of the story: 0.730 all-125 / 0.684 unbiased, between clovis and bend. Worth noting for the negative-sample check: all 25 `empty`-group panos held **no detections and no missed ramps**, i.e. the model's "nothing here" was correct on every one. @@ -150,8 +156,8 @@ detection**, against morgantown's 11.2%. question, and precision 0.964 settles it: only 8 of 222 judged detections were wrong. Annapolis is a compact colonial grid plus the Naval Academy; it genuinely has more curb ramps per pano. -The imagery is the most legible in the benchmark. The reviewer abstained on just **2.2% of -detections (5 of 227)** — the lowest rate of any split, beating morgantown's 4.3%. +The imagery is the most legible of the Mapillary splits. The reviewer abstained on just **2.2% of +detections (5 of 227)** — beating morgantown's 4.3%; only bend, the GSV split, is lower at 1.9%. **The finding worth carrying elsewhere: the model's misses are overwhelmingly far away.** Treating the equirectangular elevation of each point as a ground distance (assuming a ~2.5 m camera mount): diff --git a/benchmark/annapolis/incremental_fp_tags.json b/benchmark/annapolis/incremental_fp_tags.json new file mode 100644 index 0000000..a0d8a68 --- /dev/null +++ b/benchmark/annapolis/incremental_fp_tags.json @@ -0,0 +1,29 @@ +{ + "334237679155324_0.11328_0.63281": "B", + "1349860502075738_0.09766_0.59961": "B", + "1349860502075738_0.6123_0.56836": "A", + "1069711381045172_0.83887_0.58594": "B", + "360772096383255_0.93262_0.55273": "B", + "1255568348226573_0.78516_0.57031": "U", + "340722771792916_0.08203_0.59961": "B", + "1069711381045172_0.93359_0.58398": "B", + "270044664458604_0.21484_0.55469": "U", + "3946684388792708_0.06543_0.55469": "A", + 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+ "963194777761975_0.32324_0.56836": "B", + "167719748544354_0.95703_0.55469": "A" +} \ No newline at end of file diff --git a/benchmark/morgantown/incremental_fp_tags.json b/benchmark/morgantown/incremental_fp_tags.json new file mode 100644 index 0000000..ccd57dc --- /dev/null +++ b/benchmark/morgantown/incremental_fp_tags.json @@ -0,0 +1,32 @@ +{ + "1314106923314560_0.80078_0.56836": "B", + "1053392859080211_0.84668_0.53906": "B", + "254762570847217_0.21387_0.55469": "B", + "1053392859080211_0.58203_0.91406": "B", + "1098428298746698_0.41797_0.55469": "B", + "701464722124781_0.12793_0.63281": "B", + "242318955595938_0.83105_0.61523": "B", + "778147450850347_0.46387_0.53906": "B", + "1098721767937588_0.34766_0.58594": "B", + "1320929785258220_0.20703_0.58594": "B", + "3625726214363805_0.62793_0.58594": "B", + "1563664664398611_0.58105_0.91211": "B", + "254762570847217_0.04297_0.75781": "B", + "919312849636317_0.30859_0.58594": "B", + "2020608445007968_0.83887_0.77148": "B", + 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integrated into +every analysis that quotes benchmark numbers." Written because the expensive half is not the +part people expect: the ~125-pano ground-truth review is one afternoon, but a new split +invalidates roughly a dozen committed numbers, two figures and four documents, and there is no +mechanism that tells you which. + +**Use the checklist at the bottom.** The prose explains why each step exists; the checklist is +what stops a split from landing half-integrated. + +## The two-repo boundary + +RampNet and [`sidewalk-auto-labeler`](https://github.com/ProjectSidewalk/sidewalk-auto-labeler) +split **by question, not by city** (see `benchmark/README.md`): + +- *"What are the curb ramps in this city?"* → **auto-labeler**. It is the only repo that + fetches pixels, so it also hands RampNet the imagery bundle. +- *"How good is the model, and is it improving?"* → **RampNet**. Ground truth, scoring, and + the benchmark itself. + +Phase 1 is auto-labeler work; phases 2–6 are RampNet. + +## Phase 0 — decide what the city is *for*, before spending the review + +Every split in the benchmark answers a question the others cannot, and that intent determines +how it is scored later. Decide it up front and write it down, because it is the thing that +gets lost: + +| Split | What it exists to answer | +|---|---| +| richmond | OOD deployment on mixed Mapillary rigs | +| bend | in-domain GSV reference (a Stage-2 training city) | +| clovis | worst-case imagery — 2018 GoPro Fusion | +| morgantown | cleanest imagery; the control | +| annapolis | survey-grade rig; the far-field distance finding | +| budapest_district5 | non-US infrastructure + rubric transfer | +| manual_gold | in-distribution reference with **un-anchored** GT | + +A city that duplicates an existing split's answer costs a day of review and buys a row in a +table. The most valuable additions right now are a **second GSV city** (bend's operating point +is confounded between "GSV" and "in-domain" — see `docs/operating_point.md`) and a **second +rater on budapest**, which is a review, not a new city. + +Also decide now, and record it in the PR: + +- **Does this split enter the pooled recommendation?** Default yes for a US/VA deployment city + with verdict-grade GT. No for out-of-distribution or rubric-uncertain splits. +- **Which imagery tier is it?** If the rig is new, `tier_of()` needs a branch (see phase 5). + +## Phase 1 — auto-labeler: detect the city and export the bundle + +```bash +# in sidewalk-auto-labeler +python main.py --source # enumerate → thin → detect +# results land in runs// — defaults to the geojson basename +python scripts/export_benchmark.py runs//results.jsonl \ + --bundle /benchmark/ +``` + +`--bundle` samples the run and fetches native-res pixels. The sample is **stratified**, and the +strata matter downstream: + +| stratum | n | why | +|---|---|---| +| `top` | 5 | the densest panos, always included — they make precision look good, so scoring excludes them from the *unbiased* column | +| `random` | 95 | spatially de-clustered detection panos — the honest sample | +| `empty` | 25 | zero-detection panos, so a silent model is measurable, not invisible | + +125 panos total in the four newest splits; richmond has 124 and bend 110 (only 10 `empty`). +Keep 5/95/25 unless there is a reason not to; changing it makes the split non-comparable on +the unbiased column. + +The exporter reconciles the archive against the records and writes `index.csv` (per-pano +sha256) and `decayed.txt`. **It exits non-zero on anything that means the archive is not +trustworthy** — a pano with no matching record, or a fetch that failed rather than a source +that dropped the image. Do not proceed past a non-zero exit. + +Deliverable: `benchmark//{records.jsonl, panos/}`, with `panos/` git-ignored. + +## Phase 2 — RampNet: the ground-truth review (~125 panos) + +```bash +python scripts/gt_gallery.py benchmark/ +# open the printed index.html, review, "Export verdicts", +# save over benchmark//verdicts.json +``` + +Per pano the reviewer judges **every detection** (correct / false positive / duplicate / +unsure) *and* **scans the whole pano for ramps the model missed**. Both halves are required — +the missed-ramp pass is the only thing that makes recall measurable, and the `no_missed` +attestation on a clean pano is what lets that pano into the recall denominator at all. + +Three things that are easy to get wrong: + +- **Review at model resolution.** The gallery renders at 4096×2048 with pan/zoom, never the + native image, on purpose. Mapillary panos are often 11000×5500; showing the reviewer more + than the model saw inflates the miss count and deflates recall. Richmond and bend had to be + **re-reviewed** for this reason (recall corrected 0.895 → 0.765 and 0.831 → 0.758). +- **Write the review notes.** The gallery's *Review notes* panel and the per-pano note box feed + `review_notes`, which `score_validation.py` prints **above** the numbers. This is where "the + rubric did not fit" goes. Budapest exists as a usable split only because its reviewer + recorded low confidence in their own pass; without that the 0.873/0.503 would have been + quoted as a clean number. +- **Use `unsure` freely.** It abstains from both metrics rather than forcing a guess. + Abstention rates run 1.9% (bend) to 8.5% (budapest) of detections and are themselves a + reported signal about imagery legibility. + +Deliverable: `benchmark//verdicts.json` — committed, image-free, self-contained. + +## Phase 3 — score and sanity-check + +```bash +python scripts/score_validation.py benchmark/ +``` + +Reports precision/recall with Wilson intervals, both all-panos and on the **unbiased subset** +(random + empty, dropping the 5 hand-picked `top` panos). Quote the unbiased column for +between-city comparison. + +Gates before going further: + +- Do the `empty`-group panos behave? All 25 clean means the model's "nothing here" is right. + The stratum is zero-detection by construction, so what can appear there is **missed marks** — + real ramps the model was silent on, worth reading (annapolis's negative check: 4 of its 25 + held 10 of them). +- Does the `duplicate` count look sane? 0–3 is normal; budapest's 7 flagged a genuine rubric + ambiguity (diagonal corner aprons) worth ~4 precision points. +- Does the recall gate include most panos? Panos with neither a `no_missed` attestation nor a + missed mark are excluded from recall entirely. + +## Phase 4 — regenerate the operating-point analysis + +**This is the step that gets skipped.** A new split changes pooled numbers, per-tier numbers, +the GT-completeness correction, and both figures. See `docs/operating_point.md`. + +```bash +# 1. GPU, once, on Hyak (~1.5 s/pano; the launcher skips splits already cached) +mkdir -p logs # the launcher's #SBATCH output paths live under logs/; the job dies without it +CITIES= sbatch -A scripts/analysis/run_low_floor_extract.slurm + +# 2. THE GATE — run before trusting anything downstream +python scripts/analysis/low_floor_sweep.py parity --cities +``` + +**Parity must pass.** Peaks at ≥0.55 must reproduce the committed `records.jsonl`, measured in +match radii. Expect **100% bit-exact for a Mapillary split**. A GSV split will not be exact — +the GSV production path builds a 4096×2048 intermediate, so production saw a different resample +than the native-res bundle — but should still land inside 0.5 R (bend's max is 0.439 R). The +gate itself is slightly looser than that: **≥95% of detections within 0.5 R and a count delta +≤5%** (`PARITY_MIN_MATCHED` / `PARITY_MAX_COUNT_DELTA` in `low_floor_sweep.py`). Anything worse +means preprocessing diverged and every number downstream inherits it. + +```bash +# 3. CPU — the analyses +python scripts/analysis/low_floor_sweep.py sweep # P/R/F1 + density, per split / pooled / per tier +python scripts/analysis/low_floor_sweep.py hist # calibration (feeds auto-labeler#27 stage 4) +python scripts/analysis/low_floor_sweep.py gtbias # confirm the anchoring pattern holds +python scripts/analysis/low_floor_sweep.py floor # storage-floor cost + recall ceiling +python scripts/analysis/low_floor_sweep.py distance # where the recall gain lands +``` + +### The #55 GT-completeness correction (do not skip) + +The new split's sub-0.55 precision is a **lower bound by construction** — its GT was assembled +from detections at or above 0.55, so a real ramp nobody marked scores as a false positive. +`gtbias` will show it: below the review floor, every true positive comes from a missed mark and +none from a reviewed detection. Correct it: + +```bash +python scripts/analysis/operating_point_curve.py gallery --city --op-threshold 0.25 +# review index.html, tag A / B / unsure, Download _tags.json, then: +cp _tags.json benchmark//incremental_fp_tags.json +python scripts/analysis/low_floor_sweep.py corrected --op-threshold 0.30 +python scripts/analysis/low_floor_sweep.py tagcheck --cities +``` + +Use **op-threshold 0.25** so the band is `[0.25, 0.55)`, identical to every existing split — +otherwise the A-rates are not comparable. Expect 23–30 items for a US city (budapest had 89). +A-rates so far span 13%–30% and **do not order by imagery quality**, so do not extrapolate one +from a neighbouring city; measure it. + +Two flags to not get wrong: `corrected`'s `--op-threshold` defaults to **0.35, not the +recommended 0.30**, so the flag above is load-bearing. And each `corrected` run reports a single +operating point — the multi-point corrected tables in `docs/operating_point.md` take one run +per row, not one run total. + +`tagcheck` exists because tag ids are keyed to peak *coordinates*: re-extracting can move a +marginal peak and orphan a tag silently, shrinking the correction with no error. + +### Figures + +```bash +python scripts/analysis/plot_operating_point.py # docs/figures/operating_point_pr.png +python scripts/analysis/plot_storage_floor.py # docs/figures/storage_floor_ceiling.png +``` + +Both read the committed caches and need no GPU. **Look at the output**, don't just regenerate +it — an eighth series changes the legend layout and can collide with the curves. + +## Phase 5 — code that must be told the split exists + +Grep-able touchpoints. None of these fail loudly if missed; they fail by quietly omitting the +city or mislabelling it. + +| File | What to change | +|---|---| +| `scripts/analysis/low_floor_sweep.py` → `US_SPLITS` / `CITY_SPLITS` / `ALL_SPLITS` | add the split; `US_SPLITS` membership is what puts it in the pooled recommendation | +| `low_floor_sweep.py` → `HELD_OUT` | **required** if it is not pooled — every held-out split must carry a stated reason, and a test enforces this | +| `low_floor_sweep.py` → `tier_of()` | add a branch if the rig is new, or the split lands in `unknown` | +| `low_floor_sweep.py` → `SPLIT_IMAGERY_FALLBACK` | only if `records.jsonl` lacks camera provenance | +| `low_floor_sweep.py` → `TTA_RECORD_SPLITS` | only if the committed detections used TTA | +| `scripts/analysis/plot_operating_point.py` → `SERIES` | add a colour **from the validated palette, in slot order** — never a made-up hue. Past 8 slots, fold to "Other" or facet | +| `scripts/build_benchmark_dataset.py` | ⚠️ still hardcoded to bend + richmond — the HF dataset lags the repo (issue #21) | + +## Phase 6 — documentation (where a split actually becomes real) + +| Document | What to update | +|---|---| +| `benchmark/README.md` | both split tables (all-panos and unbiased), and a **prose section** for the city: what it is for, what its imagery is, what fought the rubric, negative-check result | +| `docs/model_comparison.md` | the coverage matrix at the top — including the `#55` column with the new A-rate — and the anchoring caveat's list of measured cities | +| `docs/operating_point.md` | per-split table, per-tier table, pooled rows if it is pooled, the corrected-results tables, the storage-floor table | +| `docs/adding_a_benchmark_city.md` | this file, if the protocol changed | + +Per `CLAUDE.md`: **an omission is indistinguishable from a withheld result.** If a step was not +run, say so explicitly and say why, rather than leaving a blank. + +## Rough cost + +| Phase | Cost | +|---|---| +| 1 — detect + export | hours of compute, mostly unattended; native-res fetch is the slow part | +| 2 — **GT review** | **the dominant human cost**, ~125 panos judged detection-by-detection plus a full-pano miss scan | +| 3 — score | seconds | +| 4 — extraction | ~3 min GPU for 125 panos; everything else is CPU-seconds | +| 4 — **#55 tagging** | second human pass, 23–30 items for a US city | +| 5–6 — code + docs | the part that gets rushed, and the reason this file exists | + +## Traps + +- **A new split changes pooled numbers everywhere.** Every pooled figure in + `docs/operating_point.md` and the recommendation itself are computed over `US_SPLITS`. Adding + a US city silently moves them; re-run and update the prose, don't just add a row. +- **Do not pool a split whose GT you do not trust.** Budapest is swept and reported but held out + of every recommendation. That is a scientific judgement, recorded in `HELD_OUT`, not a bug. +- **`analysis_out/` is mostly git-ignored.** The detection caches (`op_cache/*.json`) and the + derived tables (`op/*.csv`, `*.json`) are committed exceptions so results survive without a + GPU. The gallery crops are not — but the **A/B tags are**, under + `benchmark//incremental_fp_tags.json`. Losing those means redoing the human pass. +- **`panos/` is git-ignored and irreplaceable if the source decays.** Mapillary images can + disappear. `index.csv` records sha256 per pano; archive the bundle somewhere durable (the HF + dataset, issue #21) rather than relying on a laptop. +- **Never review at native resolution.** It is the one methodological error the benchmark has + already made once, and it cost a re-review of two cities. + +## Checklist + +Copy into the PR description and tick. Anything not done gets a line saying so and why. + +**Phase 1 — bundle (auto-labeler)** +- [ ] City detected (`main.py --source `) +- [ ] `export_benchmark.py --bundle` exited **zero**; `index.csv` written, `decayed.txt` reviewed +- [ ] `benchmark//records.jsonl` committed; `panos/` present locally and archived durably +- [ ] Strata are 5 `top` / 95 `random` / 25 `empty` (or the deviation is justified) + +**Phase 2–3 — ground truth** +- [ ] Reviewed with `gt_gallery.py` **at model resolution** +- [ ] `review_notes` written (reviewer confidence + what fought the rubric) +- [ ] `verdicts.json` committed +- [ ] `score_validation.py` run; unbiased column recorded +- [ ] Camera provenance present in `records.jsonl` (`camera_make` / `camera_model`) + +**Phase 4 — operating point** +- [ ] Low-floor extraction run (`run_low_floor_extract.slurm`) +- [ ] **`low_floor_sweep.py parity` PASSES** — Mapillary bit-exact, GSV within 0.5 R +- [ ] `sweep` / `hist` / `gtbias` / `floor` / `distance` re-run +- [ ] #55 gallery tagged at **op-threshold 0.25**; `incremental_fp_tags.json` committed +- [ ] `corrected` re-run; `tagcheck` passes 100% +- [ ] `op_cache/.json` and the refreshed `op/*.csv` committed +- [ ] Both figures regenerated **and visually inspected** + +**Phase 5 — code** +- [ ] Added to `US_SPLITS` / `CITY_SPLITS` / `ALL_SPLITS` +- [ ] `HELD_OUT` reason recorded if not pooled +- [ ] `tier_of()` recognises the rig (not `unknown`) +- [ ] `SERIES` colour added from the validated palette in slot order +- [ ] `pytest -q` green + +**Phase 6 — docs** +- [ ] `benchmark/README.md`: both tables + a prose section for the city +- [ ] `docs/model_comparison.md`: coverage matrix incl. the #55 A-rate, and the anchoring caveat +- [ ] `docs/operating_point.md`: per-split, per-tier, pooled, corrected and storage-floor tables +- [ ] **Pooled numbers and the recommendation re-checked** — a new US city moves them +- [ ] Anything deliberately not run is stated explicitly, with the reason diff --git a/docs/figures/operating_point_pr.png b/docs/figures/operating_point_pr.png new file mode 100644 index 0000000..8c6b28a Binary files /dev/null and b/docs/figures/operating_point_pr.png differ diff --git a/docs/figures/storage_floor_ceiling.png b/docs/figures/storage_floor_ceiling.png new file mode 100644 index 0000000..418fdcf Binary files /dev/null and b/docs/figures/storage_floor_ceiling.png differ diff --git a/docs/model_comparison.md b/docs/model_comparison.md index 2822b46..e10212b 100644 --- a/docs/model_comparison.md +++ b/docs/model_comparison.md @@ -15,16 +15,24 @@ a run that hasn't happened, not a result being withheld. |---|---|---|---|---|---| | richmond | ✅ | ✅ all 8 | ✅ | ✅ 17% (5/29) | OOD deployment, Mapillary 360 | | bend | ✅ | ✅ all 8 | ✅ | ✅ 29% (7/24) | in-domain GSV reference | -| clovis | ✅ | ✅ all 8 | ✅ | ❌ | hardest split — 2018 GoPro Fusion | -| annapolis | ✅ | ✅ all 8 | ✅ | ❌ | survey-grade Trimble MX7; far-field finding | -| morgantown | ✅ | ✅ all 8 | ✅ | ❌ | cleanest imagery; the control split | -| budapest_district5 | ✅ | ✅ all 8 | ✅ | ❌ | **GT itself is low-confidence**; the one ranking inversion | -| manual_gold | ✅ | ✅ all 8 | ❌ too slow | ❌ | 1k panos, un-anchored GT | +| clovis | ✅ | ✅ all 8 | ✅ | ✅ 30% (7/23) | hardest split — 2018 GoPro Fusion | +| annapolis | ✅ | ✅ all 8 | ✅ | ✅ 22% (6/27) | survey-grade Trimble MX7; far-field finding | +| morgantown | ✅ | ✅ all 8 | ✅ | ✅ 13% (4/30) | cleanest imagery; the control split | +| budapest_district5 | ✅ | ✅ all 8 | ✅ | ✅ 26% (23/89) | **GT itself is low-confidence**; the one ranking inversion | +| manual_gold | ✅ | ✅ all 8 | ❌ too slow | n/a — un-anchored GT | 1k panos, the anchoring control | "All 8" is the roster in the class table below: RampNet, 2 Geminis, 2 Qwens, Molmo, OWLv2, -Grounding DINO. Every split now carries the full roster; the remaining ❌s are the -GT-completeness correction (#55, done on two cities) and the null-recall pass on -`manual_gold` (O(n²) in panos — see that section). +Grounding DINO. Every split now carries the full roster, and as of 2026-07-28 the +GT-completeness correction (#55) covers **all six city splits**. `manual_gold` needs no +correction — its GT was labelled independently of RampNet, which is what makes it the control +for the anchoring effect. The one remaining ❌ is the null-recall pass on `manual_gold` +(O(n²) in panos — see that section). + +**The operating-point analysis those A-rates feed lives in +[`docs/operating_point.md`](operating_point.md)** (issue #54): the full precision/recall/F1 +sweep from a 0.05 peak floor across all seven splits, per-imagery-tier curves, the +confidence-calibration tables, and the recommendation to lower the deployment threshold from +0.55 to 0.30. **Supervised baseline in progress (issue #51).** The roster above is all zero-shot except RampNet. A supervised **YOLO** baseline — the architecture-vs-data ablation (does a generic @@ -487,21 +495,30 @@ two columns coincide exactly). The `compare.py` CLI prints both side by side. - **RampNet-anchored GT.** The GT was assembled during a RampNet review. A reviewer scanning fresh for another model might catch a few more ramps; the complete-scan attestation (`no_missed`) mitigates this, but it is a known asymmetry. **It has now been measured on - two cities, and it is not small.** Re-reviewing the detections RampNet only surfaces below - its deployed 0.55 threshold — a confidence band the GT never fully audited — found real, - unlabelled curb ramps at **17% (5/29, richmond)** and **29% (7/24, bend)** of those - detections (issue #55; tags in `benchmark//incremental_fp_tags.json`, reproduce with - `operating_point_curve.py gallery --tags`). Two consequences worth carrying: + all six city splits, and it is not small.** Re-reviewing the detections RampNet only + surfaces below its deployed 0.55 threshold — a confidence band the GT never fully audited — + found real, unlabelled curb ramps at **17% (richmond)**, **29% (bend)**, **30% (clovis)**, + **13% (morgantown)**, **22% (annapolis)** and **26% (budapest)** of those detections + (issue #55; tags in `benchmark//incremental_fp_tags.json`, reproduce with + `operating_point_curve.py gallery --tags` or `low_floor_sweep.py corrected`). Two + consequences worth carrying: - Precision below ~0.55 is **understated** for RampNet, and by an amount that varies by city — so the anchoring asymmetry is not a constant that cancels in a ranking. - The A-rate is measured *only* on RampNet's own low-confidence detections. It says the GT is incomplete; it does **not** say by how much a challenger is penalised, since a challenger's misses are a different population. Do not subtract it from anyone's score. - Only richmond and bend have been corrected — clovis, morgantown, annapolis, - budapest_district5 and manual_gold have not (see the coverage matrix at the top), so no - cross-city GT-completeness constant should be quoted yet. Two cities is also too few to - tell whether the 17%/29% spread is imagery, reviewer, or noise. + All six city splits are now corrected, and the spread — **13%–30%** — still does **not** + support a single cross-city GT-completeness constant. It does not order by imagery quality + either: morgantown, the cleanest split in the benchmark, has the *lowest* A-rate and clovis, + the softest, the highest. Apply the per-split correction; do not subtract an average. + + `manual_gold` is the control rather than a gap: its GT was labelled independently of + RampNet, so it has no anchoring to correct. Comparing the two regimes shows the mechanism + clearly (the anchored splits have a precision discontinuity at the 0.55 review floor; + manual_gold's calibration curve is smooth) while aggregate sub-0.50 precision is + near-identical (0.195 anchored vs 0.208 un-anchored) — most likely because manual_gold's + in-distribution advantage offsets the anchoring. See `docs/operating_point.md`. - **Box → point reduction.** Box models are scored by their box centers, at the same radius as RampNet's point detections. Localization differences finer than the radius aren't measured. Molmo is the exception — it emits points natively, so nothing is reduced. diff --git a/docs/operating_point.md b/docs/operating_point.md new file mode 100644 index 0000000..05feb3b --- /dev/null +++ b/docs/operating_point.md @@ -0,0 +1,523 @@ +# The deployment operating point: how low should the peak threshold go? + +RampNet's deployed operating point is peak extraction at `threshold_abs = 0.55`, +`min_distance = 10`. Until this analysis it had **never been characterised below 0.55** — +the committed benchmark detections stop there, so every published precision/recall number +describes one point on a curve nobody had drawn. + +This document draws it, on all seven benchmark splits, and recommends a number. It is +issue [#54](https://github.com/ProjectSidewalk/RampNet/issues/54); the ground-truth +correction it depends on is [#55](https://github.com/ProjectSidewalk/RampNet/issues/55); +the deployment consumer is +[sidewalk-auto-labeler#20](https://github.com/ProjectSidewalk/sidewalk-auto-labeler/issues/20) +and the multi-view consumer is +[labeler#27](https://github.com/ProjectSidewalk/sidewalk-auto-labeler/issues/27) stage 4. + +**Headline:** lowering the threshold from 0.55 to **0.30** buys **+7.7 recall points** pooled +(+6 to +10 per split) for −4.8 points of GT-completeness-corrected precision, while detection +density rises only from 1.85 to 2.23 per pano. Recall is RampNet's weak metric everywhere, +and this is the cheapest lever that exists — one constant, no retraining. + +## How the numbers were produced + +One inference pass per pano, extracting *every* heatmap peak down to a 0.05 floor (keeping +`min_distance = 10`) and carrying each peak's height as its confidence; the threshold is +then swept post-hoc on CPU. So a single GPU run supports every operating point rather than +one run per threshold. + +- Extraction: `scripts/analysis/operating_point_curve.py extract`, launched on Hyak by + `scripts/analysis/run_low_floor_extract.slurm` (one L40S, 41 min for 1,625 panos). +- Analysis: `scripts/analysis/low_floor_sweep.py` (`parity`, `sweep`, `hist`, `gtbias`, + `distance`, `tagcheck`) — **CPU-only**, reading the cached detections, so every number + below re-derives without a GPU. +- Preprocessing replicates the deployment path exactly: PIL RGB → resize 2048×4096 bilinear + → ImageNet normalisation → `peak_local_max`, **no TTA**. + +`min_distance` stays at 10 and is not a lever: #25 found 10→3 buys ~0.5 recall points and +costs precision, and `peak_nms_check.py` (#62) showed no suppression radius separates real +adjacent ramp pairs from the rare duplicate. + +### The parity gate + +Peaks at ≥ 0.55 must reproduce each split's committed `records.jsonl`, or the sweep inherits +a preprocessing divergence. Bit-exactness is the wrong bar, so displacement is measured in +**match radii (R)** — the unit the scorer works in — and the gate asks whether any drift +could change a scoring outcome. + +| split | records | cache | identical cell | within 0.5 R | max displacement | +|---|---|---|---|---|---| +| richmond | 267 | 267 | 100.0% | 100.0% | 0.000 R | +| clovis | 164 | 164 | 100.0% | 100.0% | 0.000 R | +| morgantown | 209 | 209 | 100.0% | 100.0% | 0.000 R | +| annapolis | 227 | 227 | 100.0% | 100.0% | 0.000 R | +| budapest_district5 | 189 | 189 | 100.0% | 100.0% | 0.000 R | +| bend | 265 | 257 | 79.0% | 98.4% | 0.439 R | +| manual_gold † | 3610 | 3487 | 80.9% | 99.9% | 0.472 R | + +**Every Mapillary split reproduces bit-exactly, including on different hardware** (these were +extracted on an L40S; the committed records were not). Bend is the sole exception and the +reason is structural, not stochastic: bend is the only GSV city split, and the GSV production +path assembled tiles into a **4096×2048 intermediate**, so production fed the model a +different resample of the same pano than this native-res bundle does. The prediction that +follows — that the four then-unextracted Mapillary splits would come back exact and bend +would remain the only one that jitters — was made before those splits ran, and held. + +† `manual_gold` is deliberately **not gated**: its committed detections were exported *with* +horizontal-flip TTA at a 0.05 floor (`benchmark/manual_gold/detections_meta.json`), while +this cache is the no-TTA deployment path. Its row is therefore a **TTA-vs-no-TTA delta**, and +a useful free data point for [#78](https://github.com/ProjectSidewalk/RampNet/issues/78): +TTA yields **3.5% more detections** at ≥0.55 (3610 vs 3487) with 99.9% of them co-located +within half a match radius. + +**Carry this caveat for bend specifically.** Its ground-truth points derive from the +*production* detections while its predictions here come from the *native-res* resample, so +GT and predictions sit up to 0.44 R apart. That eats into the 1 R matching tolerance and +makes bend's numbers mildly pessimistic relative to the other splits. + +## The central bias: sub-0.55 precision is a lower bound, and we can prove it + +The benchmark GT for the six city splits was assembled during a review of RampNet's own +detections *at or above 0.55*. So in exactly the band this sweep opens up, a prediction can +only be credited as a true positive if a human independently flagged that ramp during the +missed-ramp pass. **A real curb ramp nobody marked scores as a false positive.** + +This is usually stated as a caveat. It can be measured. Decomposing every true positive by +the origin of the GT point it matched (`low_floor_sweep.py gtbias`): + +| confidence | TPs from a reviewed detection | TPs from a missed mark | +|---|---|---| +| 0.0–0.5 | **0** | 37 (richmond) / 32 (bend) / 43 (clovis) | +| 0.5–0.6 | 6 / 15 / 8 | 4 / 6 / 1 | +| 0.6–1.0 | 231 / 233 / 131 | 1 / 3 / 0 | + +Below the review floor, **every** true positive comes from a missed mark and none from a +reviewed detection, on every split. That is structural, and it has a visible fingerprint in +the calibration curve: pooled P(real) jumps from 0.476 in the 0.45–0.50 bin to 0.821 in +0.50–0.55 — a discontinuity at the review boundary that no property of the model could +produce. + +### `manual_gold` as a control — and a tension it exposes + +`benchmark/manual_gold` was labelled independently of RampNet (1,000 panos, YOLO box centres, +no verdict review), so it carries **no anchoring at any threshold**. It is the natural control. +The comparison supports the *mechanism* but **not** a large aggregate effect, and that is worth +reporting rather than smoothing over. + +What it does show — the discontinuity is specific to the anchored splits: + +| bin | city splits (anchored) | manual_gold (un-anchored) | +|---|---|---| +| 0.40–0.45 | 0.568 | 0.481 | +| 0.45–0.50 | 0.476 | 0.544 | +| **0.50–0.55** | **0.821** | **0.621** | +| 0.55–0.60 | 0.839 | 0.637 | + +The anchored curve leaps at the review floor (+0.345 across it); the un-anchored one steps by ++0.077 and keeps rising smoothly. That is the fingerprint the mechanism predicts. + +What it does **not** show is a large aggregate gap: + +| band | city splits (anchored) | manual_gold (un-anchored) | +|---|---|---| +| below 0.50 | 0.195 (n = 995) | 0.208 (n = 1164) | +| 0.50–0.55 | 0.821 (n = **28**) | 0.621 (n = 58) | +| 0.55 and up | 0.965 (n = 1074) | 0.955 (n = 3487) | + +Pooled precision below 0.50 is **essentially identical** on anchored and un-anchored GT +(0.195 vs 0.208), and so is precision above 0.55. So although the *direction* of the bias is +certain — an unreviewed real ramp can only be scored as a false positive — this control does +**not** establish that it is large, and the cliff itself rests on only 28 detections in the +0.50–0.55 bin. + +Two readings are consistent with this and the control alone cannot separate them: the anchoring +effect is genuinely small in aggregate, or it is real but offset by `manual_gold` being +*in-distribution* GSV from the training cities (which should make RampNet look better there, +pushing the un-anchored numbers up). + +**#55's A/B tagging settles it, and the effect is real.** All six city splits have now been +spot-checked (jonf, 2026-07-28): every unmatched prediction in the `[0.25, 0.55)` band was +tagged **A** (a real ramp the GT missed), **B** (a genuine false positive) or **unsure**. + +| split | incremental FPs | A | B | unsure | A-rate | +|---|---|---|---|---|---| +| richmond | 29 | 5 | 22 | 2 | 17.2% | +| bend | 24 | 7 | 15 | 2 | 29.2% | +| clovis | 23 | 7 | 13 | 3 | 30.4% | +| morgantown | 30 | 4 | 25 | 1 | 13.3% | +| annapolis | 27 | 6 | 15 | 6 | 22.2% | +| budapest_district5 | 89 | 23 | 59 | 7 | 25.8% | + +**Pooled over the five US splits, 26.9% of the incremental false positives in `[0.30, 0.55)` +were real curb ramps the ground truth had missed.** So the raw curve's precision penalty for +lowering the threshold is materially overstated — by roughly a quarter of the newly-added +false positives — and the aggregate similarity to `manual_gold` above is better explained by +that split's in-distribution advantage than by the bias being small. + +Spread is 13%–30% with no obvious ordering by imagery quality (morgantown, the cleanest split, +is lowest; clovis, the softest, is highest), so **do not quote a single cross-split +GT-completeness constant** — apply the per-split correction. + +### Why the recall gain, by contrast, is exact — and itself a lower bound + +Every additional true positive below 0.55 matches a ramp a reviewer explicitly marked as a +real missed ramp. Those are confirmed ramps, not inferred ones, so the recall gain is not +subject to the bias above. + +It is stronger than that. If the GT is missing `K` real ramps (which #55 exists to measure), +completing it would add those ramps to the denominator *and* credit them to the low-threshold +operating point that found them. Writing `a` for the extra TPs gained and `n` for the recall +denominator, the corrected gain `(a+K)/(n+K)` exceeds the measured `a/n` whenever `n > a` — +and here `n` ≈ 1,393 against `a` ≈ 70. **So the measured recall gain understates the true +one.** + +Both directions favour lowering the threshold. The measured trade is a worst case. + +## Results + +![Precision-recall response to the peak threshold, per split](figures/operating_point_pr.png) + +Reproduce with `python scripts/analysis/plot_operating_point.py`. + +The left panel is the artifact this issue asked for: the precision/recall trade traced out by +sweeping the threshold, one line per split, with the deployed 0.55 point (hollow) and the +recommended 0.30 point (filled) marked on each. The shape is the finding — the curves run +almost flat across the operating region, so recall is bought at a shallow precision cost until +the curve turns down hard somewhere past 0.85 recall. + +The right panel is the same sweep read as F1 against threshold, and it is there to make a +negative point: **F1 barely discriminates**. The pooled curve varies by under 0.01 across +0.25–0.55, so "F1-optimal" is not a sufficient reason to pick any particular number, and the +recommendation below rests on the recall-first policy, the density budget and reversibility +instead. Note also how the per-split optima (the dots) cluster in a narrow band while the +*levels* differ a lot — that is the same pattern the per-tier table shows. + +### Pooled across the five US/VA city splits (n = 609 panos) + +Wilson 95% intervals in brackets. + +| threshold | precision | recall | F1 | detections/pano | +|---|---|---|---|---| +| 0.25 | 0.871 [0.852, 0.888] | 0.829 [0.808, 0.848] | 0.850 | 2.34 | +| 0.30 | 0.897 [0.879, 0.912] | 0.818 [0.797, 0.837] | 0.855 | 2.23 | +| **0.32** | 0.905 [0.888, 0.920] | 0.814 [0.793, 0.834] | **0.857** | 2.20 | +| 0.35 | 0.913 [0.896, 0.928] | 0.802 [0.780, 0.822] | 0.854 | 2.14 | +| 0.40 | 0.931 [0.916, 0.944] | 0.790 [0.767, 0.810] | 0.855 | 2.06 | +| 0.45 | 0.943 [0.928, 0.955] | 0.775 [0.752, 0.796] | 0.851 | 1.98 | +| 0.50 | 0.961 [0.948, 0.971] | 0.760 [0.737, 0.782] | 0.849 | 1.91 | +| **0.55** (deployed) | 0.965 [0.952, 0.974] | 0.744 [0.720, 0.766] | 0.840 | 1.85 | + +**F1 is flat.** It varies by 0.008 across the whole 0.25–0.55 range, so F1-optimality alone +does not pick an operating point — a finding worth stating plainly, because "F1-optimal" is +the obvious criterion and here it is nearly uninformative. What *does* move decisively is +recall: 0.744 → 0.814, with **non-overlapping** confidence intervals. + +### Per split, 0.55 → 0.32 + +| split | P@0.55 | R@0.55 | P@0.32 | R@0.32 | ΔR | ΔP | dets/pano | F1-opt | +|---|---|---|---|---|---|---|---|---| +| richmond | 0.964 | 0.768 | 0.911 | 0.829 | +0.061 | −0.052 | 2.15 → 2.57 | 0.33 | +| bend | 0.980 | 0.755 | 0.927 | 0.813 | +0.058 | −0.053 | 2.34 → 2.75 | 0.50 | +| clovis | 0.914 | 0.713 | 0.855 | 0.815 | **+0.103** | −0.060 | 1.31 → 1.61 | 0.35 | +| morgantown | 0.975 | 0.730 | 0.903 | 0.805 | +0.075 | −0.072 | 1.67 → 1.99 | 0.32 | +| annapolis | 0.973 | 0.738 | 0.912 | 0.806 | +0.068 | −0.062 | 1.82 → 2.13 | 0.32 | +| budapest † | 0.874 | 0.510 | 0.718 | 0.637 | +0.127 | −0.156 | 1.51 → 2.27 | 0.37 | +| manual_gold ‡ | 0.955 | 0.849 | 0.926 | 0.884 | +0.035 | −0.028 | 3.49 → 3.74 | 0.36 | + +† Budapest is swept but **held out of the pooled recommendation** (single-rater GT at low +reviewer confidence — see `benchmark/README.md`). +‡ `manual_gold` is likewise held out: in-distribution GSV from the training cities with +independently-labelled GT. It is the control, not a deployment city. + +**Clovis is the cheapest place to lower the threshold, not the most expensive.** This +reverses the expectation going in — clovis has the softest imagery (2018 GoPro Fusion) and +the lowest deployed precision, so it looked like the binding constraint. It gains **+10.3 +recall points for −6.0 precision**, the best ratio of any US split. + +### Per imagery tier + +Tiers are assigned **per pano** from `camera_make`/`camera_model`, not per split — richmond +alone mixes iSTAR Pulsar and GoPro Max, so split-level grouping would smear the camera effect. + +| tier | n | F1-opt | ΔR at 0.32 | ΔP at 0.32 | +|---|---|---|---|---| +| action cam, modern (GoPro Max) | 158 | 0.32 | +0.073 | −0.068 | +| action cam, legacy (GoPro Fusion 2018) | 125 | 0.35 | +0.103 | −0.060 | +| survey-grade (Trimble MX7) | 125 | 0.32 | +0.068 | −0.062 | +| pro 360 (iSTAR Pulsar) | 77 | 0.34 | +0.063 | −0.051 | +| Google Street View | 110 | **0.50** | +0.058 | −0.053 | + +**The tier optima cluster at 0.32–0.35, which argues against per-tier thresholds.** Four of +five tiers agree within 0.03 despite spanning a 2018 action camera and a survey-grade vehicle +rig. That convergence is the useful result: imagery quality moves the *level* of precision but +not the *location* of the optimum. + +GSV is the exception at 0.50, and it should not drive a per-tier policy yet, because three +things are confounded in it and all point the same way: it is a single split (bend, n=110), +bend is **in-domain** (a Stage-2 training city), and it is the one split whose GT and +predictions come from different resamples (see the parity caveat). Revisit when a second GSV +split exists. + +### Where the recall gain lands on the distance axis + +`benchmark/README.md` establishes that RampNet's misses skew far-field, which is the case for +multi-view fusion (labeler#27). If lowering the threshold only recovered near ramps, the two +levers would overlap. It does not: + +| band | mean ΔR across the 5 US splits | ramps gained | +|---|---|---| +| near (<12.5 m) | +0.071 | 46 | +| mid (12.5–25 m) | +0.078 | 40 | +| far (>25 m) | +0.072 | 12 | + +**The gain is essentially uniform across distance**, so the threshold lever and the +multi-view lever are largely independent and **stack**. Far-field recall stays poor even at +0.32 (bend 0.214, clovis 0.389, annapolis 0.490), so multi-view remains necessary — lowering +the threshold does not substitute for it. + +Distances are the flat-ground estimate (camera height 2.5 m assumed), monotonic in `y`, so the +band ordering is a rank statement; only the metre labels depend on the assumption. + +### Density: this is not recall-by-carpet + +`docs/model_comparison.md` establishes that an open detector's apparent recall is largely +density — OWLv2 reaches its recall at **55–88 boxes/pano**. The obvious objection to lowering +RampNet's threshold is that it buys recall the same cheap way. It does not: pooled density goes +**1.85 → 2.23 detections/pano** at the recommended 0.30 (2.20 at 0.32, 2.14 at 0.35). That is a +21% increase in review burden for a 10% relative increase in recall, and it leaves RampNet +roughly **25–40× sparser** than the open detectors at their operating points. + +### Corrected results (#55 applied) + +Crediting confirmed A tags moves them from false positives to true positives, in **both** the +precision and recall denominators — correcting precision alone would report a corrected P +against an uncorrected R. `band hi` additionally credits the `unsure` items, so it is the +honest upper end rather than a formality. + +Pooled over the five US splits: + +| operating point | raw P | **corrected P** | band hi | raw R | corrected R | corrected F1 | +|---|---|---|---|---|---|---| +| 0.25 | 0.871 | 0.893 | 0.904 | 0.829 | 0.833 | 0.862 | +| **0.30** | 0.897 | **0.917** | 0.928 | 0.818 | 0.821 | **0.866** | +| 0.35 | 0.913 | 0.928 | 0.935 | 0.802 | 0.804 | 0.862 | +| 0.55 (deployed) | 0.965 | — † | — | 0.744 | — | 0.840 | + +† No correction applies at 0.55: the incremental band is empty by construction. Its precision +is not immune to GT incompleteness, but with only 38 pooled false positives there the effect is +small. + +Per split at 0.30: + +| split | raw P | corrected P | band hi | corrected R | corrected F1 | +|---|---|---|---|---|---| +| richmond | 0.902 | 0.919 | 0.930 | 0.832 | 0.873 | +| bend | 0.924 | 0.942 | 0.948 | 0.825 | 0.880 | +| clovis | 0.851 | **0.883** | 0.899 | 0.826 | 0.853 | +| morgantown | 0.888 | 0.905 | 0.909 | 0.808 | 0.854 | +| annapolis | 0.902 | 0.920 | 0.939 | 0.813 | 0.863 | +| budapest ‡ | 0.707 | 0.762 | 0.777 | 0.660 | 0.707 | + +‡ excluded from the pooled row and the recommendation. + +**The correction changed the answer.** On raw numbers the F1 optimum sat at 0.32 and the +conservative choice was 0.35; with the GT completeness correction applied, corrected F1 peaks +at **0.30** and corrected precision there is **0.917** pooled — higher than the *raw* precision +at 0.35. That is precisely what #55 existed to determine, and it moved the recommendation down. + +**Caveat on 3 of the A tags** (1 each in clovis, morgantown, budapest): they sit within 2 match +radii of an already-detected ramp, so they are more likely a second hit on one ramp than a ramp +the GT missed. The tooling flags these rather than hiding them; removing all three from the US +pool changes pooled corrected precision by under 0.002. + +### The storage floor and the recall ceiling (labeler#28, labeler#27 stage 4) + +The submission threshold this document recommends is a *policy* choice and reversible. The +labeler's **storage floor** (`DETECTION_STORAGE_FLOOR = 0.1`, with a top-50 per-pano cap; +labeler#28) is neither: a peak below it is never written, so no downstream consensus policy +can ever promote it. That makes it worth checking directly rather than assuming. + +![Storage floor and recall ceiling](figures/storage_floor_ceiling.png) + +Reproduce with `python scripts/analysis/low_floor_sweep.py floor` and +`python scripts/analysis/plot_storage_floor.py`. + +**What a 0.1 floor costs.** Counting ground-truth ramps whose *best* candidate falls in +`[0.05, 0.10)` — i.e. ramps that a 0.1 floor makes permanently unrecoverable: + +| split | GT ramps | best candidate in [0.05, 0.10) | share | +|---|---|---|---| +| richmond | 310 | 8 | 2.58% | +| bend | 327 | 8 | 2.45% | +| clovis | 195 | 6 | 3.08% | +| morgantown | 267 | 7 | 2.62% | +| annapolis | 294 | 9 | 3.06% | +| **POOLED (5 US)** | **1393** | **38** | **2.73%** | +| budapest | 300 | 7 | 2.33% | +| manual_gold | 3919 | 19 | 0.48% | + +**The recall ceiling.** The share of GT ramps with *any* candidate at or above a floor — +the hard upper bound on what multi-view consensus can ever recover: + +| | pooled (5 US) | +|---|---| +| recall at the deployed 0.55 | 0.744 | +| recall at the recommended 0.30 | 0.818 | +| **ceiling at the 0.10 storage floor** | **0.872** | +| ceiling at the 0.05 extraction floor | 0.899 | + +So labeler#27 stage 4 has **+12.8 recall points** of headroom above the deployed threshold to +work with, and **+5.4 points** above the operating point recommended here — but it is capped +at 0.872 by the storage floor, not by the 0.899 the model actually produces. + +**The verdict: lower the storage floor from 0.10 to 0.05.** The floor's own stated +justification (labeler#28) is that storing too little is irreversible while storing too much +only costs disk, bounded by the top-K cap. That argument survives contact with the data, and +the data says 0.1 is not where the bound should sit: + +- **The cap never binds.** At the 0.1 floor the busiest pano in the entire benchmark holds + **14** candidates against a cap of 50; medians are 2–5. At a 0.05 floor the maximum is still + **14**. The top-50 cap is not the volume bound — the floor is, and it is doing work nobody + asked it to do. +- **The cost is ~2.7% of findable ramps**, permanently, for a volume saving of roughly one + extra candidate per pano. +- **The recovered ramps are exactly the population multi-view fusion is for.** They are + ramps too faint to clear any single-view threshold, which is the case stage 4 exists to + handle. + +This is a labeler-side change and belongs in that repo; it is recorded here because this is +where the measurement lives. + +## The deployment constraint is not what it was assumed to be + +It is widely assumed that Project Sidewalk gates AI label submissions on +`ai-validation-min-accuracy` (0.92). **It does not.** In `AiController.submitAiLabel`, +submission is gated by an internal API key plus the boolean per-city +`ai-label-submission-enabled` flag — there is **no accuracy threshold on that path at all**. +The 0.92 belongs to a different subsystem (the DINOv2 *validator* scoring existing labels) and +never touches the auto-labeler. + +Two consequences: + +1. **No hard floor blocks a lower operating point.** The constraint is a policy judgment about + review burden, not a gate. +2. **The change is reversible.** `ExploreService.submitAiLabelData` persists each label's + `confidence` into `label_ai_info`, so anything submitted at a lower threshold can be + re-thresholded server-side later. The asymmetry that matters — a ramp never submitted is + invisible forever, a low-confidence one that was submitted can always be filtered — points + the same way as the recall-first policy. + +## Recommendation + +**Adopt a single uniform threshold of 0.30**, replacing 0.55. + +| | at 0.55 (deployed) | at 0.30 | +|---|---|---| +| pooled precision | 0.965 | **0.917** corrected (0.897 raw, 0.928 band high) | +| pooled recall | 0.744 | **0.821** corrected (0.818 raw) | +| pooled F1 | 0.840 | **0.866** corrected | +| detections/pano | 1.85 | 2.23 | + +**+7.7 recall points for −4.8 precision points, at 0.38 more detections per pano.** + +Why 0.30: + +- It is the **corrected** F1 optimum, computed after applying #55's per-split A/B tagging + rather than from the raw curve that the anchoring bias distorts. +- Corrected precision stays **≥0.88 on every US split** and 0.917 pooled — above the 0.92 bar + the project uses elsewhere for AI assertions once the `band high` (0.928) is taken into + account, and comfortably clear of anything that reads as a quality regression. +- Recall is RampNet's weak metric on **every** split, precision is not. Under the recall-first + policy — a false negative is a permanent hole in the inventory, a false positive is a cheap + human review — this is the right side of a flat trade. +- Density stays sparse (2.23/pano), so the recall is not bought by carpeting the image. +- The change is **reversible**: per-label confidence is stored server-side, so anything + submitted at 0.30 can be filtered later. + +**If a more conservative first move is wanted, 0.35** gives corrected P 0.928 / R 0.804 and +gives up 1.7 recall points. Both are inside the flat region; 0.30 is the recall-first choice +and 0.35 the precision-first one. I recommend 0.30. + +**Clovis is the one split to watch**: corrected precision 0.883 at 0.30 (band high 0.899) +against 0.914 deployed — the only US split that gives up meaningfully more than it does +elsewhere. It is also the split with the largest recall gain (+10.3 points) and the highest +A-rate (30%), so the trade is still favourable; but if per-city tuning is ever introduced, +clovis-like imagery (2018-era GoPro Fusion) is where it would start. + +**Not per-tier**, for now: four of five tiers agree within 0.03, and the one exception (GSV) is +confounded by in-domain-ness, a single split, and the resample caveat. A per-tier policy would +be fitting noise. + +**Budapest is excluded from this recommendation** and should get its own decision. It is the +one split where lowering costs about as much precision as it buys recall, and its GT is +single-rater at low reviewer confidence. Nothing here transfers to non-US infrastructure +without a rubric written for it. + +### Honest limits on this number + +- **It is tuned on the benchmark.** There is no separate validation split, so 0.30 is an + optimistic estimate of what threshold tuning buys. The mitigation is that the F1 curve is + flat — anything in 0.25–0.40 performs within noise, so the *choice* is robust even if the + *argmax* is not. +- **The correction rests on one rater**, the same person who produced the GT. A second rater on + the A/B tags would tighten it, and is the same ask already outstanding for budapest. +- **`unsure` items are not credited** in the corrected column, only in `band high`. Pooled that + is 14 items — the gap between 0.917 and 0.928. + +### What would change this + +- **A second GSV split**, which would show whether bend's 0.50 optimum is GSV or in-domain-ness. +- **Multi-view fusion** (labeler#27), which changes the question from "what threshold for a + single view" to "what threshold for promotion given k agreeing views" — a lower number, since + consensus supplies the precision the threshold currently has to. +- **Flip-TTA at the deployment point** (#78), measurable from the same cache: TTA yields 3.5% + more detections at ≥0.55 on manual_gold. + +## For labeler#27 stage 4: the promotion floor + +Stage 4 wants the confidence at which a single-view detection is trustworthy enough to promote +on multi-view consensus, taken from GT-true vs GT-false histograms. Those are in +`analysis_out/op/confidence_calibration.{json,csv}` per split and pooled, with Wilson intervals. + +**The measured single-view crossover is ≈0.40–0.45, on both anchored and un-anchored GT.** On +the five US city splits pooled, P(real) first exceeds 0.5 in the 0.40–0.45 bin (0.568) but dips +back to 0.476 in 0.45–0.50 — it is noisy at these bin sizes. On `manual_gold`, whose GT carries +no anchoring, the curve is smooth and crosses cleanly in the 0.45–0.50 bin (0.544, against +0.481 just below). + +Worth being precise about the direction, because it is easy to get backwards: the anchored +splits *under-credit* real ramps below 0.55, so their crossover is if anything an +**over**-estimate of where real detections stop dominating — yet the un-anchored control lands +at essentially the same place or slightly higher. So there is no evidence here for a +dramatically lower crossover, and the two GT regimes agreeing is the more useful result than +either number alone. + +Caveat in the other direction: `manual_gold` is in-distribution GSV from the training cities, +so it is optimistic for out-of-distribution deployment. + +**The promotion floor should nonetheless sit below this crossover**, and that is reasoning +rather than measurement: the crossover is where a *single* view stops being more likely right +than wrong, whereas stage 4 accepts a detection only when *k* views independently agree on a +ground location. Consensus supplies evidence a single view lacks, so the break-even confidence +under agreement is lower. How much lower is not answerable from per-pano data — the benchmark +bundles are thinned to 30 m spacing precisely so no physical ramp appears twice, so nothing +here measures view agreement. That is stage 3's own GT to produce. + +## Reproducing + +Adding a **new** city to the benchmark re-opens most of this document — pooled numbers, the +per-tier table, the correction and both figures all move. The end-to-end protocol, including +which of these commands to re-run and which documents to update, is +[`docs/adding_a_benchmark_city.md`](adding_a_benchmark_city.md). + +```bash +# GPU, once (Hyak): writes analysis_out/op_cache/.json +sbatch -A scripts/analysis/run_low_floor_extract.slurm + +# CPU, everything else +python scripts/analysis/low_floor_sweep.py parity # the gate — run this first +python scripts/analysis/low_floor_sweep.py sweep # per-split + pooled + per-tier +python scripts/analysis/low_floor_sweep.py hist # calibration for labeler#27 +python scripts/analysis/low_floor_sweep.py gtbias # the anchoring measurement +python scripts/analysis/low_floor_sweep.py distance # where the recall gain lands +python scripts/analysis/low_floor_sweep.py tagcheck # #55 tags still resolve +``` diff --git a/scripts/analysis/README.md b/scripts/analysis/README.md index d0fcd55..28c340b 100644 --- a/scripts/analysis/README.md +++ b/scripts/analysis/README.md @@ -1,10 +1,25 @@ # Recall error-analysis scripts -The analysis behind [`docs/detection_recall_analysis.md`](../../docs/detection_recall_analysis.md). -Everything here reads the committed benchmark bundles (`benchmark/{richmond,bend}/`) plus the -native-res `panos/` (git-ignored — they must be present locally). +The analysis behind [`docs/detection_recall_analysis.md`](../../docs/detection_recall_analysis.md) +and [`docs/operating_point.md`](../../docs/operating_point.md). Scripts read the committed +benchmark bundles in `benchmark/`; the ones that need pixels also need the native-res `panos/` +(git-ignored — they must be present locally). -Outputs go to `$RAMPNET_ANALYSIS_OUT` (default `analysis_out/`, git-ignored). +Outputs go to `$RAMPNET_ANALYSIS_OUT` (default `analysis_out/`), which is git-ignored **except** +for two things committed on purpose so results survive without a GPU: + +- `analysis_out/op_cache/*.json` — the low-floor detection caches (image-free, ~780 KB). Every + number in `docs/operating_point.md` re-derives from these on CPU. +- `analysis_out/op/*.csv` and `*.json` — the derived result tables, so a figure quoted in prose + can be checked against the table it came from. + +The gallery crops under `analysis_out/op/*_incremental_fp/` stay ignored (181 MB of regenerable +PNGs); their irreplaceable part, the human A/B tags, is committed at +`benchmark//incremental_fp_tags.json`. + +**`low_floor_sweep.py` and the two `plot_*.py` scripts need no GPU and no imagery** — they read +the committed caches only, so anyone can reproduce the operating-point numbers from a clean +checkout. ## Run order @@ -20,9 +35,22 @@ Outputs go to `$RAMPNET_ANALYSIS_OUT` (default `analysis_out/`, git-ignored). | `depth_analysis.py` | no | Recall vs true distance / apparent size + the resolution forecast. Needs `gt_depth_da3.json`. | | `size_analysis.py` | no | Geometry-only size stratification (no depth model) + the hard-miss montage figure. | | `overlap_test.py` | **yes** | Do the threshold and resolution levers target the same ramps? Needs `gt_depth_da3.json`. | -| `operating_point_curve.py extract` | **yes** | Inference once → all peaks down to a low score floor → per-pano cache (issue #54). | +| `operating_point_curve.py extract` | **yes** | Inference once → all peaks down to a low score floor → per-pano cache (issue #54). Handles both bundle kinds, so `manual_gold` (independent YOLO GT, no verdict review) is covered too. | | `operating_point_curve.py curve` | no | Continuous PR curve + honest AP + F1-vs-threshold from the cache (#54). | | `operating_point_curve.py gallery` | no | Incremental-FP crops for the GT-completeness spot-check → corrected precision with an error band (#54). | +| `low_floor_sweep.py parity` | no | **Gate — run first.** Do the cached peaks at 0.55 reproduce each split's committed `records.jsonl`? Measured in match radii, since bit-exactness is the wrong bar (#54). | +| `low_floor_sweep.py sweep` | no | P/R/F1 **and detections-per-pano** vs threshold, per split, pooled, and per **imagery tier** (tier assigned per pano from camera provenance, not per split). | +| `low_floor_sweep.py hist` | no | GT-true vs GT-false confidence calibration with Wilson intervals — the promotion floor input for auto-labeler#27 stage 4. | +| `low_floor_sweep.py gtbias` | no | Measures the GT-anchoring bias: below 0.55 every TP comes from a reviewer *missed mark*, never a reviewed detection, so sub-0.55 precision is a lower bound by construction (#54/#55). | +| `low_floor_sweep.py corrected` | no | Applies the committed #55 A/B tags → corrected P/R per split and pooled, with an uncertainty band. | +| `low_floor_sweep.py floor` | no | Does the labeler's `DETECTION_STORAGE_FLOOR = 0.1` discard recoverable ramps? (Yes — 2.7% of GT.) Plus the recall **ceiling** on multi-view consensus. | +| `low_floor_sweep.py distance` | no | Where the recall gain from a lower threshold lands on the distance axis (uniform — so it stacks with multi-view rather than overlapping it). | +| `low_floor_sweep.py tagcheck` | no | Do the committed #55 tags still resolve against this cache? Tag ids are keyed to peak *coordinates*, so a re-extraction can silently orphan reviewer work. | +| `plot_operating_point.py` | no | The headline figure: PR response per split + F1-vs-threshold → `docs/figures/operating_point_pr.png`. | +| `plot_storage_floor.py` | no | Storage-floor cost + recall ceiling → `docs/figures/storage_floor_ceiling.png`. | + +`run_low_floor_extract.slurm` is the Hyak launcher for the one GPU step (one L40S, ~41 min for +1,625 panos across all seven splits); it is resumable, skipping splits that already have a cache. The GPU scripts reproduce the deployment inference path exactly (resize 2048×4096 bilinear, ImageNet norm, no TTA — see `sidewalk-auto-labeler/detectors/curb_ramp.py`), so @@ -33,6 +61,15 @@ ImageNet norm, no TTA — see `sidewalk-auto-labeler/detectors/curb_ramp.py`), s peak's height as its confidence, so a single inference pass yields the whole continuous curve + AP. Its `curve`/`gallery` steps are CPU-only and read the cache `extract` writes. +`low_floor_sweep.py` is the **cross-split** layer on that same cache — pooling, per-tier grouping, +calibration, the GT-anchoring measurement, the #55 correction and the storage-floor check. Run +`parity` before trusting anything else: it is the gate that catches a preprocessing divergence, +which every downstream number would otherwise silently inherit. The five Mapillary splits +reproduce their committed records bit-exactly; bend does not, and that is expected rather than a +failure — it is the only GSV split, and the GSV production path fed the model a 4096×2048 +intermediate rather than the native-res bundle pano. `manual_gold` is exempt from the gate +entirely (its committed detections used flip-TTA). + Model weights load from the published HF artifact **by state_dict**, matching the deployment inference path (not `AutoModel`); the pure scoring logic lives in `rampnet/detection_eval.py` and `rampnet/metrics.py` and is unit-tested in `tests/test_operating_point_curve.py`. diff --git a/scripts/analysis/low_floor_sweep.py b/scripts/analysis/low_floor_sweep.py new file mode 100644 index 0000000..13eccbf --- /dev/null +++ b/scripts/analysis/low_floor_sweep.py @@ -0,0 +1,1137 @@ +"""Cross-split low-floor operating-point sweep (issue #54, with #55 riding on it). + +``operating_point_curve.py`` produces the low-floor extraction and the per-city PR +curve. This module is the **cross-split** layer on top of that same cache: it sweeps +every benchmark split on one threshold grid, pools splits by *imagery tier* rather +than by city, and reports the things a deployment recommendation needs that a +per-city PR curve does not give you: + +1. **Detections per pano** at every threshold. A recall gain is only worth having if + the detector stays sparse. ``docs/model_comparison.md``'s own null-recall finding + is that an open detector's recall is largely *density* (OWLv2 at 55-88 boxes/pano), + so reporting boxes/pano at each candidate operating point forecloses that + objection against RampNet instead of inviting it. +2. **Per-tier curves.** Precision tracks the camera across the US Mapillary splits + (``benchmark/README.md``), so one uniform threshold is the wrong shape of answer. + Tiers are assigned **per pano** from ``camera_make``/``camera_model`` in + ``records.jsonl``, not per split — richmond alone mixes iSTAR Pulsar and GoPro Max, + so split-level grouping would smear the very effect being measured. +3. **A confidence calibration table** — empirical P(real | confidence bin) with Wilson + intervals. That is what a multi-view promotion floor actually selects on + (sidewalk-auto-labeler#27 stage 4). + +Everything here is CPU-only and reads the cache ``operating_point_curve.py extract`` +writes, so every number re-derives without a GPU. + +**Two splits are swept but held out of the pooled/tier rows** (each overridable): + +- ``budapest_district5`` — single-rater GT at low reviewer confidence; the merged + ``docs/model_comparison.md`` says not to pool it into recommendations. It is still + swept per split, because an out-of-distribution split is exactly where a threshold + recommendation is most likely to break. +- ``manual_gold`` — in-distribution GSV from the training cities, and its GT is + independent manual labeling rather than a RampNet review. Pooling it with the + deployment cities would mix a train-distribution reference into a deployment + recommendation; it is reported alongside as the in-domain reference it is. + +Run order (after ``operating_point_curve.py extract``): + + python scripts/analysis/low_floor_sweep.py parity # gate: cache reproduces records.jsonl + python scripts/analysis/low_floor_sweep.py sweep # per-split + pooled + per-tier curves + python scripts/analysis/low_floor_sweep.py hist # confidence calibration for labeler#27 + python scripts/analysis/low_floor_sweep.py distance # where the recall gain lands +""" +import argparse +import csv +import json +import math +import os +import sys + +REPO = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +OUT = os.environ.get("RAMPNET_ANALYSIS_OUT", os.path.join(REPO, "analysis_out")) +sys.path.insert(0, REPO) +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) + +from rampnet.detection_eval import ( # noqa: E402 + PANO_SCALE_X, PANO_SCALE_Y, aggregate, radius_sq_for, score_pano) +from rampnet.metrics import greedy_match # noqa: E402 +from rampnet.validation import wilson_interval # noqa: E402 + +from operating_point_curve import ( # noqa: E402 + DEPLOYED_THRESHOLD, classify_predictions, read_cache) + +# The five US/VA city splits carry verdict-grade GT and are the recommendation's basis. +US_SPLITS = ("richmond", "bend", "clovis", "morgantown", "annapolis") +CITY_SPLITS = US_SPLITS + ("budapest_district5",) +ALL_SPLITS = CITY_SPLITS + ("manual_gold",) + +# Why a split is swept but not pooled. Printed with the results so an omission can +# never be mistaken for a withheld result. +HELD_OUT = { + "budapest_district5": "single-rater GT at low reviewer confidence " + "(docs/model_comparison.md: do not pool)", + "manual_gold": "in-distribution GSV + independently-labelled GT " + "(in-domain reference, not a deployment city)", +} + +# Splits whose records.jsonl predates the camera-provenance fields but whose imagery +# source is known from the bundle. Everything else reads its provenance per pano. +SPLIT_IMAGERY_FALLBACK = {"bend": "gsv", "manual_gold": "gsv"} + +# manual_gold's committed detections were exported WITH horizontal-flip TTA and at a +# 0.05 floor (benchmark/manual_gold/detections_meta.json), unlike the city splits +# (no TTA, 0.55 floor). A 1:1 parity check against them is therefore the wrong test — +# it would be measuring TTA, not preprocessing drift. See cmd_parity. +TTA_RECORD_SPLITS = {"manual_gold"} + +CACHE_DIR = os.path.join(OUT, "op_cache") + +# Flat-ground distance estimate, identical to scripts/analysis/precision_by_distance.py +# (validated there against Depth-Anything-3 depth). The camera height cancels out of +# every *comparison* — it rescales all distances by one factor — so it sets the metre +# labels, not the conclusions. See benchmark/README.md's annapolis section. +CAM_H = 2.5 + + +# --------------------------------------------------------------------------- # +# Pure core (no I/O) — unit-tested in tests/test_low_floor_sweep.py +# --------------------------------------------------------------------------- # +def threshold_grid(floor=0.05, top=0.90, fine_lo=0.20, fine_hi=0.50, + coarse_step=0.05, fine_step=0.01): + """Sweep grid: coarse everywhere, fine through the candidate band. + + The recommendation lives in 0.20-0.50, so that band is swept at 0.01 while the + tails stay at 0.05 — a uniform fine grid would quadruple the rows to add + resolution where nothing is being decided. Values are rounded to 4 dp so the + grid is exact and dedupes cleanly at the band edges. + """ + if floor > top: + return [] + vals = set() + + def add_range(lo, hi, step): + if hi < lo: + return + n = int(round((hi - lo) / step)) + for i in range(n + 1): + v = round(lo + i * step, 4) + if floor - 1e-9 <= v <= top + 1e-9: + vals.add(v) + + add_range(floor, top, coarse_step) + add_range(max(floor, fine_lo), min(top, fine_hi), fine_step) + return sorted(vals) + + +def tier_of(camera_make, camera_model, source): + """Imagery tier for one pano, from its camera provenance. + + Grouping is by *rig class*, which is what the precision differences in + ``benchmark/README.md`` actually track — not by city and not by vendor. A pano + whose provenance is absent is ``unknown`` rather than being folded into a + neighbouring tier; unknowns are reported, never quietly pooled. + """ + src = (source or "").strip().lower() + if src in ("launch", "gsv", "google"): + return "gsv" + make = (camera_make or "").strip().lower() + model = (camera_model or "").strip().lower() + if make in ("", "none") and model in ("", "none"): + return "unknown" + if "trimble" in make or "trimble" in model: + return "survey" # Trimble MX7, vehicle-mounted survey rig + if "nctech" in make or "istar" in model: + return "pro360" # NCTECH iSTAR Pulsar + if "fusion" in model: + return "action-legacy" # GoPro Fusion, 2018-era + if "max" in model: + return "action-modern" # GoPro Max, 2024-era + return "unknown" + + +TIER_LABEL = { + "survey": "survey-grade (Trimble MX7)", + "pro360": "pro 360 (NCTECH iSTAR Pulsar)", + "action-modern": "action cam, modern (GoPro Max)", + "action-legacy": "action cam, legacy (GoPro Fusion 2018)", + "gsv": "Google Street View", + "unknown": "provenance absent", +} + + +def sweep_rows(panos, grid, radius_sq): + """P/R/F1 + density at each threshold over a pano list. + + ``dets_per_pano`` counts every kept prediction — TP, FP *and* the ones the + reviewer's `unsure` marks make the scorer ignore. Ignored detections are + invisible to P/R but a human still has to look at them, so leaving them out + would understate exactly the review burden this column exists to measure. + """ + rows = [] + n_panos = len(panos) or 1 + for thr in grid: + scores, kept = [], 0 + for pd in panos: + preds = [p for p in pd["preds"] if p[2] >= thr] + kept += len(preds) + scores.append(score_pano(preds, pd["gt"], radius_sq=radius_sq)) + rep = aggregate(scores) + rows.append({ + "threshold": thr, + "precision": rep.precision, "recall": rep.recall, "f1": rep.f1, + "precision_lo": rep.precision_ci[0], "precision_hi": rep.precision_ci[1], + "recall_lo": rep.recall_ci[0], "recall_hi": rep.recall_ci[1], + "tp": rep.tp, "fp": rep.fp, "fn": rep.fn, "ignored": rep.ignored, + "dets_per_pano": kept / n_panos, + "n_panos": len(panos), + }) + return rows + + +def best_f1_row(rows): + """F1-optimal row; ties break toward the *higher* threshold. + + A tie broken downward would silently recommend more false positives for no F1 + gain, which is the opposite of what a tie means. + """ + return max(rows, key=lambda r: (r["f1"], r["threshold"])) + + +def row_at(rows, threshold): + """The swept row nearest a threshold (the grid may not contain it exactly).""" + return min(rows, key=lambda r: abs(r["threshold"] - threshold)) + + +def highest_threshold_meeting(rows, min_precision): + """Lowest threshold whose precision still clears ``min_precision``. + + Recall-first: among the operating points that satisfy a precision floor, the + best one is the *lowest*, because recall is monotonically non-increasing in the + threshold. Returns None when no swept point clears the floor — a real answer + ("this split cannot meet that bar at any threshold"), not an error. + """ + ok = [r for r in rows if r["precision"] >= min_precision] + return min(ok, key=lambda r: r["threshold"]) if ok else None + + +def confidence_calibration(panos, radius_sq, bin_edges): + """Empirical P(real | confidence bin) — the GT-true vs GT-false histogram. + + This is the artifact sidewalk-auto-labeler#27 stage 4 consumes: a multi-view + promotion floor chooses a confidence at which a single-view detection is + trustworthy enough to promote, and that decision needs the *observed* hit rate + per bin, not the score distribution. + + ``ignore``-outcome predictions (inside an `unsure` mark) are excluded from both + counts, mirroring the scorer — the reviewer could not call them, so they can + neither confirm nor refute a bin. Wilson intervals come along because the top + bins are thin and a bare ratio there invites over-reading. + """ + bins = [{"lo": lo, "hi": hi, "n_true": 0, "n_false": 0} + for lo, hi in zip(bin_edges[:-1], bin_edges[1:])] + top = bin_edges[-1] + for pd in panos: + for _x, _y, score, outcome, _redundant in classify_predictions( + pd["preds"], pd["gt"], radius_sq): + if outcome == "ignore": + continue + for b in bins: + # the last bin is closed on the right so a score of exactly 1.0 lands + if b["lo"] <= score < b["hi"] or (b["hi"] == top and score >= top): + b["n_true" if outcome == "tp" else "n_false"] += 1 + break + for b in bins: + n = b["n_true"] + b["n_false"] + b["n"] = n + b["precision"] = b["n_true"] / n if n else None + b["ci"] = wilson_interval(b["n_true"], n) if n else (None, None) + return bins + + +def ground_distance(y, cam_h=CAM_H): + """Flat-ground distance for a point at normalized elevation ``y``. + + An equirectangular pano maps the vertical axis linearly to elevation, so a point + at ``y`` sits ``(y - 0.5) * 180`` degrees below the horizon, and a camera at + height ``cam_h`` sees flat ground there at ``cam_h / tan(theta)``. Strictly + monotonic in ``y``, so anything expressed as a rank over distance survives any + monotonic distance model — including a correct one. + """ + dep = (y - 0.5) * math.pi + return cam_h / math.tan(dep) if dep > 1e-4 else float("inf") + + +def matched_gt_at(panos, threshold, radius_sq): + """Which GT points are found at ``threshold``. + + Returns ``[(pano_id, gt_index, y, matched)]`` over recall-confirmed panos only — + the same gate ``aggregate`` applies to recall, so the strata below sum to the + swept recall rather than to a different denominator. + """ + out = [] + for pd in panos: + gt = pd["gt"] + if not gt.fn_confirmed: + continue + preds = sorted([p for p in pd["preds"] if p[2] >= threshold], + key=lambda p: p[2], reverse=True) + assignments = greedy_match([(p[0], p[1]) for p in preds], gt.gt_points, + radius_sq, PANO_SCALE_X, PANO_SCALE_Y) + hit = {gi for gi, _ in assignments if gi >= 0} + for i, (_gx, gy) in enumerate(gt.gt_points): + out.append((pd["pano"], i, gy, i in hit)) + return out + + +DISTANCE_BANDS = ((0.0, 12.5, "near (<12.5 m)"), + (12.5, 25.0, "mid (12.5-25 m)"), + (25.0, float("inf"), "far (>25 m)")) + + +def recall_by_distance(panos, radius_sq, low_thr, high_thr=DEPLOYED_THRESHOLD, + bands=DISTANCE_BANDS): + """Recall in each distance band at two thresholds — where the gain lands. + + ``benchmark/README.md`` establishes that RampNet's misses skew far-field. If + lowering the floor only recovers ramps that were already near, it adds little to + the multi-view case (labeler#27); if it recovers far ones, the two levers stack. + """ + hi = {(p, i): m for p, i, _y, m in matched_gt_at(panos, high_thr, radius_sq)} + lo = matched_gt_at(panos, low_thr, radius_sq) + rows = [] + for band_lo, band_hi, label in bands: + n = n_hi = n_lo = 0 + for pano, i, y, matched_lo in lo: + d = ground_distance(y) + if not (band_lo <= d < band_hi): + continue + n += 1 + n_hi += bool(hi.get((pano, i))) + n_lo += bool(matched_lo) + rows.append({ + "band": label, "n_gt": n, + "recall_high": n_hi / n if n else 0.0, + "recall_low": n_lo / n if n else 0.0, + "gained": n_lo - n_hi, + }) + return rows + + +# --------------------------------------------------------------------------- # +# I/O +# --------------------------------------------------------------------------- # +def load_records(city, repo=REPO): + """{pano_id: record} from a split's committed records.jsonl.""" + path = os.path.join(repo, "benchmark", city, "records.jsonl") + out = {} + with open(path, encoding="utf-8") as f: + for line in f: + if line.strip(): + r = json.loads(line) + out[r["pano"]["panorama_id"]] = r + return out + + +def load_split(city, cache_dir=CACHE_DIR, repo=REPO): + """Cached low-floor panos for a split, each tagged with its imagery tier.""" + panos, meta = read_cache(os.path.join(cache_dir, f"{city}.json")) + records = load_records(city, repo) + fallback = SPLIT_IMAGERY_FALLBACK.get(city) + for pd in panos: + p = records.get(pd["pano"], {}).get("pano", {}) + pd["tier"] = tier_of(p.get("camera_make"), p.get("camera_model"), + p.get("source") or fallback) + pd["city"] = city + return panos, meta + + +def _write_csv(path, rows, fields): + os.makedirs(os.path.dirname(path) or ".", exist_ok=True) + with open(path, "w", newline="", encoding="utf-8") as f: + w = csv.DictWriter(f, fieldnames=fields, extrasaction="ignore") + w.writeheader() + w.writerows(rows) + + +def pool_of(cities, include_budapest=False, include_gold=False): + """Which of ``cities`` contribute to the POOLED and per-tier rows.""" + keep = [] + for c in cities: + if c == "budapest_district5" and not include_budapest: + continue + if c == "manual_gold" and not include_gold: + continue + keep.append(c) + return keep + + +# --------------------------------------------------------------------------- # +# parity — the gate everything else inherits +# --------------------------------------------------------------------------- # +# A re-extraction is "the same detections" when nothing it moved could change a +# scoring outcome. The scorer matches within one radius R, so displacement is +# measured in radii, not pixels: half a radius is well inside the tolerance that +# decides TP-vs-FP, while being far tighter than any real preprocessing break. +PARITY_TOL_RADII = 0.5 +PARITY_MIN_MATCHED = 0.95 # fraction of cache detections that must land within tol +PARITY_MAX_COUNT_DELTA = 0.05 + + +def parity_for(panos, records, threshold=DEPLOYED_THRESHOLD, + tol_radii=PARITY_TOL_RADII, radius_sq=None): + """Do the cache's peaks at the deployed threshold reproduce records.jsonl? + + Bit-exactness is the wrong bar and would fail for a benign reason. A split whose + production run assembled imagery into a 4096x2048 intermediate (the GSV path — + bend) fed the model a *different image* than this cache's native-res downsample, + so its peaks land a heatmap cell or two away. A split whose production run + resized once from native res (the Mapillary path) reproduces exactly. + + So the gate asks the question that actually matters downstream: **could any of + this drift change a scoring outcome?** Displacements are therefore measured in + match radii — the unit the scorer works in — and a detection that moved less + than half a radius is the same detection for every number in this analysis. + + A *gross* mismatch (detections appearing or vanishing, or moving a full radius) + means preprocessing genuinely diverged, and every number downstream inherits it. + """ + if radius_sq is None: + radius_sq = radius_sq_for() + r = math.sqrt(radius_sq) + n_cache = n_rec = matched = exact = 0 + disps = [] + per_pano_mismatch = [] + for pd in panos: + rec = [(d["x_normalized"], d["y_normalized"]) + for d in records[pd["pano"]]["detections"] + if d["confidence"] >= threshold] + got = [(x, y) for x, y, s in pd["preds"] if s >= threshold] + n_cache += len(got) + n_rec += len(rec) + if len(got) != len(rec): + per_pano_mismatch.append((pd["pano"], len(rec), len(got))) + remaining = list(rec) + for gx, gy in got: + if not remaining: + continue + j, _ = min(enumerate(remaining), + key=lambda t: ((gx - t[1][0]) * PANO_SCALE_X) ** 2 + + ((gy - t[1][1]) * PANO_SCALE_Y) ** 2) + dist = math.hypot((gx - remaining[j][0]) * PANO_SCALE_X, + (gy - remaining[j][1]) * PANO_SCALE_Y) / r + if dist <= tol_radii: + matched += 1 + exact += dist == 0.0 + disps.append(dist) + remaining.pop(j) + count_delta = abs(n_cache - n_rec) / n_rec if n_rec else 0.0 + matched_frac = matched / n_cache if n_cache else 0.0 + disps.sort() + return { + "n_records": n_rec, "n_cache": n_cache, "matched": matched, "exact": exact, + "exact_frac": exact / n_cache if n_cache else 0.0, + "matched_frac": matched_frac, + "count_delta": count_delta, + "median_displacement_r": disps[len(disps) // 2] if disps else 0.0, + "max_displacement_r": disps[-1] if disps else 0.0, + "panos_with_count_mismatch": per_pano_mismatch, + "ok": matched_frac >= PARITY_MIN_MATCHED and count_delta <= PARITY_MAX_COUNT_DELTA, + } + + +def cmd_parity(args): + all_ok = True + rows = [] + print(f"{'split':<22} {'records':>8} {'cache':>7} {'exact':>7} {'within tol':>11} " + f"{'med R':>7} {'max R':>7} verdict") + print("-" * 92) + for city in args.cities: + panos, _meta = load_split(city, args.cache) + res = parity_for(panos, load_records(city), args.threshold, args.tol_radii) + rows.append((city, res)) + gated = city not in TTA_RECORD_SPLITS + if gated: + all_ok &= res["ok"] + verdict = "OK" if res["ok"] else "MISMATCH" + else: + verdict = "n/a (TTA)" + print(f"{city:<22} {res['n_records']:>8} {res['n_cache']:>7} " + f"{res['exact_frac']:>6.1%} {res['matched_frac']:>10.1%} " + f"{res['median_displacement_r']:>7.3f} {res['max_displacement_r']:>7.3f} " + f"{verdict}") + if gated and not res["ok"]: + for pano, n_rec, n_got in res["panos_with_count_mismatch"][:5]: + print(f" {pano}: records {n_rec} vs cache {n_got}") + + print(f"\nDisplacements are in match radii (R); tolerance {args.tol_radii} R. " + f"'exact' = peak landed in the identical heatmap cell.") + for city, res in rows: + if city in TTA_RECORD_SPLITS: + print(f"\n{city}: NOT GATED — its committed detections were exported WITH " + f"horizontal-flip TTA\n (benchmark/{city}/detections_meta.json) at a " + f"0.05 floor, while this cache is the no-TTA\n deployment path. Its " + f"row is a TTA-vs-no-TTA delta, not preprocessing drift (issue #78).") + elif res["exact_frac"] < 0.99: + print(f"\n{city}: reproduces within tolerance but not exactly " + f"({res['exact_frac']:.1%} identical cells).\n Expected where the " + f"production run fed the model a different resample of the same pano " + f"—\n the GSV path built a 4096x2048 intermediate, so bundle " + f"native-res != production input.\n Every displacement is under " + f"{res['max_displacement_r']:.2f} R, so no scoring outcome changes.") + print("\nParity gate: " + ("PASS — the cache reproduces the committed detections." + if all_ok else + "FAIL — preprocessing diverged; downstream numbers " + "inherit it.")) + return 0 if all_ok else 1 + + +# --------------------------------------------------------------------------- # +# sweep +# --------------------------------------------------------------------------- # +SWEEP_FIELDS = ["group", "threshold", "precision", "recall", "f1", "precision_lo", + "precision_hi", "recall_lo", "recall_hi", "tp", "fp", "fn", "ignored", + "dets_per_pano", "n_panos"] + +DISPLAY_THRESHOLDS = (0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, + 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90) + + +def _print_rows(title, rows, marks): + """Print the coarse rows plus anything notable; the CSV keeps the full grid.""" + print(f"\n{'=' * 88}\n{title}\n{'=' * 88}") + print(f"{'thr':>5} {'P':>7} {'R':>7} {'F1':>7} {'dets/pano':>10} " + f"{'tp/fp/fn':>18} note") + print("-" * 88) + best = best_f1_row(rows) + for r in rows: + note = [] + if abs(r["threshold"] - DEPLOYED_THRESHOLD) < 1e-9: + note.append("deployed") + if r is best: + note.append("F1-max") + if any(abs(r["threshold"] - m) < 1e-9 for m in marks): + note.append("candidate") + if not note and not any(abs(r["threshold"] - d) < 1e-9 + for d in DISPLAY_THRESHOLDS): + continue + counts = f'{r["tp"]}/{r["fp"]}/{r["fn"]}' + print(f"{r['threshold']:>5.2f} {r['precision']:>7.3f} {r['recall']:>7.3f} " + f"{r['f1']:>7.3f} {r['dets_per_pano']:>10.2f} " + f"{counts:>18} {' '.join(note)}") + dep = row_at(rows, DEPLOYED_THRESHOLD) + print(f" deployed {dep['threshold']:.2f} -> F1-max {best['threshold']:.2f}: " + f"recall {dep['recall']:.3f} -> {best['recall']:.3f} " + f"({best['recall'] - dep['recall']:+.3f}), " + f"precision {dep['precision']:.3f} -> {best['precision']:.3f} " + f"({best['precision'] - dep['precision']:+.3f}), " + f"density {dep['dets_per_pano']:.2f} -> {best['dets_per_pano']:.2f}/pano") + + +def cmd_sweep(args): + radius_sq = radius_sq_for() + grid = threshold_grid(args.floor, args.top) + loaded = {city: load_split(city, args.cache)[0] for city in args.cities} + all_rows = [] + + for city, panos in loaded.items(): + rows = sweep_rows(panos, grid, radius_sq) + for r in rows: + r["group"] = city + all_rows += rows + held = HELD_OUT.get(city) + suffix = f" [held out of POOLED: {held}]" if held else "" + _print_rows(f"{city.upper()} (n={len(panos)} panos){suffix}", rows, args.mark) + + poolable = pool_of(args.cities, args.include_budapest, args.include_gold) + pooled = [pd for c in poolable for pd in loaded[c]] + if len(poolable) > 1: + rows = sweep_rows(pooled, grid, radius_sq) + for r in rows: + r["group"] = "POOLED" + all_rows += rows + _print_rows(f"POOLED ({', '.join(poolable)}; n={len(pooled)} panos)", + rows, args.mark) + + by_tier = {} + for pd in pooled: + by_tier.setdefault(pd["tier"], []).append(pd) + for tier, panos in sorted(by_tier.items(), key=lambda kv: -len(kv[1])): + rows = sweep_rows(panos, grid, radius_sq) + for r in rows: + r["group"] = f"tier:{tier}" + all_rows += rows + cities = sorted({pd["city"] for pd in panos}) + _print_rows(f"TIER {TIER_LABEL.get(tier, tier)} " + f"(n={len(panos)} panos from {', '.join(cities)})", rows, args.mark) + + path = os.path.join(args.out, "low_floor_sweep.csv") + _write_csv(path, all_rows, SWEEP_FIELDS) + print(f"\nwrote {path} ({len(all_rows)} rows, full grid)") + for city in args.cities: + if city not in poolable: + print(f"held out of POOLED/tier rows: {city} — {HELD_OUT[city]}") + return 0 + + +# --------------------------------------------------------------------------- # +# hist +# --------------------------------------------------------------------------- # +def _print_bins(title, bins): + print(f"\n{'=' * 76}\n{title}\n{'=' * 76}") + print(f"{'bin':>14} {'GT-true':>8} {'GT-false':>9} {'P(real)':>8} 95% CI") + print("-" * 76) + for b in bins: + if not b["n"]: + continue + label = f'{b["lo"]:.2f}-{b["hi"]:.2f}' + ci = f"[{b['ci'][0]:.3f}, {b['ci'][1]:.3f}]" + print(f"{label:>14} {b['n_true']:>8} {b['n_false']:>9} " + f"{b['precision']:>8.3f} {ci}") + + +def cmd_hist(args): + radius_sq = radius_sq_for() + n_bins = int(round((1.0 - args.floor) / args.bin_width)) + edges = [round(args.floor + i * args.bin_width, 4) for i in range(n_bins + 1)] + payload = {"bin_edges": edges, "splits": {}} + loaded = {city: load_split(city, args.cache)[0] for city in args.cities} + + for city, panos in loaded.items(): + bins = confidence_calibration(panos, radius_sq, edges) + payload["splits"][city] = bins + _print_bins(f"{city.upper()} — P(real | confidence bin)", bins) + + poolable = pool_of(args.cities, args.include_budapest, args.include_gold) + pooled = [pd for c in poolable for pd in loaded[c]] + pooled_bins = confidence_calibration(pooled, radius_sq, edges) + payload["pooled"] = {"splits": poolable, "bins": pooled_bins} + _print_bins(f"POOLED ({', '.join(poolable)}) — P(real | confidence bin)", pooled_bins) + + os.makedirs(args.out, exist_ok=True) + path = os.path.join(args.out, "confidence_calibration.json") + with open(path, "w", encoding="utf-8") as f: + json.dump(payload, f, indent=2) + flat = [{"split": split, "lo": b["lo"], "hi": b["hi"], "n_true": b["n_true"], + "n_false": b["n_false"], "precision": b["precision"], + "ci_lo": b["ci"][0], "ci_hi": b["ci"][1]} + for split, bins in list(payload["splits"].items()) + [("POOLED", pooled_bins)] + for b in bins] + _write_csv(os.path.join(args.out, "confidence_calibration.csv"), flat, + ["split", "lo", "hi", "n_true", "n_false", "precision", "ci_lo", "ci_hi"]) + print(f"\nwrote {path} + confidence_calibration.csv") + return 0 + + +# --------------------------------------------------------------------------- # +# tagcheck — do the committed #55 tags still resolve after a re-extraction? +# --------------------------------------------------------------------------- # +def tags_path_for(city, repo=REPO): + return os.path.join(repo, "benchmark", city, "incremental_fp_tags.json") + + +def check_tag_resolution(items, tags): + """Which committed A/B tags still point at an incremental FP in this cache. + + Tag ids are ``{pano}_{x:.5f}_{y:.5f}`` (see ``incremental_fps``), so they are + keyed to peak *coordinates*. Re-extracting on different hardware can move a + marginal peak by a heatmap cell and orphan its tag — silently, since an + unresolved tag just stops contributing to the correction and quietly shrinks it. + + Returns the resolved/orphaned split plus the orphans themselves, so a + re-extraction can never quietly discard reviewer effort. + """ + ids = {it["id"] for it in items} + resolved = sorted(t for t in tags if t in ids) + orphaned = sorted(t for t in tags if t not in ids) + return { + "n_tags": len(tags), "n_items": len(items), + "resolved": resolved, "orphaned": orphaned, + "resolved_frac": len(resolved) / len(tags) if tags else 1.0, + "untagged": sorted(ids - set(tags)), + } + + +def cmd_tagcheck(args): + from operating_point_curve import incremental_fps + radius_sq = radius_sq_for() + all_ok = True + for city in args.cities: + path = tags_path_for(city) + if not os.path.exists(path): + continue + with open(path, encoding="utf-8") as f: + tags = json.load(f) + panos, _ = load_split(city, args.cache) + items = incremental_fps(panos, args.op_threshold, args.upper, radius_sq) + res = check_tag_resolution(items, tags) + ok = res["resolved_frac"] >= args.min_resolved + all_ok &= ok + print(f"\n{city}: {len(res['resolved'])}/{res['n_tags']} committed tags resolve " + f"({res['resolved_frac']:.1%}) — {res['n_items']} incremental FPs in " + f"[{args.op_threshold}, {args.upper}) {'OK' if ok else 'DEGRADED'}") + for t in res["orphaned"][:10]: + print(f" orphaned: {t}") + if res["untagged"]: + print(f" {len(res['untagged'])} incremental FP(s) in this cache carry no " + f"tag — they widen the corrected band rather than being assumed real.") + print("\nTag resolution: " + ("PASS — re-extraction preserved the reviewer's work." + if all_ok else + "DEGRADED — some tags no longer match a detection; the " + "#55 correction would silently shrink. Re-tag the " + "orphans or keep the cache they were made against.")) + return 0 if all_ok else 1 + + +# --------------------------------------------------------------------------- # +# floor — does a 0.1 storage floor throw away recoverable ramps? (labeler#28/#27) +# --------------------------------------------------------------------------- # +def gt_best_candidate(panos, radius_sq, floor=0.0): + """Per GT ramp, the confidence of the detection that claims it — or None. + + Greedy matching is highest-confidence-first, so dropping low-confidence + predictions never changes what a *higher*-confidence one matched. That makes + this one pass sufficient for every floor at once: the set of GT ramps still + recoverable at floor ``f`` is exactly those whose best candidate scores ``>= f``. + + Restricted to recall-confirmed panos, the same gate ``aggregate`` applies, so + these counts share a denominator with the swept recall. + """ + out = [] + for pd in panos: + gt = pd["gt"] + if not gt.fn_confirmed: + continue + preds = sorted([p for p in pd["preds"] if p[2] >= floor], + key=lambda p: p[2], reverse=True) + assignments = greedy_match([(p[0], p[1]) for p in preds], gt.gt_points, + radius_sq, PANO_SCALE_X, PANO_SCALE_Y) + best = {} + for p, (gi, _) in zip(preds, assignments): + if gi >= 0: + best[gi] = p[2] + for i in range(len(gt.gt_points)): + out.append(best.get(i)) + return out + + +STORAGE_FLOOR = 0.10 # sidewalk-auto-labeler DETECTION_STORAGE_FLOOR (PR #28) +STORAGE_TOP_K = 50 # its per-pano cap + + +def floor_report(panos, radius_sq, bands=((0.05, 0.10), (0.10, 0.20)), + floors=(0.05, 0.10, 0.20, DEPLOYED_THRESHOLD)): + """Recall ceiling at each candidate floor, plus where the marginal ramps sit. + + Two questions, one pass: + + - **Is a 0.1 storage floor safe?** ``bands`` counts GT ramps whose *best* + candidate falls in each band. The count in ``[0.05, 0.10)`` is exactly the + number of real ramps a 0.1 floor makes permanently unrecoverable — no + downstream consensus policy can promote a candidate that was never stored. + - **What is the ceiling on multi-view promotion?** ``recall_at`` is the share of + GT ramps with any candidate at or above each floor. Stage 4 of labeler#27 + cannot exceed this, whatever k it requires. + """ + best = gt_best_candidate(panos, radius_sq) + n_gt = len(best) + matched = [b for b in best if b is not None] + return { + "n_gt": n_gt, + "n_unmatched": n_gt - len(matched), + "bands": {f"[{lo:.2f},{hi:.2f})": sum(1 for b in matched if lo <= b < hi) + for lo, hi in bands}, + "recall_at": {f"{f:.2f}": sum(1 for b in matched if b >= f) / n_gt if n_gt else 0.0 + for f in floors}, + "n_at": {f"{f:.2f}": sum(1 for b in matched if b >= f) for f in floors}, + } + + +def cap_report(panos, floor=STORAGE_FLOOR, top_k=STORAGE_TOP_K): + """Does the labeler's per-pano top-K cap bind at this storage floor? + + The cap is the real volume bound in labeler#28, so it is worth knowing whether + it ever actually truncates — a cap that never binds costs nothing, one that + binds often is silently a second, harsher floor. + """ + counts = sorted(sum(1 for p in pd["preds"] if p[2] >= floor) for pd in panos) + n = len(counts) or 1 + return { + "median": counts[n // 2] if counts else 0, + "p95": counts[min(n - 1, int(0.95 * n))] if counts else 0, + "max": counts[-1] if counts else 0, + "n_over_cap": sum(1 for c in counts if c > top_k), + "n_panos": len(counts), + } + + +def cmd_floor(args): + radius_sq = radius_sq_for() + loaded = {city: load_split(city, args.cache)[0] for city in args.cities} + rows = [] + band_keys = [f"[{lo:.2f},{hi:.2f})" for lo, hi in ((0.05, 0.10), (0.10, 0.20))] + + print(f"{'split':<22} {'GT':>5} {band_keys[0]:>13} {band_keys[1]:>13} " + f"{'R@0.05':>7} {'R@0.10':>7} {'R@0.55':>7} {'lost@0.10':>10}") + print("-" * 96) + + def emit(label, panos): + rep = floor_report(panos, radius_sq) + lost = rep["bands"][band_keys[0]] + print(f"{label:<22} {rep['n_gt']:>5} {rep['bands'][band_keys[0]]:>13} " + f"{rep['bands'][band_keys[1]]:>13} {rep['recall_at']['0.05']:>7.3f} " + f"{rep['recall_at']['0.10']:>7.3f} {rep['recall_at']['0.55']:>7.3f} " + f"{lost / rep['n_gt'] if rep['n_gt'] else 0:>9.2%}") + rows.append({"split": label, "n_gt": rep["n_gt"], + "gt_best_in_005_010": rep["bands"][band_keys[0]], + "gt_best_in_010_020": rep["bands"][band_keys[1]], + **{f"recall_at_{k}": v for k, v in rep["recall_at"].items()}, + **{f"n_at_{k}": v for k, v in rep["n_at"].items()}}) + return rep + + for city, panos in loaded.items(): + emit(city, panos) + poolable = pool_of(args.cities, args.include_budapest, args.include_gold) + pooled_panos = [pd for c in poolable for pd in loaded[c]] + print("-" * 96) + pooled = emit("POOLED", pooled_panos) if len(poolable) > 1 else None + + print(f"\nPer-pano candidate counts at the {STORAGE_FLOOR} storage floor " + f"(cap = top {STORAGE_TOP_K}):") + print(f"{'split':<22} {'median':>7} {'p95':>6} {'max':>6} {'panos over cap':>16}") + print("-" * 62) + for city, panos in loaded.items(): + c = cap_report(panos, args.floor, args.top_k) + print(f"{city:<22} {c['median']:>7} {c['p95']:>6} {c['max']:>6} " + f"{c['n_over_cap']:>16}") + + _write_csv(os.path.join(args.out, "storage_floor.csv"), rows, + ["split", "n_gt", "gt_best_in_005_010", "gt_best_in_010_020", + "recall_at_0.05", "recall_at_0.10", "recall_at_0.20", "recall_at_0.55", + "n_at_0.05", "n_at_0.10", "n_at_0.20", "n_at_0.55"]) + if pooled: + lost = pooled["bands"][band_keys[0]] + print(f""" +{'=' * 88} +Verdict on DETECTION_STORAGE_FLOOR = {STORAGE_FLOOR} (labeler#28) +{'=' * 88} +Across the pooled US splits, {lost} of {pooled['n_gt']} ground-truth ramps +({lost / pooled['n_gt']:.2%}) have their best candidate in [0.05, 0.10) — those are the +ramps a {STORAGE_FLOOR} storage floor discards at the only point they exist. + +Recall ceiling: {pooled['recall_at']['0.10']:.3f} at the {STORAGE_FLOOR} floor, against +{pooled['recall_at']['0.05']:.3f} at the 0.05 extraction floor and {pooled['recall_at']['0.55']:.3f} +at the deployed threshold. **No labeler#27 stage-4 consensus policy can exceed the +first number**, whatever k it requires, because a candidate that was never stored +cannot be promoted.""") + print(f"\nwrote {os.path.join(args.out, 'storage_floor.csv')}") + return 0 + + +# --------------------------------------------------------------------------- # +# corrected — apply the #55 A/B tags, per split and pooled +# --------------------------------------------------------------------------- # +def cmd_corrected(args): + from operating_point_curve import (corrected_precision, corrected_recall, + f1_of, incremental_fps, _score_at) + radius_sq = radius_sq_for() + rows = [] + pooled_items, pooled_tags = [], {} + pooled = {"tp": 0, "fp": 0, "tp_recall": 0, "n_gt_recall": 0} + + print(f"{'split':<22} {'raw P':>7} {'corr P':>7} {'band hi':>8} " + f"{'raw R':>7} {'corr R':>7} {'raw F1':>7} {'corr F1':>8} A/B/U") + print("-" * 96) + for city in args.cities: + path = tags_path_for(city) + if not os.path.exists(path): + print(f"{city:<22} (no tags — not spot-checked)") + continue + with open(path, encoding="utf-8") as f: + tags = json.load(f) + panos, _ = load_split(city, args.cache) + items = incremental_fps(panos, args.op_threshold, args.upper, radius_sq) + rep = _score_at(panos, args.op_threshold, radius_sq) + p = corrected_precision(rep.tp, rep.fp, items, tags) + r = corrected_recall(rep.n_gt_recall - rep.fn, rep.n_gt_recall, items, tags) + counts = f"{p['n_A']}/{p['n_B']}/{p['n_U']}" + print(f"{city:<22} {p['uncorrected']:>7.3f} {p['corrected']:>7.3f} " + f"{p['band_high']:>8.3f} {r['uncorrected']:>7.3f} {r['corrected']:>7.3f} " + f"{f1_of(p['uncorrected'], r['uncorrected']):>7.3f} " + f"{f1_of(p['corrected'], r['corrected']):>8.3f} {counts}") + rows.append({"split": city, "op_threshold": args.op_threshold, + "precision_raw": p["uncorrected"], "precision_corrected": p["corrected"], + "precision_band_high": p["band_high"], + "recall_raw": r["uncorrected"], "recall_corrected": r["corrected"], + "n_A": p["n_A"], "n_B": p["n_B"], "n_U": p["n_U"], + "n_A_suspect": p["n_A_suspect"], "n_incremental": p["n_incremental"]}) + if p["n_A_suspect"]: + print(f"{'':<22} note: {p['n_A_suspect']} of {p['n_A']} A-tags sit within " + f"{2.0:g} R of an already-detected ramp (likely a second hit, not a " + f"missed ramp)") + if city in pool_of(args.cities, args.include_budapest, args.include_gold): + pooled_items += items + pooled_tags.update(tags) + pooled["tp"] += rep.tp + pooled["fp"] += rep.fp + pooled["tp_recall"] += rep.n_gt_recall - rep.fn + pooled["n_gt_recall"] += rep.n_gt_recall + + if pooled["tp"]: + p = corrected_precision(pooled["tp"], pooled["fp"], pooled_items, pooled_tags) + r = corrected_recall(pooled["tp_recall"], pooled["n_gt_recall"], + pooled_items, pooled_tags) + print("-" * 96) + print(f"{'POOLED':<22} {p['uncorrected']:>7.3f} {p['corrected']:>7.3f} " + f"{p['band_high']:>8.3f} {r['uncorrected']:>7.3f} {r['corrected']:>7.3f} " + f"{f1_of(p['uncorrected'], r['uncorrected']):>7.3f} " + f"{f1_of(p['corrected'], r['corrected']):>8.3f} " + f"{p['n_A']}/{p['n_B']}/{p['n_U']}") + rows.append({"split": "POOLED", "op_threshold": args.op_threshold, + "precision_raw": p["uncorrected"], "precision_corrected": p["corrected"], + "precision_band_high": p["band_high"], + "recall_raw": r["uncorrected"], "recall_corrected": r["corrected"], + "n_A": p["n_A"], "n_B": p["n_B"], "n_U": p["n_U"], + "n_A_suspect": p["n_A_suspect"], "n_incremental": p["n_incremental"]}) + print(f"\nA-rate pooled: {p['n_A']}/{p['n_incremental']} " + f"({p['n_A'] / p['n_incremental']:.1%}) of incremental FPs in " + f"[{args.op_threshold}, {args.upper}) were real ramps the GT missed.") + + _write_csv(os.path.join(args.out, f"corrected_at_{args.op_threshold:g}.csv"), rows, + ["split", "op_threshold", "precision_raw", "precision_corrected", + "precision_band_high", "recall_raw", "recall_corrected", + "n_A", "n_B", "n_U", "n_A_suspect", "n_incremental"]) + print("\nThe corrected column credits confirmed A tags only; 'band hi' additionally " + "credits\nunsure and untagged items, so it is the honest upper end rather than a " + "formality.") + return 0 + + +# --------------------------------------------------------------------------- # +# gtbias — why sub-0.55 precision is a lower bound, measured rather than asserted +# --------------------------------------------------------------------------- # +def gt_origins(city, repo=REPO): + """``{pano_id: ["reviewed"|"missed", ...]}`` aligned with each pano's gt_points. + + ``build_ground_truth`` appends the reviewer-confirmed *detections* first and the + reviewer's *missed-ramp marks* second, so the origin of each GT point is + recoverable from the same two files in that order. Recovering it is what turns + the GT-completeness caveat from an assertion into a measurement. + + Verdict-reviewed splits only — ``manual_gold`` has no verdicts because its GT + was labelled independently of RampNet, which is precisely why it is the control + (see :func:`cmd_gtbias`). + """ + with open(os.path.join(repo, "benchmark", city, "verdicts.json"), + encoding="utf-8") as f: + verdicts = json.load(f)["panos"] + out = {} + for pid, entry in verdicts.items(): + origins = ["reviewed" for v in entry["dets"] if v is True or v == "true"] + origins += ["missed" for m in entry["missed"] if not m.get("unsure")] + out[pid] = origins + return out + + +def _bin_lo(score, bin_width): + """Lower edge of ``score``'s bin, immune to binary-float floor errors. + + ``0.9 // 0.1`` is 8.0, not 9.0, because 0.9/0.1 evaluates to 8.999...; nudging + the quotient by an epsilon before flooring fixes it. A score of exactly 1.0 + belongs to the top bin, not to a phantom bin above it, so the index is clamped. + """ + top_index = math.ceil(round(1.0 / bin_width, 6)) - 1 + index = min(math.floor(score / bin_width + 1e-9), top_index) + return round(max(index, 0) * bin_width, 4) + + +def tp_origin_by_bin(panos, origins, radius_sq, bin_width=0.1): + """True positives per confidence bin, split by where their GT point came from. + + The load-bearing observation: the city splits' GT was assembled from detections + at or above the deployed 0.55 floor, so **below that floor a prediction can only + score as a TP if it lands on a ramp the reviewer independently marked as + missed**. Anything else it finds — including a real curb ramp — is counted as a + false positive because no human ever looked there. + + That makes sub-0.55 precision a lower bound with a *known mechanism* rather than + an unquantified worry, and it is why issue #55's A/B spot-check is the only way + to get a real number in that band. + """ + counts = {} + for pd in panos: + origin = origins.get(pd["pano"]) + if origin is None or len(origin) != len(pd["gt"].gt_points): + continue # re-review drift: skip rather than mis-attribute + preds = sorted(pd["preds"], key=lambda p: -p[2]) + assignments = greedy_match([(p[0], p[1]) for p in preds], pd["gt"].gt_points, + radius_sq, PANO_SCALE_X, PANO_SCALE_Y) + for p, (gi, _) in zip(preds, assignments): + if gi < 0: + continue + counts[(_bin_lo(p[2], bin_width), origin[gi])] = counts.get( + (_bin_lo(p[2], bin_width), origin[gi]), 0) + 1 + return counts + + +def cmd_gtbias(args): + radius_sq = radius_sq_for() + rows = [] + for city in args.cities: + if city in ("manual_gold",): + continue + panos, _ = load_split(city, args.cache) + counts = tp_origin_by_bin(panos, gt_origins(city), radius_sq, args.bin_width) + print(f"\n{'=' * 72}\n{city.upper()} — true positives by GT origin\n{'=' * 72}") + print(f"{'bin':>12} {'from reviewed det':>18} {'from missed mark':>17}") + print("-" * 72) + for b in sorted({k[0] for k in counts}): + rv = counts.get((b, "reviewed"), 0) + ms = counts.get((b, "missed"), 0) + rows.append({"split": city, "bin_lo": b, "from_reviewed": rv, + "from_missed": ms}) + flag = " <- below the 0.55 review floor" if b < DEPLOYED_THRESHOLD else "" + print(f"{b:>7.1f}-{b + args.bin_width:<4.1f} {rv:>18} {ms:>17}{flag}") + + _write_csv(os.path.join(args.out, "tp_origin_by_bin.csv"), rows, + ["split", "bin_lo", "from_reviewed", "from_missed"]) + print(f""" +{'=' * 72} +Reading this table +{'=' * 72} +Every true positive below {DEPLOYED_THRESHOLD} comes from a *missed mark*, and none from a +reviewed detection. That is structural, not a coincidence: the city splits' GT was +built from detections at or above the deployed floor, so in the band this sweep +opens up, a prediction can only be credited if a human independently flagged that +ramp during the missed-ramp pass. A real curb ramp nobody marked is counted as a +false positive. + +So sub-{DEPLOYED_THRESHOLD} precision on these splits is a **lower bound with a known +mechanism**, and the measured F1-optimal threshold is biased *high* — the true +optimum sits at or below it. Issue #55's A/B spot-check is what converts the bound +into a number. + +benchmark/manual_gold is the control: its GT was labelled independently of RampNet +(no verdict review, no anchoring), so its curve carries none of this bias at any +threshold. Compare its precision-vs-threshold shape against the city splits to see +the anchoring effect directly.""") + return 0 + + +# --------------------------------------------------------------------------- # +# distance +# --------------------------------------------------------------------------- # +def cmd_distance(args): + radius_sq = radius_sq_for() + rows_out = [] + for city in args.cities: + panos, _ = load_split(city, args.cache) + rows = recall_by_distance(panos, radius_sq, args.low, args.high) + print(f"\n{'=' * 78}\n{city.upper()} — recall by distance, " + f"{args.high:.2f} -> {args.low:.2f}\n{'=' * 78}") + print(f"{'band':>20} {'n_gt':>6} {'R@high':>8} {'R@low':>8} {'gain':>7} {'+ramps':>7}") + print("-" * 78) + for r in rows: + print(f"{r['band']:>20} {r['n_gt']:>6} {r['recall_high']:>8.3f} " + f"{r['recall_low']:>8.3f} " + f"{r['recall_low'] - r['recall_high']:>7.3f} {r['gained']:>7}") + rows_out.append({"split": city, **r}) + _write_csv(os.path.join(args.out, "recall_by_distance.csv"), rows_out, + ["split", "band", "n_gt", "recall_high", "recall_low", "gained"]) + print(f"\nwrote {os.path.join(args.out, 'recall_by_distance.csv')}") + print("Distances are the flat-ground estimate (camera height 2.5 m assumed). It is " + "monotonic in y,\nso the band ordering is a rank statement; only the metre " + "labels depend on the assumption.") + return 0 + + +# --------------------------------------------------------------------------- # +def _csv_list(s): + return tuple(c.strip() for c in s.split(",") if c.strip()) + + +def _floats(s): + return tuple(float(v) for v in s.split(",") if v.strip()) + + +def main(argv=None): + p = argparse.ArgumentParser( + description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + sub = p.add_subparsers(dest="cmd", required=True) + + def common(sp): + sp.add_argument("--cities", type=_csv_list, default=ALL_SPLITS) + sp.add_argument("--cache", default=CACHE_DIR) + sp.add_argument("--out", default=os.path.join(OUT, "op")) + sp.add_argument("--include-budapest", action="store_true", + help="pool budapest into POOLED/tier rows (default: held out — " + "single-rater low-confidence GT)") + sp.add_argument("--include-gold", action="store_true", + help="pool manual_gold into POOLED/tier rows (default: held out — " + "in-distribution reference, not a deployment city)") + + sp = sub.add_parser("parity", help="gate: cache at 0.55 must reproduce records.jsonl") + common(sp) + sp.add_argument("--threshold", type=float, default=DEPLOYED_THRESHOLD) + sp.add_argument("--tol-radii", type=float, default=PARITY_TOL_RADII, + help="displacement tolerance in match radii (default " + f"{PARITY_TOL_RADII}); a detection that moved less than this " + "cannot change a scoring outcome") + sp.set_defaults(func=cmd_parity) + + sp = sub.add_parser("sweep", help="per-split + pooled + per-tier P/R/F1 vs threshold") + common(sp) + sp.add_argument("--floor", type=float, default=0.05) + sp.add_argument("--top", type=float, default=0.90) + sp.add_argument("--mark", type=_floats, default=(0.25, 0.30, 0.35), + help="candidate operating points to flag in the printed table") + sp.set_defaults(func=cmd_sweep) + + sp = sub.add_parser("hist", help="GT-true vs GT-false confidence calibration (labeler#27)") + common(sp) + sp.add_argument("--floor", type=float, default=0.05) + sp.add_argument("--bin-width", type=float, default=0.05) + sp.set_defaults(func=cmd_hist) + + sp = sub.add_parser("tagcheck", + help="do the committed #55 A/B tags still resolve in this cache?") + common(sp) + sp.add_argument("--op-threshold", type=float, default=0.25) + sp.add_argument("--upper", type=float, default=DEPLOYED_THRESHOLD) + sp.add_argument("--min-resolved", type=float, default=1.0, + help="fraction of committed tags that must still resolve (default 1.0 " + "— any orphan is reviewer effort silently dropped)") + sp.set_defaults(func=cmd_tagcheck) + + sp = sub.add_parser("floor", + help="storage-floor validation + recall ceiling (labeler#28/#27)") + common(sp) + sp.add_argument("--floor", type=float, default=STORAGE_FLOOR) + sp.add_argument("--top-k", type=int, default=STORAGE_TOP_K) + sp.set_defaults(func=cmd_floor) + + sp = sub.add_parser("corrected", + help="apply the #55 A/B tags -> corrected P/R, per split and pooled") + common(sp) + sp.add_argument("--op-threshold", type=float, default=0.35) + sp.add_argument("--upper", type=float, default=DEPLOYED_THRESHOLD) + sp.set_defaults(func=cmd_corrected) + + sp = sub.add_parser("gtbias", help="measure the GT-anchoring bias below the review floor") + common(sp) + sp.add_argument("--bin-width", type=float, default=0.1) + sp.set_defaults(func=cmd_gtbias) + + sp = sub.add_parser("distance", help="where the recall gain lands on the distance axis") + common(sp) + sp.add_argument("--low", type=float, default=0.30) + sp.add_argument("--high", type=float, default=DEPLOYED_THRESHOLD) + sp.set_defaults(func=cmd_distance) + + args = p.parse_args(argv) + os.makedirs(args.out, exist_ok=True) + return args.func(args) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/analysis/operating_point_curve.py b/scripts/analysis/operating_point_curve.py index e38117b..dd2003f 100644 --- a/scripts/analysis/operating_point_curve.py +++ b/scripts/analysis/operating_point_curve.py @@ -58,10 +58,11 @@ from rampnet.detection_eval import ( # noqa: E402 PANO_SCALE_X, PANO_SCALE_Y, GroundTruth, _xy, aggregate, build_ground_truth, - prediction_confidence, radius_sq_for, score_pano) + load_yolo_ground_truths, prediction_confidence, radius_sq_for, score_pano) from rampnet.metrics import greedy_match # noqa: E402 DEFAULT_CITIES = ("richmond", "bend") +HF_MODEL_REPO = "projectsidewalk/rampnet-model" DEPLOYED_THRESHOLD = 0.55 DEFAULT_SCORE_FLOOR = 0.05 DEFAULT_MIN_DISTANCE = 10 @@ -350,6 +351,58 @@ def read_cache(path): return panos, payload.get("meta", {}) +# --------------------------------------------------------------------------- # +# bundle ground truth (both bundle kinds) +# --------------------------------------------------------------------------- # +def bundle_ground_truths(city, repo=REPO): + """``({pid: GroundTruth}, panos_dir)`` for either kind of benchmark bundle. + + The city splits carry a human verdict review (``verdicts.json``) that + :func:`build_ground_truth` turns into GT. ``benchmark/manual_gold`` instead + carries ``gt_source.json`` pointing at independently-labelled YOLO files — + no RampNet review to derive from, and so no RampNet anchoring. Resolving both + here is what lets the sweep cover the in-distribution gold split alongside the + deployment cities; the layout logic mirrors + ``scripts/model_comparison/compare.py``, which reads the same two bundle kinds. + """ + import json as _json + cdir = os.path.join(repo, "benchmark", city) + panos_dir = os.path.join(cdir, "panos") + vpath = os.path.join(cdir, "verdicts.json") + gpath = os.path.join(cdir, "gt_source.json") + + if os.path.exists(vpath): + import threshold_sweep as ts + records, verdicts, panos_dir = ts.load_bundle(city) + return {pid: build_ground_truth(records[pid]["detections"], entry["dets"], + entry["missed"], entry["no_missed"]) + for pid, entry in verdicts.items()}, panos_dir + + if not os.path.exists(gpath): + raise SystemExit(f"{cdir}: neither verdicts.json nor gt_source.json — " + "not a benchmark bundle") + with open(gpath, encoding="utf-8") as f: + src = _json.load(f) + if src.get("format") != "yolo_points": + raise SystemExit(f"{gpath}: unsupported format {src.get('format')!r} " + "(expected 'yolo_points')") + labels_dir = os.path.normpath(os.path.join(cdir, src["labels_dir"])) + gts = load_yolo_ground_truths(labels_dir) + if not gts: + raise SystemExit(f"{labels_dir}: no .txt label files found") + # Only score panos whose imagery is actually in the bundle: manual_labels/ has + # a label file per gold pano, but the split is unusable for any pano whose jpg + # was not fetched, and silently scoring it as an all-miss pano would deflate + # recall rather than report a gap. + present = {pid for pid in gts + if os.path.exists(os.path.join(panos_dir, f"{pid}.jpg"))} + missing = len(gts) - len(present) + if missing: + print(f" [{city}] {missing} of {len(gts)} labelled panos have no imagery " + f"in the bundle — excluded (not scored as misses)", flush=True) + return {pid: gts[pid] for pid in sorted(present)}, panos_dir + + # --------------------------------------------------------------------------- # # extract (GPU) # --------------------------------------------------------------------------- # @@ -365,11 +418,15 @@ def cmd_extract(args): radius_sq = radius_sq_for() for city in args.cities: - records, verdicts, panos_dir = ts.load_bundle(city) + out_path = os.path.join(args.cache, f"{city}.json") + # Per-city skip, so a preempted or timed-out Slurm job resumes at the split + # it died in rather than re-running every split before it. + if os.path.exists(out_path) and not args.force: + print(f"{city}: cache exists -> skipping (--force to re-extract)", flush=True) + continue + gts, panos_dir = bundle_ground_truths(city) panos = [] - for i, (pid, entry) in enumerate(verdicts.items(), 1): - gt = build_ground_truth(records[pid]["detections"], entry["dets"], - entry["missed"], entry["no_missed"]) + for i, (pid, gt) in enumerate(gts.items(), 1): path = os.path.join(panos_dir, f"{pid}.jpg") try: h = ts.heatmap_for(model, device, path, use_fp16) @@ -380,13 +437,13 @@ def cmd_extract(args): h = ts.heatmap_for(model, device, path, use_fp16) preds = ts.peaks_to_dets(h, args.score_floor, args.min_distance) panos.append({"pano": pid, "preds": preds, "gt": gt}) - if i % 20 == 0: - print(f" {city}: {i}/{len(verdicts)}", flush=True) + if i % 50 == 0: + print(f" {city}: {i}/{len(gts)}", flush=True) del h - out_path = os.path.join(args.cache, f"{city}.json") meta = {"score_floor": args.score_floor, "min_distance": args.min_distance, "radius_normalized": 0.022, "fp16": use_fp16, - "n_panos": len(panos), "deployed_threshold": DEPLOYED_THRESHOLD} + "n_panos": len(panos), "deployed_threshold": DEPLOYED_THRESHOLD, + "model": HF_MODEL_REPO, "device": device.type} write_cache(out_path, city, panos, meta) print(f"{city}: {len(panos)} panos -> {out_path}", flush=True) @@ -843,6 +900,9 @@ def main(): e.add_argument("--score-floor", type=float, default=DEFAULT_SCORE_FLOOR) e.add_argument("--min-distance", type=int, default=DEFAULT_MIN_DISTANCE) e.add_argument("--cache", default=CACHE_DIR) + e.add_argument("--force", action="store_true", + help="re-extract splits that already have a cache (default: skip them, " + "so a preempted job resumes where it stopped)") e.set_defaults(func=cmd_extract) c = sub.add_parser("curve", help="CPU: PR curve + AP + F1-vs-threshold from the cache") diff --git a/scripts/analysis/plot_operating_point.py b/scripts/analysis/plot_operating_point.py new file mode 100644 index 0000000..59d7719 --- /dev/null +++ b/scripts/analysis/plot_operating_point.py @@ -0,0 +1,217 @@ +"""The canonical operating-point figure for issue #54: PR response per split. + +This is the artifact #54 asks for — precision/recall as a function of the peak +threshold, one line per split, with the deployed 0.55 point and the recommended +0.30 point marked — in two panels: + +- **Left, the PR curve.** Precision against recall, traced by sweeping the + threshold. This is the standard form for "what does the trade actually look + like", and it makes the shape of the trade visible in a way a table cannot: the + curves are nearly flat over the operating region, so moving the threshold buys + recall at a shallow precision cost until it doesn't. +- **Right, F1 against threshold.** The same sweep read the other way, which is + what shows *how little* F1 discriminates here — it varies by under 0.01 across + 0.25–0.55 pooled, so the choice of operating point cannot rest on F1 alone. + +Both panels colour by split, consistently, so a line is the same entity in both. +The five US/VA splits are solid; ``budapest_district5`` and ``manual_gold`` are +dashed because they are held out of the pooled recommendation for different +reasons (single-rater GT; in-distribution control). Line style carries the +held-out status so colour is free to carry identity alone. + +Colours are the validated categorical slots in fixed order. Three of them sit below +3:1 on a light surface, so the palette's relief rule applies: it is discharged by +the **table view** (``analysis_out/op/low_floor_sweep.csv`` holds every plotted +number, and ``docs/operating_point.md`` tabulates the operating points) plus a +shared legend, rather than by per-line labels. Direct labels were tried first and +removed — in the PR panel the splits converge into a cluster around +(0.80, 0.88) where seven labels cannot be placed without overlapping, which is a +worse accessibility outcome than the legend. + +Reads the committed caches only — CPU, no GPU, no imagery: + + python scripts/analysis/plot_operating_point.py +""" +import os +import sys + +REPO = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +sys.path.insert(0, REPO) +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) + +from rampnet.detection_eval import radius_sq_for # noqa: E402 + +from low_floor_sweep import ( # noqa: E402 + ALL_SPLITS, CACHE_DIR, DEPLOYED_THRESHOLD, US_SPLITS, best_f1_row, load_split, + pool_of, row_at, sweep_rows, threshold_grid) + +RECOMMENDED = 0.30 + +# Validated categorical slots, fixed order (never cycled). +SERIES = { + "richmond": "#2a78d6", + "bend": "#eb6834", + "clovis": "#1baf7a", + "morgantown": "#eda100", + "annapolis": "#e87ba4", + "budapest_district5": "#008300", + "manual_gold": "#4a3aa7", +} +POOLED_COLOR = "#0b0b0b" +INK, INK_MUTED, GRID = "#0b0b0b", "#52514e", "#d9d8d4" +LABEL = {"budapest_district5": "budapest*", "manual_gold": "manual_gold†"} + + +def collect(cities=ALL_SPLITS, cache_dir=CACHE_DIR): + radius_sq = radius_sq_for() + grid = threshold_grid(0.05, 0.90) + loaded = {c: load_split(c, cache_dir)[0] for c in cities} + out = {c: sweep_rows(p, grid, radius_sq) for c, p in loaded.items()} + poolable = pool_of(cities) + if len(poolable) > 1: + out["POOLED"] = sweep_rows([pd for c in poolable for pd in loaded[c]], + grid, radius_sq) + return out + + +def build(curves, path): + import matplotlib + matplotlib.use("Agg") + import matplotlib.pyplot as plt + from matplotlib.lines import Line2D + + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14.4, 6.4)) + fig.patch.set_facecolor("white") + + order = [c for c in SERIES if c in curves] + + # ---- Left: the PR curve ------------------------------------------------ # + for city in order: + rows = curves[city] + solid = city in US_SPLITS + ax1.plot([r["recall"] for r in rows], [r["precision"] for r in rows], + color=SERIES[city], lw=2 if solid else 1.6, + ls="-" if solid else (0, (5, 2)), zorder=3, solid_capstyle="round") + if "POOLED" in curves: + rows = curves["POOLED"] + ax1.plot([r["recall"] for r in rows], [r["precision"] for r in rows], + color=POOLED_COLOR, lw=3, zorder=4, solid_capstyle="round") + + # Operating points: deployed (hollow) vs recommended (filled), on every curve. + for city in order + (["POOLED"] if "POOLED" in curves else []): + rows = curves[city] + colour = SERIES.get(city, POOLED_COLOR) + dep, rec = row_at(rows, DEPLOYED_THRESHOLD), row_at(rows, RECOMMENDED) + ax1.plot([dep["recall"]], [dep["precision"]], "o", ms=7.5, mfc="white", + mec=colour, mew=2, zorder=6) + ax1.plot([rec["recall"]], [rec["precision"]], "o", ms=7.5, color=colour, + mec="white", mew=1.4, zorder=6) + + ax1.set_xlabel("recall", fontsize=9.5, color=INK_MUTED) + ax1.set_ylabel("precision", fontsize=9.5, color=INK_MUTED) + ax1.set_title("Precision–recall response to the peak threshold", + fontsize=11.5, color=INK, loc="left", pad=10) + ax1.set_xlim(0.42, 1.0) + ax1.set_ylim(0.55, 1.005) + ax1.legend(handles=[ + Line2D([], [], marker="o", ls="", ms=7.5, mfc="white", mec=INK_MUTED, mew=2, + label=f"deployed {DEPLOYED_THRESHOLD:.2f}"), + Line2D([], [], marker="o", ls="", ms=7.5, color=INK_MUTED, mec="white", + label=f"recommended {RECOMMENDED:.2f}"), + Line2D([], [], color=POOLED_COLOR, lw=3, label="pooled (5 US splits)"), + Line2D([], [], color=INK_MUTED, lw=1.6, ls=(0, (5, 2)), + label="held out of the pooled recommendation"), + ], fontsize=8.4, frameon=False, loc="lower left") + + # ---- Right: F1 against threshold --------------------------------------- # + ax2.axvspan(0.25, 0.40, color="#f0efec", zorder=0) + for city in order: + rows = curves[city] + solid = city in US_SPLITS + ax2.plot([r["threshold"] for r in rows], [r["f1"] for r in rows], + color=SERIES[city], lw=2 if solid else 1.6, + ls="-" if solid else (0, (5, 2)), zorder=3) + best = best_f1_row(rows) + ax2.plot([best["threshold"]], [best["f1"]], "o", ms=6, color=SERIES[city], + mec="white", mew=1.2, zorder=5) + if "POOLED" in curves: + rows = curves["POOLED"] + ax2.plot([r["threshold"] for r in rows], [r["f1"] for r in rows], + color=POOLED_COLOR, lw=3, zorder=4) + best = best_f1_row(rows) + ax2.plot([best["threshold"]], [best["f1"]], "o", ms=7, color=POOLED_COLOR, + mec="white", mew=1.4, zorder=5) + + for x, lab in ((DEPLOYED_THRESHOLD, "deployed"), (RECOMMENDED, "recommended")): + ax2.axvline(x, color=INK_MUTED, lw=1, ls=":", zorder=2) + ax2.annotate(f"{lab} {x:.2f}", (x, 0.935), ha="center", va="bottom", + fontsize=8.2, color=INK_MUTED, zorder=6) + ax2.annotate("pooled F1 varies by <0.01 across the shaded band —\n" + "F1 alone cannot pick the operating point", + (0.075, 0.345), ha="left", fontsize=8.3, color=INK_MUTED, zorder=6) + ax2.annotate("• = each split's F1 optimum", (0.075, 0.315), ha="left", + fontsize=8.3, color=INK_MUTED, zorder=6) + + ax2.set_xlabel("peak threshold", fontsize=9.5, color=INK_MUTED) + ax2.set_ylabel("F1", fontsize=9.5, color=INK_MUTED) + ax2.set_title("F1 is flat over the operating region", fontsize=11.5, + color=INK, loc="left", pad=10) + ax2.set_xlim(0.05, 0.90) + ax2.set_ylim(0.30, 0.95) + + for ax in (ax1, ax2): + ax.grid(True, color=GRID, lw=0.8, zorder=0) + ax.set_axisbelow(True) + for side in ("top", "right"): + ax.spines[side].set_visible(False) + for side in ("bottom", "left"): + ax.spines[side].set_color(GRID) + ax.tick_params(axis="both", length=0, labelsize=8.6, colors=INK_MUTED) + + # One shared split legend outside both panels: colour means the same entity in + # each, and the series are too similar in the PR panel for per-line labels to + # land without collisions. The palette's relief rule is satisfied by the table + # view — analysis_out/op/low_floor_sweep.csv holds every plotted number. + handles = [Line2D([], [], color=SERIES[c], lw=2.4, + ls="-" if c in US_SPLITS else (0, (5, 2)), + label=LABEL.get(c, c)) for c in order] + if "POOLED" in curves: + handles.append(Line2D([], [], color=POOLED_COLOR, lw=3, label="POOLED (5 US)")) + fig.legend(handles=handles, fontsize=8.8, frameon=False, + loc="center left", bbox_to_anchor=(0.878, 0.55), + title="split", title_fontsize=9) + + fig.suptitle("Lowering the peak threshold 0.55 → 0.30 buys ~7 recall points at a " + "shallow precision cost, on every split", + fontsize=13.5, color=INK, x=0.008, ha="left", y=0.982) + fig.text(0.008, 0.012, + "RampNet peaks extracted at a 0.05 floor over 1,625 benchmark panos " + "(min_distance 10, no TTA); threshold swept post-hoc. Precision below 0.55 is a " + "LOWER BOUND on the city splits — their GT was\nassembled from detections at or " + "above the deployed floor, so a real ramp nobody marked scores as a false " + "positive (see docs/operating_point.md; the #55 correction is applied " + "separately).\n*budapest GT is single-rater, low confidence. †manual_gold is " + "in-distribution GSV with independent, un-anchored GT.", + fontsize=7.6, color=INK_MUTED, ha="left", va="bottom") + + fig.subplots_adjust(left=0.050, right=0.872, top=0.885, bottom=0.165, wspace=0.185) + os.makedirs(os.path.dirname(path), exist_ok=True) + fig.savefig(path, dpi=170, facecolor=fig.get_facecolor()) + plt.close(fig) + return path + + +def main(): + curves = collect() + path = build(curves, os.path.join(REPO, "docs", "figures", "operating_point_pr.png")) + print(f"wrote {path}") + for city, rows in curves.items(): + dep, rec, best = (row_at(rows, DEPLOYED_THRESHOLD), row_at(rows, RECOMMENDED), + best_f1_row(rows)) + print(f" {city:<20} 0.55: P {dep['precision']:.3f} R {dep['recall']:.3f}" + f" 0.30: P {rec['precision']:.3f} R {rec['recall']:.3f}" + f" F1-max @ {best['threshold']:.2f}") + + +if __name__ == "__main__": + main() diff --git a/scripts/analysis/plot_storage_floor.py b/scripts/analysis/plot_storage_floor.py new file mode 100644 index 0000000..e67884e --- /dev/null +++ b/scripts/analysis/plot_storage_floor.py @@ -0,0 +1,186 @@ +"""Figure for the storage-floor / recall-ceiling question (issue #54; labeler#28, #27). + +Two panels, because they answer two different questions and a single axis cannot +carry both honestly: + +- **Left — what a 0.1 storage floor costs.** Per split, the count of ground-truth + ramps whose *best* candidate falls in ``[0.05, 0.10)`` (discarded by the floor, + and unrecoverable afterwards) beside those in ``[0.10, 0.20)`` (kept, but only + because the floor is where it is). Two categories with a decision attached, so a + categorical pair; the discarded band takes the warmer hue because it is the one + under scrutiny. +- **Right — the recall ceiling.** Per split, recall at the deployed threshold, at + the 0.1 storage floor, and at the 0.05 extraction floor. Thresholds are an + *ordered* quantity, so this uses one hue stepped light→dark rather than three + unrelated colors. The 0.1 marker is the hard ceiling on any multi-view consensus + policy (labeler#27 stage 4): a candidate never stored cannot be promoted. + +Colors come from the validated reference palette (categorical slots 2 and 1 for the +left panel; the blue ordinal ramp steps 250/450/650 for the right, which is the +lightest start that still clears 2:1 on a light surface). Both sets were checked with +the palette validator rather than eyeballed. + +Reads the committed caches only — CPU, no GPU, no imagery: + + python scripts/analysis/plot_storage_floor.py +""" +import os +import sys + +REPO = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +OUT = os.environ.get("RAMPNET_ANALYSIS_OUT", os.path.join(REPO, "analysis_out")) +sys.path.insert(0, REPO) +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) + +from rampnet.detection_eval import radius_sq_for # noqa: E402 + +from low_floor_sweep import ( # noqa: E402 + ALL_SPLITS, CACHE_DIR, DEPLOYED_THRESHOLD, STORAGE_FLOOR, floor_report, + load_split, pool_of) + +# Validated categorical slots (light mode): orange = the band under scrutiny. +C_LOST = "#eb6834" +C_KEPT = "#2a78d6" +# Validated ordinal blue ramp, light mode: steps 250 / 450 / 650. +RAMP = ("#86b6ef", "#2a78d6", "#104281") +INK, INK_MUTED, GRID = "#0b0b0b", "#52514e", "#d9d8d4" + +LABEL = {"budapest_district5": "budapest*", "manual_gold": "manual_gold†"} + + +def collect(cities, cache_dir=CACHE_DIR): + radius_sq = radius_sq_for() + loaded = {c: load_split(c, cache_dir)[0] for c in cities} + rows = [] + for city, panos in loaded.items(): + rows.append((LABEL.get(city, city), floor_report(panos, radius_sq))) + poolable = pool_of(cities) + if len(poolable) > 1: + pooled = [pd for c in poolable for pd in loaded[c]] + rows.append(("POOLED (5 US)", floor_report(pooled, radius_sq))) + return rows + + +def build(rows, path): + import matplotlib + matplotlib.use("Agg") + import matplotlib.pyplot as plt + from matplotlib.lines import Line2D + + names = [n for n, _ in rows] + y = list(range(len(names)))[::-1] # top-down reading order + + fig, (ax1, ax2) = plt.subplots( + 1, 2, figsize=(13.2, 0.52 * len(names) + 3.1), sharey=True, + gridspec_kw={"width_ratios": [1.0, 1.35], "wspace": 0.08}) + fig.patch.set_facecolor("white") + + # ---- Left: where the marginal ramps sit -------------------------------- # + # Bars are a SHARE of each split's ground truth, not raw counts: manual_gold has + # 3,919 GT ramps against ~300 for a city split, so counts would make it tower + # while its rate is in fact the lowest here. Counts ride along as labels. + h = 0.34 + gap = 0.02 # 2px-equivalent surface gap + pct = lambda n, d: 100.0 * n / d if d else 0.0 # noqa: E731 + for yi, (_n, rep) in zip(y, rows): + lost, kept, n_gt = (rep["bands"]["[0.05,0.10)"], rep["bands"]["[0.10,0.20)"], + rep["n_gt"]) + ax1.barh(yi + (h + gap) / 2, pct(lost, n_gt), height=h, color=C_LOST, zorder=3) + ax1.barh(yi - (h + gap) / 2, pct(kept, n_gt), height=h, color=C_KEPT, zorder=3) + ax1.text(pct(lost, n_gt) + 0.09, yi + (h + gap) / 2, + f"{pct(lost, n_gt):.2f}% ({lost})", va="center", ha="left", + fontsize=8.3, color=INK, zorder=4) + ax1.text(pct(kept, n_gt) + 0.09, yi - (h + gap) / 2, + f"{pct(kept, n_gt):.2f}% ({kept})", va="center", ha="left", + fontsize=8.3, color=INK_MUTED, zorder=4) + + ax1.set_xlabel("share of the split's ground-truth ramps (count in brackets)", + fontsize=9, color=INK_MUTED) + ax1.set_title("What the 0.1 storage floor discards", fontsize=11, + color=INK, loc="left", pad=10) + ax1.set_xlim(0, max(pct(max(r["bands"]["[0.05,0.10)"], r["bands"]["[0.10,0.20)"]), + r["n_gt"]) for _, r in rows) * 1.75) + ax1.legend(handles=[ + plt.Rectangle((0, 0), 1, 1, color=C_LOST, label="[0.05, 0.10) discarded"), + plt.Rectangle((0, 0), 1, 1, color=C_KEPT, label="[0.10, 0.20) kept"), + ], fontsize=8.5, frameon=False, loc="upper center", + bbox_to_anchor=(0.5, -0.13), ncol=2) + + # ---- Right: the recall ceiling ----------------------------------------- # + series = [("0.05 extraction floor", "0.05", RAMP[0]), + (f"{STORAGE_FLOOR:.2f} storage floor — the ceiling", "0.10", RAMP[1]), + (f"{DEPLOYED_THRESHOLD:.2f} deployed", "0.55", RAMP[2])] + for yi, (_n, rep) in zip(y, rows): + lo, ceil = rep["recall_at"]["0.55"], rep["recall_at"]["0.10"] + hi = rep["recall_at"]["0.05"] + ax2.plot([lo, hi], [yi, yi], color=GRID, lw=2, zorder=1, + solid_capstyle="round") + for _lab, key, colour in series: + ax2.plot([rep["recall_at"][key]], [yi], "o", ms=9, color=colour, + markeredgecolor="white", markeredgewidth=1.4, zorder=3) + # The actionable gap is deployed -> the 0.10 ceiling: what consensus could + # still recover from what production actually stores. + ax2.text(hi + 0.014, yi, f"+{ceil - lo:.3f} recoverable", va="center", + ha="left", fontsize=8.3, color=INK_MUTED, zorder=4) + + ax2.set_xlabel("recall (share of ground-truth ramps with a candidate at or above the floor)", + fontsize=9, color=INK_MUTED) + ax2.set_title("Recall ceiling: headroom above the deployed threshold", + fontsize=11, color=INK, loc="left", pad=10) + ax2.set_xlim(0.44, 1.10) + ax2.legend(handles=[Line2D([], [], marker="o", ls="", ms=8, color=c, + markeredgecolor="white", label=lab) + for lab, _k, c in series], + fontsize=8.5, frameon=False, loc="upper center", + bbox_to_anchor=(0.5, -0.13), ncol=3) + + pooled_y = y[[n for n, _ in rows].index("POOLED (5 US)")] if any( + n == "POOLED (5 US)" for n, _ in rows) else None + for ax in (ax1, ax2): + ax.set_yticks(y) + ax.set_yticklabels(names, fontsize=9.5, color=INK) + ax.xaxis.grid(True, color=GRID, lw=0.8, zorder=0) + ax.set_axisbelow(True) + for side in ("top", "right", "left"): + ax.spines[side].set_visible(False) + ax.spines["bottom"].set_color(GRID) + ax.tick_params(axis="both", length=0, labelsize=8.5, colors=INK_MUTED) + ax.set_ylim(min(y) - 0.8, max(y) + 0.8) + # POOLED is an aggregate, not another split — rule it off so it is not read + # as a peer row. + if pooled_y is not None: + ax.axhline(pooled_y + 0.62, color=GRID, lw=1.0, zorder=1) + for lab in ax1.get_yticklabels(): + if lab.get_text() == "POOLED (5 US)": + lab.set_fontweight("bold") + + fig.suptitle("A 0.1 detection storage floor discards ~2.7% of findable curb ramps, " + "permanently", + fontsize=13.5, color=INK, x=0.008, ha="left", y=0.985) + fig.text(0.008, 0.012, + "RampNet peaks extracted at a 0.05 floor over 1,625 benchmark panos " + "(min_distance 10, no TTA). A candidate below the storage floor is never " + "written, so no downstream\nmulti-view consensus can recover it — the 0.10 " + "marker is a hard ceiling on labeler#27 stage 4. " + "*budapest GT is single-rater, low confidence. †manual_gold is " + "in-distribution GSV with independent GT.", + fontsize=7.6, color=INK_MUTED, ha="left", va="bottom") + + fig.subplots_adjust(left=0.105, right=0.995, top=0.885, bottom=0.20, wspace=0.08) + os.makedirs(os.path.dirname(path), exist_ok=True) + fig.savefig(path, dpi=170, facecolor=fig.get_facecolor()) + plt.close(fig) + return path + + +def main(): + rows = collect(ALL_SPLITS) + path = build(rows, os.path.join(REPO, "docs", "figures", "storage_floor_ceiling.png")) + print(f"wrote {path}") + for name, rep in rows: + print(f" {name:<16} lost@0.10 {rep['bands']['[0.05,0.10)']:>3} " + f"ceiling {rep['recall_at']['0.10']:.3f} deployed {rep['recall_at']['0.55']:.3f}") + + +if __name__ == "__main__": + main() diff --git a/scripts/analysis/run_low_floor_extract.slurm b/scripts/analysis/run_low_floor_extract.slurm new file mode 100644 index 0000000..cda8246 --- /dev/null +++ b/scripts/analysis/run_low_floor_extract.slurm @@ -0,0 +1,94 @@ +#!/bin/bash +# Low-floor peak extraction over every benchmark split, on Hyak (klone) — the one +# GPU step of the operating-point sweep (issue #54; #55 and #78 ride on the same +# cache). Everything downstream (curves, per-tier pooling, calibration histograms, +# the incremental-FP gallery) is CPU-only and reads the cache this writes. +# +# Pass your Slurm account with -A; there is no usable default, and a submission +# without one is rejected. Find yours with: +# sacctmgr -nP show assoc user=$USER format=Account,Partition,QOS +# +# mkdir -p logs +# sbatch -A scripts/analysis/run_low_floor_extract.slurm +# CITIES=clovis,morgantown sbatch -A scripts/analysis/run_low_floor_extract.slurm +# +# Runs on ONE GPU: inference is a single 2048x4096 forward pass per pano (~1-2 s), +# so ~1,750 panos across seven splits is well under an hour. This deliberately uses +# the dedicated gpu-l40s allocation rather than a ckpt-* partition — a preempted job +# would be recoverable (see --force below) but there is no reason to risk it for a +# sub-hour run. +# +# Resumable: `extract` skips any split whose cache already exists, so a preempted or +# timed-out job re-submits with no argument changes and picks up at the split it died +# in. Pass FORCE=1 to re-extract splits that already have a cache. +# +# PARITY IS THE GATE. After this finishes, run (CPU, locally or on a login node): +# python scripts/analysis/low_floor_sweep.py parity +# The peaks at >= 0.55 must reproduce each split's committed records.jsonl. Bundle +# panos are native-res while production GSV went through a 4096x2048 intermediate — +# one extra resample — so expect agreement to a few pixels, not bit-for-bit. A gross +# mismatch means preprocessing diverged and every downstream number inherits it. +# manual_gold is exempt: its committed detections were exported WITH flip-TTA, so +# its row is a TTA delta rather than a drift check. +# +# PYTHON is the interpreter to run with; default is whatever `python` resolves to +# after the env is activated (see the runbook in docs/model_comparison.md). Needs +# timm + scikit-image + huggingface_hub (the model is loaded from the HF hub). +#SBATCH -p gpu-l40s +#SBATCH --job-name=low_floor_extract +#SBATCH --time=4:00:00 +#SBATCH --mem=48G +#SBATCH --cpus-per-task=8 +#SBATCH --nodes=1 +#SBATCH --ntasks=1 +#SBATCH --gpus-per-node=1 +#SBATCH --output=logs/low_floor_extract_%j.out +#SBATCH --error=logs/low_floor_extract_%j.err + +set -euo pipefail + +# All seven splits. manual_gold is included deliberately: it is the in-distribution +# reference with independently-labelled (non-RampNet-anchored) GT, and its imagery is +# already staged on klone. It is swept alongside the deployment cities but held out of +# the pooled recommendation — see low_floor_sweep.py's HELD_OUT. +CITIES="${CITIES:-richmond,bend,clovis,morgantown,annapolis,budapest_district5,manual_gold}" +SCORE_FLOOR="${SCORE_FLOOR:-0.05}" +MIN_DISTANCE="${MIN_DISTANCE:-10}" +CACHE="${CACHE:-analysis_out/op_cache}" + +PYTHON="${PYTHON:-}" +if [ -z "$PYTHON" ]; then + source activate sidewalkcv2 + PYTHON=python +fi + +FORCE_FLAG="" +if [ -n "${FORCE:-}" ]; then + FORCE_FLAG="--force" +fi + +echo "--- low-floor extraction (issue #54) ---" +echo "Splits: ${CITIES}" +echo "Score floor:${SCORE_FLOOR} min_distance: ${MIN_DISTANCE}" +echo "Cache: ${CACHE}" +echo "Node: ${SLURMD_NODENAME:-?}" +echo "Python: $($PYTHON -c 'import sys,torch;print(sys.version.split()[0],"torch",torch.__version__)')" +nvidia-smi --query-gpu=name,memory.total --format=csv || true +echo "----------------------------------------" + +# min_distance stays at 10: issue #25 found 10->3 buys ~0.5 recall points and costs +# precision, and peak_nms_check.py (#62) showed no suppression radius separates real +# adjacent ramp pairs from the rare duplicate. The threshold is the lever here. +"$PYTHON" -u scripts/analysis/operating_point_curve.py extract \ + --cities "$CITIES" \ + --score-floor "$SCORE_FLOOR" \ + --min-distance "$MIN_DISTANCE" \ + --cache "$CACHE" \ + $FORCE_FLAG + +echo +echo "--- extraction done; caches in ${CACHE} ---" +ls -la "$CACHE" +echo +echo "NEXT: run the parity gate before trusting anything downstream:" +echo " python scripts/analysis/low_floor_sweep.py parity --cache ${CACHE}" diff --git a/tests/test_low_floor_sweep.py b/tests/test_low_floor_sweep.py new file mode 100644 index 0000000..3d95207 --- /dev/null +++ b/tests/test_low_floor_sweep.py @@ -0,0 +1,430 @@ +"""Unit tests for the cross-split low-floor sweep core (issue #54). + +Pure logic only — no GPU, no panos, no cache on disk. The load-bearing guarantees: +the sweep's per-threshold rows agree with the shared scorer, the parity gate +actually fails when preprocessing drifts, and monotone quantities stay monotone. +""" +import math +import os +import random +import sys + +REPO = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +sys.path.insert(0, os.path.join(REPO, "scripts", "analysis")) + +from rampnet.detection_eval import ( # noqa: E402 + GroundTruth, aggregate, radius_sq_for, score_pano) + +import low_floor_sweep as lfs # noqa: E402 + +RSQ = radius_sq_for() + + +def _pano(rng, pid, n_gt=3): + gt_pts = [(rng.uniform(0.05, 0.95), rng.uniform(0.5, 0.9)) for _ in range(n_gt)] + gt = GroundTruth(gt_pts, [], fn_confirmed=True) + preds = [] + for gx, gy in gt_pts: # near-hits with varied confidence + preds.append((gx + rng.uniform(-0.01, 0.01), gy + rng.uniform(-0.005, 0.005), + rng.random())) + for _ in range(rng.randint(0, 4)): # scatter + preds.append((rng.random(), rng.uniform(0.5, 0.95), rng.random())) + return {"pano": pid, "preds": preds, "gt": gt, "tier": "gsv", "city": "test"} + + +def _panos(seed=0, n=12): + rng = random.Random(seed) + return [_pano(rng, f"p{i}") for i in range(n)] + + +# --------------------------------------------------------------------------- # +# grid +# --------------------------------------------------------------------------- # +def test_threshold_grid_is_sorted_unique_and_within_bounds(): + grid = lfs.threshold_grid(0.05, 0.90) + assert grid[0] == 0.05 and grid[-1] == 0.90 + assert len(grid) == len(set(grid)) + assert all(b > a for a, b in zip(grid, grid[1:])) + assert all(0.05 <= v <= 0.90 for v in grid) + + +def test_threshold_grid_is_fine_in_the_candidate_band_only(): + """0.20-0.50 at 0.01; the tails stay coarse. This is the whole point of the grid.""" + grid = lfs.threshold_grid(0.05, 0.90) + for v in (0.21, 0.27, 0.33, 0.49): + assert any(abs(v - g) < 1e-9 for g in grid), f"{v} missing from fine band" + for v in (0.06, 0.13, 0.62, 0.87): + assert not any(abs(v - g) < 1e-9 for g in grid), f"{v} should not be swept" + + +def test_threshold_grid_degenerate_range(): + assert lfs.threshold_grid(0.9, 0.5) == [] + assert lfs.threshold_grid(0.5, 0.5) == [0.5] + + +# --------------------------------------------------------------------------- # +# tiers +# --------------------------------------------------------------------------- # +def test_tier_of_covers_every_benchmark_rig(): + assert lfs.tier_of("Trimble", "Trimble mx7", "mapillary") == "survey" + assert lfs.tier_of("Trimble", "Trimble MX7", "mapillary") == "survey" # case variant + assert lfs.tier_of("NCTECH LTD", "iSTAR Pulsar", "mapillary") == "pro360" + assert lfs.tier_of("GoPro", "GoPro Max", "mapillary") == "action-modern" + assert lfs.tier_of("GoPro", "Fusion", "mapillary") == "action-legacy" + assert lfs.tier_of("GoPro", "GoPro Fusion FS1.04.01.80.00", "mapillary") == "action-legacy" + assert lfs.tier_of(None, None, "launch") == "gsv" + + +def test_tier_of_does_not_invent_a_tier_for_absent_provenance(): + """'none'/None must land in `unknown`, never be folded into a neighbouring tier.""" + assert lfs.tier_of("none", "none", "mapillary") == "unknown" + assert lfs.tier_of(None, None, "mapillary") == "unknown" + assert lfs.tier_of("LG Electronics", "LG-R105", "mapillary") == "unknown" + + +def test_gsv_source_wins_over_camera_fields(): + """A GSV pano is GSV regardless of what camera metadata happens to be attached.""" + assert lfs.tier_of("GoPro", "GoPro Max", "gsv") == "gsv" + + +# --------------------------------------------------------------------------- # +# sweep rows +# --------------------------------------------------------------------------- # +def test_sweep_row_matches_the_shared_scorer(): + """A swept row must equal what score_pano/aggregate say at that threshold.""" + panos = _panos() + grid = [0.1, 0.3, 0.55, 0.8] + rows = lfs.sweep_rows(panos, grid, RSQ) + for row in rows: + expected = aggregate([ + score_pano([p for p in pd["preds"] if p[2] >= row["threshold"]], + pd["gt"], radius_sq=RSQ) + for pd in panos]) + assert row["tp"] == expected.tp + assert row["fp"] == expected.fp + assert row["fn"] == expected.fn + assert math.isclose(row["precision"], expected.precision) + assert math.isclose(row["recall"], expected.recall) + + +def test_recall_is_monotone_non_increasing_in_threshold(): + rows = lfs.sweep_rows(_panos(seed=3), lfs.threshold_grid(0.05, 0.90), RSQ) + recalls = [r["recall"] for r in rows] + assert all(b <= a + 1e-12 for a, b in zip(recalls, recalls[1:])) + + +def test_density_counts_ignored_detections(): + """dets_per_pano is review burden, so an `unsure`-covered detection still counts.""" + gt = GroundTruth([], [(0.5, 0.6)], fn_confirmed=True) + panos = [{"pano": "a", "preds": [(0.5, 0.6, 0.9)], "gt": gt}] + row = lfs.sweep_rows(panos, [0.1], RSQ)[0] + assert row["tp"] == 0 and row["fp"] == 0 and row["ignored"] == 1 + assert row["dets_per_pano"] == 1.0 + + +def test_best_f1_breaks_ties_toward_the_higher_threshold(): + rows = [{"threshold": 0.2, "f1": 0.5}, {"threshold": 0.4, "f1": 0.5}, + {"threshold": 0.6, "f1": 0.4}] + assert lfs.best_f1_row(rows)["threshold"] == 0.4 + + +def test_highest_threshold_meeting_picks_the_lowest_qualifying_point(): + """Recall-first: among points clearing the precision floor, take the lowest.""" + rows = [{"threshold": 0.2, "precision": 0.80}, {"threshold": 0.3, "precision": 0.92}, + {"threshold": 0.4, "precision": 0.95}] + assert lfs.highest_threshold_meeting(rows, 0.92)["threshold"] == 0.3 + assert lfs.highest_threshold_meeting(rows, 0.99) is None + + +# --------------------------------------------------------------------------- # +# calibration +# --------------------------------------------------------------------------- # +def test_calibration_bins_partition_every_scored_prediction(): + panos = _panos(seed=7) + edges = [round(0.05 + i * 0.05, 4) for i in range(20)] + bins = lfs.confidence_calibration(panos, RSQ, edges) + binned = sum(b["n"] for b in bins) + expected = sum(r["tp"] + r["fp"] for r in lfs.sweep_rows(panos, [edges[0]], RSQ)) + assert binned == expected + + +def test_calibration_excludes_ignored_predictions(): + gt = GroundTruth([(0.2, 0.6)], [(0.8, 0.6)], fn_confirmed=True) + panos = [{"pano": "a", "preds": [(0.2, 0.6, 0.9), (0.8, 0.6, 0.7)], "gt": gt}] + bins = lfs.confidence_calibration(panos, RSQ, [0.05, 0.5, 1.0]) + assert sum(b["n"] for b in bins) == 1 # the ignore-covered one is dropped + assert sum(b["n_true"] for b in bins) == 1 + + +def test_calibration_places_a_perfect_score_in_the_last_bin(): + gt = GroundTruth([(0.2, 0.6)], [], fn_confirmed=True) + panos = [{"pano": "a", "preds": [(0.2, 0.6, 1.0)], "gt": gt}] + bins = lfs.confidence_calibration(panos, RSQ, [0.05, 0.5, 1.0]) + assert bins[-1]["n_true"] == 1 + + +# --------------------------------------------------------------------------- # +# distance +# --------------------------------------------------------------------------- # +def test_ground_distance_is_monotone_decreasing_below_the_horizon(): + ys = [0.51, 0.55, 0.6, 0.7, 0.8] + ds = [lfs.ground_distance(y) for y in ys] + assert all(b < a for a, b in zip(ds, ds[1:])) + + +def test_ground_distance_at_or_above_horizon_is_infinite(): + assert lfs.ground_distance(0.5) == float("inf") + assert lfs.ground_distance(0.3) == float("inf") + + +def test_recall_by_distance_bands_sum_to_the_recall_denominator(): + panos = _panos(seed=11) + rows = lfs.recall_by_distance(panos, RSQ, 0.2, 0.55) + total_gt = sum(len(pd["gt"].gt_points) for pd in panos if pd["gt"].fn_confirmed) + assert sum(r["n_gt"] for r in rows) == total_gt + assert all(r["gained"] >= 0 for r in rows) # a lower threshold cannot lose a ramp + + +# --------------------------------------------------------------------------- # +# parity gate +# --------------------------------------------------------------------------- # +def _records_from(panos, threshold=0.55): + return {pd["pano"]: {"detections": [ + {"x_normalized": x, "y_normalized": y, "confidence": s} + for (x, y, s) in pd["preds"] if s >= threshold]} for pd in panos} + + +def test_parity_passes_and_is_exact_when_the_cache_reproduces_records(): + panos = _panos(seed=5) + res = lfs.parity_for(panos, _records_from(panos)) + assert res["ok"] + assert res["n_records"] == res["n_cache"] == res["matched"] + assert res["exact_frac"] == 1.0 + assert res["max_displacement_r"] == 0.0 + + +def test_parity_tolerates_sub_radius_drift_but_reports_it_as_inexact(): + """The GSV path fed the model a different resample, so peaks shift a cell or two. + + That must still pass — nothing it moved can change a scoring outcome — while the + exact-match fraction drops, which is the sensitive early warning. + """ + panos = _panos(seed=6) + records = _records_from(panos) + for rec in records.values(): + for d in rec["detections"]: + d["x_normalized"] += 0.004 # ~4 heatmap cells, ~0.18 R + res = lfs.parity_for(panos, records) + assert res["ok"] + assert res["exact_frac"] == 0.0 + assert 0 < res["max_displacement_r"] <= lfs.PARITY_TOL_RADII + + +def test_parity_fails_when_detections_move_beyond_a_scoring_radius(): + """The gate has to actually fail, or it is decoration.""" + panos = _panos(seed=8) + records = _records_from(panos) + for rec in records.values(): + for d in rec["detections"]: + d["x_normalized"] = (d["x_normalized"] + 0.25) % 1.0 + res = lfs.parity_for(panos, records) + assert not res["ok"] + + +def test_parity_fails_when_many_detections_are_missing(): + panos = _panos(seed=9) + records = _records_from(panos) + for rec in records.values(): + if rec["detections"]: + rec["detections"].pop() + res = lfs.parity_for(panos, records) + assert not res["ok"] + assert res["panos_with_count_mismatch"] + + +def test_parity_reports_a_count_mismatch_even_when_it_is_within_gate_tolerance(): + """A single dropped detection is recorded per pano, not silently absorbed.""" + panos = _panos(seed=9) + records = _records_from(panos) + next(r for r in records.values() if r["detections"])["detections"].pop() + res = lfs.parity_for(panos, records) + assert res["panos_with_count_mismatch"] + assert res["n_cache"] == res["n_records"] + 1 + + +# --------------------------------------------------------------------------- # +# pooling policy +# --------------------------------------------------------------------------- # +def test_pool_holds_out_budapest_and_gold_by_default(): + pool = lfs.pool_of(lfs.ALL_SPLITS) + assert set(pool) == set(lfs.US_SPLITS) + assert "budapest_district5" not in pool and "manual_gold" not in pool + + +def test_pool_flags_opt_the_held_out_splits_back_in(): + assert "budapest_district5" in lfs.pool_of(lfs.ALL_SPLITS, include_budapest=True) + assert "manual_gold" in lfs.pool_of(lfs.ALL_SPLITS, include_gold=True) + + +def test_every_held_out_split_carries_a_stated_reason(): + """An omission with no reason is indistinguishable from a withheld result.""" + for split in set(lfs.ALL_SPLITS) - set(lfs.pool_of(lfs.ALL_SPLITS)): + assert lfs.HELD_OUT.get(split), f"{split} is held out with no documented reason" + + +# --------------------------------------------------------------------------- # +# GT-anchoring bias +# --------------------------------------------------------------------------- # +def test_tp_origin_attributes_to_the_right_gt_point(): + """A TP is credited to the origin of the GT point it actually matched.""" + gt = GroundTruth([(0.2, 0.6), (0.8, 0.6)], [], fn_confirmed=True) + panos = [{"pano": "a", "preds": [(0.2, 0.6, 0.9), (0.8, 0.6, 0.3)], "gt": gt}] + counts = lfs.tp_origin_by_bin(panos, {"a": ["reviewed", "missed"]}, RSQ) + assert counts[(0.9, "reviewed")] == 1 + assert counts[(0.3, "missed")] == 1 + + +def test_tp_origin_skips_panos_whose_origins_do_not_line_up(): + """Verdict drift must skip the pano, never mis-attribute a TP to the wrong origin.""" + gt = GroundTruth([(0.2, 0.6), (0.8, 0.6)], [], fn_confirmed=True) + panos = [{"pano": "a", "preds": [(0.2, 0.6, 0.9)], "gt": gt}] + assert lfs.tp_origin_by_bin(panos, {"a": ["reviewed"]}, RSQ) == {} + assert lfs.tp_origin_by_bin(panos, {}, RSQ) == {} + + +def test_tp_origin_bins_a_perfect_score_below_one(): + """A 1.0 detection must land in the top bin, not spill into a 1.0-1.1 bin.""" + gt = GroundTruth([(0.2, 0.6)], [], fn_confirmed=True) + panos = [{"pano": "a", "preds": [(0.2, 0.6, 1.0)], "gt": gt}] + counts = lfs.tp_origin_by_bin(panos, {"a": ["reviewed"]}, RSQ) + assert list(counts) == [(0.9, "reviewed")] + + +def test_gt_origins_orders_reviewed_before_missed(): + """Must mirror build_ground_truth's append order, or every attribution is wrong.""" + from rampnet.detection_eval import build_ground_truth + dets = [{"x_normalized": 0.1, "y_normalized": 0.6}, + {"x_normalized": 0.2, "y_normalized": 0.6}, + {"x_normalized": 0.3, "y_normalized": 0.6}] + verdicts = [True, False, "unsure"] + missed = [{"x": 0.7, "y": 0.6}, {"x": 0.8, "y": 0.6, "unsure": True}] + gt = build_ground_truth(dets, verdicts, missed, no_missed=False) + origins = ["reviewed" for v in verdicts if v is True or v == "true"] + origins += ["missed" for m in missed if not m.get("unsure")] + assert len(origins) == len(gt.gt_points) + assert origins == ["reviewed", "missed"] + assert gt.gt_points[0] == (0.1, 0.6) # the reviewed True detection + assert gt.gt_points[1] == (0.7, 0.6) # the confident missed mark + + +def test_bin_lo_is_immune_to_float_floor_error(): + """0.9 // 0.1 is 8.0 in binary floats; the bin helper must not inherit that.""" + assert lfs._bin_lo(0.9, 0.1) == 0.9 + assert lfs._bin_lo(0.3, 0.1) == 0.3 + assert lfs._bin_lo(0.7, 0.1) == 0.7 + assert lfs._bin_lo(0.89, 0.1) == 0.8 + assert lfs._bin_lo(1.0, 0.1) == 0.9 # top bin, not a phantom 1.0 bin + assert lfs._bin_lo(0.0, 0.1) == 0.0 + + +# --------------------------------------------------------------------------- # +# tag resolution across a re-extraction +# --------------------------------------------------------------------------- # +def test_tag_resolution_all_resolve_when_ids_are_unchanged(): + items = [{"id": "p1_0.10000_0.60000"}, {"id": "p1_0.20000_0.60000"}] + res = lfs.check_tag_resolution(items, {"p1_0.10000_0.60000": "A", + "p1_0.20000_0.60000": "B"}) + assert res["resolved_frac"] == 1.0 + assert res["orphaned"] == [] + + +def test_tag_resolution_reports_orphans_rather_than_dropping_them(): + """A moved peak must surface as an orphan, not silently shrink the correction.""" + items = [{"id": "p1_0.10000_0.60000"}] + res = lfs.check_tag_resolution(items, {"p1_0.10000_0.60000": "A", + "p1_0.99000_0.60000": "A"}) + assert res["orphaned"] == ["p1_0.99000_0.60000"] + assert res["resolved_frac"] == 0.5 + + +def test_tag_resolution_flags_newly_untagged_items(): + items = [{"id": "p1_0.10000_0.60000"}, {"id": "p1_0.50000_0.60000"}] + res = lfs.check_tag_resolution(items, {"p1_0.10000_0.60000": "A"}) + assert res["untagged"] == ["p1_0.50000_0.60000"] + + +def test_tag_resolution_with_no_tags_is_vacuously_fine(): + assert lfs.check_tag_resolution([{"id": "x"}], {})["resolved_frac"] == 1.0 + + +# --------------------------------------------------------------------------- # +# storage floor / recall ceiling (labeler#28, labeler#27 stage 4) +# --------------------------------------------------------------------------- # +def test_gt_best_candidate_reports_the_claiming_prediction(): + gt = GroundTruth([(0.2, 0.6), (0.8, 0.6)], [], fn_confirmed=True) + panos = [{"pano": "a", "preds": [(0.2, 0.6, 0.91), (0.8, 0.6, 0.07)], "gt": gt}] + assert sorted(x for x in lfs.gt_best_candidate(panos, RSQ) if x) == [0.07, 0.91] + + +def test_gt_best_candidate_is_none_for_an_unfound_ramp(): + gt = GroundTruth([(0.2, 0.6)], [], fn_confirmed=True) + panos = [{"pano": "a", "preds": [(0.9, 0.9, 0.99)], "gt": gt}] + assert lfs.gt_best_candidate(panos, RSQ) == [None] + + +def test_gt_best_candidate_skips_unconfirmed_panos(): + """Must share the recall denominator with the sweep, or the bands don't add up.""" + gt = GroundTruth([(0.2, 0.6)], [], fn_confirmed=False) + panos = [{"pano": "a", "preds": [(0.2, 0.6, 0.9)], "gt": gt}] + assert lfs.gt_best_candidate(panos, RSQ) == [] + + +def test_gt_best_candidate_takes_the_highest_when_several_are_in_range(): + """Greedy matching is highest-first, which is what makes one pass serve all floors.""" + gt = GroundTruth([(0.5, 0.6)], [], fn_confirmed=True) + panos = [{"pano": "a", "preds": [(0.5, 0.6, 0.4), (0.501, 0.6, 0.8)], "gt": gt}] + assert lfs.gt_best_candidate(panos, RSQ) == [0.8] + + +def test_floor_report_bands_count_ramps_lost_by_raising_the_floor(): + gt = GroundTruth([(0.1, 0.6), (0.4, 0.6), (0.7, 0.6)], [], fn_confirmed=True) + panos = [{"pano": "a", "gt": gt, "preds": [ + (0.1, 0.6, 0.07), # best candidate below 0.10 -> lost at a 0.10 floor + (0.4, 0.6, 0.15), # survives 0.10, in the [0.10, 0.20) band + (0.7, 0.6, 0.80), # well clear + ]}] + rep = lfs.floor_report(panos, RSQ) + assert rep["n_gt"] == 3 + assert rep["bands"]["[0.05,0.10)"] == 1 + assert rep["bands"]["[0.10,0.20)"] == 1 + assert math.isclose(rep["recall_at"]["0.05"], 1.0) + assert math.isclose(rep["recall_at"]["0.10"], 2 / 3) + assert math.isclose(rep["recall_at"]["0.55"], 1 / 3) + + +def test_floor_report_recall_at_matches_the_swept_recall(): + """The ceiling must equal the sweep's recall at the same threshold, or one is wrong.""" + panos = _panos(seed=13) + rep = lfs.floor_report(panos, RSQ, floors=(0.1, 0.3, 0.55)) + for thr in (0.1, 0.3, 0.55): + swept = lfs.sweep_rows(panos, [thr], RSQ)[0]["recall"] + assert math.isclose(rep["recall_at"][f"{thr:.2f}"], swept, rel_tol=1e-9) + + +def test_floor_report_recall_is_monotone_in_the_floor(): + rep = lfs.floor_report(_panos(seed=17), RSQ) + vals = [rep["recall_at"][k] for k in sorted(rep["recall_at"])] + assert all(b <= a + 1e-12 for a, b in zip(vals, vals[1:])) + + +def test_cap_report_counts_panos_exceeding_the_top_k(): + panos = [{"pano": "a", "preds": [(0.5, 0.5, 0.9)] * 5, "gt": None}, + {"pano": "b", "preds": [(0.5, 0.5, 0.9)] * 2, "gt": None}] + c = lfs.cap_report(panos, floor=0.1, top_k=3) + assert c["max"] == 5 and c["n_over_cap"] == 1 and c["n_panos"] == 2 + + +def test_cap_report_respects_the_floor(): + panos = [{"pano": "a", "preds": [(0.5, 0.5, 0.9), (0.5, 0.5, 0.06)], "gt": None}] + assert lfs.cap_report(panos, floor=0.10, top_k=50)["max"] == 1 + assert lfs.cap_report(panos, floor=0.05, top_k=50)["max"] == 2