221 lines
No EOL
12 KiB
Markdown
221 lines
No EOL
12 KiB
Markdown
# Pipeline benchmark (A / B / C)
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Benchmark of the three segmentation pipelines — A in-house Unet3D, B nnU-Net v2
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(NTUHLPLTrainer), C MONAI UNet — driven by `scripts/benchmark_pipelines.py`
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(DICE, training time, inference time, params, VRAM). Protocol details live in
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the script docstring; this file records what has been run and what the machine
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currently supports.
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## Data chain status (2026-10-04)
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The M6 labeled set was fully rebuilt on 2026-10-04 (see
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`scripts/02_build_m6_dataset.py`): T1c fixed, best-of-ANTs
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(`ants_rigid` / `ants_affine` / `ants_syn`, winner by Mattes MI), TV warped
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onto the T1c grid with SMD+linear. After the repair pass:
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- `data/manifests/m6_labeled.jsonl`: **1312** volumes (was 1094 pre-repair),
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`m6_unlabeled.jsonl`: **229**
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- all 1541 m6 processed volumes (`data/proc/<key>{,_label}.nii.gz`, 1mm
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cropped/normalized) regenerated, 0 errors
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- patient-level splits rebuilt (`scripts/05_build_splits.py`, seed 0):
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**4200** labeled rows (ntuh + m6) → train 3570 / val 211 / test 419
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volumes, 3893 subjects; unlabeled pool 13280
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- tumor volume stats: median 3116 mm3, p95 24.8k, max 3.2M, zero_frac 0.12%
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## Dataset exclusions (2026-10-04)
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Row-level curation lives in `src/exclusions.py` and is applied at the **first
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stage** by `scripts/make_tv_slices.py` (right after the rows are fetched from
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the sources, before any split): it classifies every labeled volume and writes
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its TV-slice QA figure to `data/tv_slices/inclusion/` (kept) or
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`data/tv_slices/exclusion/` (excluded, reason in the title). `05_build_splits`
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re-applies the same rules **after** split assignment (kept rows retain their
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split; the subject-level shuffle boundaries are unchanged):
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- total TV > 1e6 mm3: **1** row (ntuh_6019528, label outlines the full skull)
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- single target (1 connected component) with TV < 100 mm3: **109** rows
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- non-brain images: **33** rows (all ntuh — spine/neck/torso CyberKnife
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cases whose series names are indistinguishable from brain T1c; identified
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by visual review of the TV-slice figures, now in `data/tv_slices/exclusion/`)
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Audit trail: `data/manifests/excluded.jsonl` (143 rows, reason + split);
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pre-exclusion manifests in `data/manifests/backup_20261004/`. New dataset:
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**4057** labeled rows → train 3447 / val 205 / test 405 (3771 subjects);
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unlabeled pool unchanged (13280); volume stats now median 3212 mm3, p95
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23.4k, max 163k (m6 meningioma), zero_frac still 0.12% (5 rows, e.g.
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ntuh_3436472 ×3).
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After the initial 39-row non-brain list, expert inspection overturned 6
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(skull-base / posterior-fossa brain targets that look like cervical spine at
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the TV-max slice: ntuh_5902230, ntuh_5231609, ntuh_5083686, ntuh_5157194,
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ntuh_5303758, ntuh_6373987). The remaining 33 were re-audited at the
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mid-cranial slice (widest head-mask axial) — all still non-brain. Review
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protocol now recorded in `src/exclusions.py`: classify at full resolution
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on the mid-cranial slice, not just the TV-max slice.
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## Smoke run (2026-10-04)
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Small/fast end-to-end check that all three pipelines run on the rebuilt data
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(1 epoch, 24 train rows, 3 eval rows, TTA on, GPU 0):
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```
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python scripts/benchmark_pipelines.py --gpu 0 --max-rows 24 --nnu-cases 24 \
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--max-eval-rows 3 --train-epochs 1 --out results/benchmark_pipelines_smoke.json
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```
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| pipeline | wall | s/epoch | tr VRAM | s/vol (TTA) | in VRAM | params | weights | dice (1 epoch) |
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|---|---|---|---|---|---|---|---|---|
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| A Unet3D(16, depth 4) | 17 s | 2.5 (8 steps) | 2.0 GB | 4.12 | 0.5 GB | 1.46 M | 5.9 MB | 0.0000 |
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| B nnU-Net 3d_fullres | 285 s (incl. 83 s plan&preprocess, 4 s raw build) | 73.3 | 9.8 GB | 34.73 | 1.8 GB | 30.8 M | 246 MB | 0.0000 |
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| C MONAI UNet(16→128) | 5 s | 2.5 (8 steps) | 1.7 GB | 0.33 | 0.5 GB | 1.19 M | 4.8 MB | 0.0005 |
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DICE ≈ 0 is expected (models trained for a single epoch on 24 volumes); the
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point of the run was validating the full chain — new m6 native/processed
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manifests, split membership, B's `ntuh + m6_labeled` native join, nnU-Net
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isolated dataset 221 build, inference + dice scoring. All three passed.
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Eval rows for the smoke run were the first three `split_test` volumes (all
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ntuh). Artifacts: `results/benchmark_pipelines_smoke.json`,
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`_per_row.jsonl`, `_bench.png`.
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### Environment fixes made during the check
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- `monai` was missing from `longitudinal-313` (pipeline C failed with
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ModuleNotFoundError) — installed `pip install monai==1.6.1 --no-deps`
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(torch 2.14+cu126 untouched); `import monai` verified.
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## Full-dataset 10-epoch run (2026-10-05, updated dataset)
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`--train-epochs 10`, default budgets on the current curated splits: **2750**
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train rows = 80% of split_train (3438 labeled), **40** eval rows = 10% of
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split_test (404), TTA on. The 2026-10-04 interim run (pre-exclusion budget
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2856/42: A 0.0008, C 0.2119) is superseded; its JSONs are kept in
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`results/backup_20261004/` (smoke B checkpoint in
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`nnu/results/backup_20261004_Dataset221_smoke/`).
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```
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python scripts/benchmark_pipelines.py --pipelines C --gpu 2 --train-epochs 10 \
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--out results/benchmark_full_C.json # done
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python scripts/benchmark_pipelines.py --pipelines A --gpu 0 --train-epochs 10 \
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--a-fg-ce-weight 500 --out results/benchmark_full_A.json # done
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# A baseline (uniform crops) on this dataset: dice 0.0026, see table;
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# checkpoint runs_bench/A/best_baseline_10ep_uniform_crop.pt
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NNU_ROOT=/home/xfr/longitudinal_nnu BENCH_B_DIR=/home/xfr/longitudinal_benchB \
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python scripts/benchmark_pipelines.py --pipelines B --gpu 1 --train-epochs 10 \
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--out results/benchmark_full_B.json # done (local-disk NNU_ROOT, see below)
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```
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| pipeline | train s/epoch | wall | tr VRAM | s/vol (TTA) | in VRAM | params | weights | dice @10ep |
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| C MONAI UNet(16→128) | 256.7 (916 steps) | 43 min | 0.4 GB | 0.37 ± 0.06 | 0.5 GB | 1.19 M | 14.3 MB | **0.2162** |
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| A Unet3D(16, depth 4), improved (fg crops + CE w=500) | 232.3 (916 steps) | 41 min | 2.0 GB | 3.60 ± 0.76 | 0.5 GB | 1.46 M | 17.6 MB | **0.0369** |
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| A baseline (uniform crops, unweighted loss) | 216.6 (916 steps) | 45 min | 2.0 GB | 3.33 ± 0.86 | 0.5 GB | 1.46 M | 17.6 MB | **0.0026** |
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| B nnU-Net 3d_fullres | 331.5 (incl. 275-case val/epoch) | ~2.5 h (raw 6 m, prep 62 m, train 55 m, infer 14 m) | 9.3 GB | 21.84 | 1.8 GB | 30.8 M | 246 MB | **0.1424** (hard 0.1424) |
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C: 25/39 timed test vols with dice > 0.001 (max 0.7392); loss 0.403 → 0.182.
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10 epochs is far below convergence for these sparse targets (median tumor
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3.2 k mm³); A/C numbers at this budget are intermediate. Checkpoints at
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`runs_bench/{A,C}/best.pt` (extend with `--resume --train-epochs <total>`).
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### A: why dice ≈ 0, and the fix (2026-10-05)
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The A baseline predicts all-background (38/39 test rows dice exactly 0.0).
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Root-cause work:
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1. **Uniform random 96³ crops starve the loss of foreground.** A 96³ patch is
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885 k voxels; the median tumor is ~4.6 k mm³ (0.02–0.05% of a head volume).
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Simulated uniform crops contain the whole blob in only 8–37% of patches and
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a *median* crop often has 0 tumor voxels. Pipeline C's
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`RandCropByPosNegLabeld(pos=1, neg=1)` centers half its patches on the
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tumor — ~4× more foreground supervision per epoch.
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2. **Fg-aware crops alone are not enough: A under-confides.** With the crop
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protocol fixed (`src/dataset.py`), A's loss matches C's at every epoch
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(0.193 vs 0.182 @10ep) yet p_tumor maxes at 0.43 (C: ~1.0): it localizes
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(top-k dice 0.48 on best rows) but never crosses the 0.5 threshold, so
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dice@0.5 stays 0.
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3. **Fix: class-weighted CE** — tumor-class weight `w` in the CE part of the
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per-sample loss (`--a-fg-ce-weight`, `src/losses.py`). 3-epoch probes on
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this dataset: w=20 → pmax median 0.33, dice 0.0; w=100 → 0.41, dice
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0.0002; w=500 → 0.62, dice 0.0188 (34/39 rows > 0.5). Official 10-epoch
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w=500 run: **dice 0.0369** (vs 0.0026 baseline, 14×; 8/39 rows > 0.001,
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best 0.555, pmax median 0.55, 29/39 rows > 0.5) — A now segments, but
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still trails C (0.2162) at this budget; its loss is still descending at
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epoch 10 (0.78 → 0.51), i.e. under-converged vs C (0.41 → 0.18), so the
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gap should narrow with more epochs.
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Changes: `src/dataset.py` (pos/neg crop sampling `pos_neg=0.5`, per-worker
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RNG reseed), `src/losses.py` (`soft_dice_fg`, `fg_ce_weight`), benchmark flags
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`--a-fg-dice` / `--a-fg-ce-weight`, per-row `pmax` in A's `dice_per_row`,
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full traceback in failure JSONs. A/B probes: `results/bench_A_*probe*.json`.
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### B: CIFS failures and local-disk NNU_ROOT (2026-10-05)
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The CIFS `/mnt/b4` mount dropped I/O repeatedly on 2026-10-05 (NAS flapping:
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`OSError [Errno 112] Host is down`, `RuntimeError: Error while setting the
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slice`, one silent `rmtree` failure that made the raw rebuild collide with
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stale entries → `FileExistsError`). Four B attempts died (07:52 plan&prep;
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11:12 raw EEXIST; ~12:40 / ~13:50 / ~14:00 plan&prep). Fixes:
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- `nnu_common.rmtree_verified()` + defensive per-link unlink in
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`make_raw_dataset` (EEXIST on rebuild)
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- failure JSONs now carry a full `traceback` key
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- plan step retries raised 2 → 5 (`N.run(..., retries=5)`)
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- **B's nnU-Net tree moved off CIFS**: `nnu_common.nnu_root()` honors
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`NNU_ROOT` (set to `/home/xfr/longitudinal_nnu`, local ZFS ~1.8 T free) and
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the benchmark's B pred I/O honors `BENCH_B_DIR`
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(`/home/xfr/longitudinal_benchB`). CIFS is then touched only for the
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one-time raw image reads from `/mnt/pve` and small log/JSON appends.
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The pre-`NNU_ROOT` attempt's half-built preprocessed tree under
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`nnu/preprocessed/` is stale — the local tree is the one being used.
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The local-disk run succeeded on attempt 1 (2026-10-05 14:07→16:38 UTC):
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raw build 6.3 min, plan&preprocess 62 min (140 GB preprocessed), 10 epochs
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in 55 min (epoch times 101–167 s train + 275-case validation —
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`nnUNet_compile is enabled`, vs the Oct-4 eager-mode pace of ~2.4 h/epoch),
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inference 21.8 s/vol TTA. Val pseudo dice 0.091 → 0.459, best EMA 0.285;
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test dice 0.1424 (29/39 rows > 0.001, max 0.675). Checkpoints in
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`/home/xfr/longitudinal_nnu/results/Dataset221_T1C_BENCH/...fold_0/`
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(copy to `nnu/results/` for the record if local space is reclaimed).
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## Resuming training (`--resume`)
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All three pipelines can continue from their latest benchmark checkpoint:
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```
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python scripts/benchmark_pipelines.py --pipelines "A B C" --resume \
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--train-epochs 200 --nnu-skip-prep
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```
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- `--train-epochs` is the **total budget** (not epochs added). Each pipeline
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trains from where its checkpoint left off up to that total.
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- **A / C**: load `runs_bench/{A,C}/best.pt` (model + optimizer + scheduler
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state — checkpoints written before 2026-10-04 carry model + epoch only and
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fall back to fresh optimizer/scheduler) and run the remaining epochs; the
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run is a no-op train (eval only) when the checkpoint already reached the
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total.
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- **B**: appends nnU-Net's `--c` flag (continue `fold_0` from
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`checkpoint_latest.pth`, native optimizer + LR-schedule restore) and
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`NNUNet_pl_EPOCHS = --train-epochs`. Pair with `--nnu-skip-prep` so the
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preprocessed tree isn't `--clean`ed; the isolated Dataset221 is rebuilt
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otherwise and a finished run's `checkpoint_latest.pth` is gone, so a
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completed run + a bigger budget retrains B from scratch (interrupted runs
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keep `checkpoint_latest.pth` and resume correctly).
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- Verified 2026-10-04: A/C resumed epoch 1 → 3 (model restored, fresh
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opt/sched for the old-format ckpt), re-run at 3/3 trained 0 epochs, B
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continued from epoch 1 with LR restored mid-decay (0.00536 @ start of
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epoch 2).
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## Readiness notes for a full run
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- Budget knobs: `--train-epochs` (default 100), `--max-rows` (default 80% of
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split_train = 2750 for A/C and for B's `--nnu-cases` on the current curated
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splits; was 2856 pre-exclusion), `--max-eval-rows` (default 10% of
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split_test = 40), `--no-tta`.
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- `--skip-train` is only usable for **A** (`runs/round0/best.pt`, trained
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2026-09-25 on the *previous* split — a few patients moved between
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train/test in the rebuild, so its test dice carries a small leakage
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caveat). No study checkpoints exist for B (Dataset210) or C
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(`runs_monai/round0/best.pt`); they must be trained.
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- GPUs: 3× 80 GB available; the benchmark uses one (`--gpu`).
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- B's one-time cost scales with `--nnu-cases` (plan_and_preprocess ≈ 3.5 s/case
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at 24 cases with 8 preprocess procs, plus training time at the planned
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batch/patch). |