From def73d35e6a4b217dd51f805aaf25628eda412a3 Mon Sep 17 00:00:00 2001 From: Douglas McLelland Date: Fri, 11 Sep 2026 15:12:58 +0200 Subject: [PATCH 01/15] UORED example integration startpoint --- akida1/model_zoo/uored_vafcls/README.md | 298 +++++++ akida1/model_zoo/uored_vafcls/colab_setup.py | 34 + akida1/model_zoo/uored_vafcls/data/.gitignore | 4 + .../uored_vafcls/docs/README.md.template | 298 +++++++ .../model_zoo/uored_vafcls/docs/metrics.json | 44 + .../uored_vafcls/docs/signal_mosaic.png | Bin 0 -> 277535 bytes akida1/model_zoo/uored_vafcls/local_utils.py | 30 + .../model_zoo/uored_vafcls/models/.gitignore | 4 + .../uored_vafcls/uored_vafcls_benchmark.py | 193 +++++ .../uored_vafcls_cross_validate.py | 432 ++++++++++ .../uored_vafcls/uored_vafcls_data.py | 783 ++++++++++++++++++ .../uored_vafcls/uored_vafcls_eval.py | 218 +++++ .../uored_vafcls/uored_vafcls_model.py | 151 ++++ .../uored_vafcls/uored_vafcls_train.py | 172 ++++ .../uored_vafcls/uored_vafcls_train.sh | 57 ++ .../model_zoo/uored_vafcls/update_readme.py | 11 + 16 files changed, 2729 insertions(+) create mode 100644 akida1/model_zoo/uored_vafcls/README.md create mode 100644 akida1/model_zoo/uored_vafcls/colab_setup.py create mode 100644 akida1/model_zoo/uored_vafcls/data/.gitignore create mode 100644 akida1/model_zoo/uored_vafcls/docs/README.md.template create mode 100644 akida1/model_zoo/uored_vafcls/docs/metrics.json create mode 100644 akida1/model_zoo/uored_vafcls/docs/signal_mosaic.png create mode 100644 akida1/model_zoo/uored_vafcls/local_utils.py create mode 100644 akida1/model_zoo/uored_vafcls/models/.gitignore create mode 100644 akida1/model_zoo/uored_vafcls/uored_vafcls_benchmark.py create mode 100644 akida1/model_zoo/uored_vafcls/uored_vafcls_cross_validate.py create mode 100644 akida1/model_zoo/uored_vafcls/uored_vafcls_data.py create mode 100644 akida1/model_zoo/uored_vafcls/uored_vafcls_eval.py create mode 100644 akida1/model_zoo/uored_vafcls/uored_vafcls_model.py create mode 100644 akida1/model_zoo/uored_vafcls/uored_vafcls_train.py create mode 100644 akida1/model_zoo/uored_vafcls/uored_vafcls_train.sh create mode 100644 akida1/model_zoo/uored_vafcls/update_readme.py diff --git a/akida1/model_zoo/uored_vafcls/README.md b/akida1/model_zoo/uored_vafcls/README.md new file mode 100644 index 0000000..f80c1db --- /dev/null +++ b/akida1/model_zoo/uored_vafcls/README.md @@ -0,0 +1,298 @@ +BrainChip Dev Hub + +# Bearing Fault Diagnosis (UORED-VAFCLS) + +Multi-label detection of rolling-element bearing faults — inner race, outer race, ball and cage — from one second of raw 42 kHz accelerometer data. The four faults are independent labels rather than classes, and a healthy bearing is the all-zero vector, not a fifth class. The reported metric is therefore macro AUROC, which needs no decision threshold. + +The interesting part of this example is not the model. It is the evaluation: this dataset makes it extremely easy to report a number that is far better than the model deserves, in at least two different ways, and most of what follows is about not doing that. + +## Model Card + +**Cross-validated performance** — TBD bearing-disjoint folds × TBD seed. **This is the model's performance.** + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
StageMacro AUROC (mean ± std)Worst foldBest fold
Full precisionTBD ± TBDTBDTBD
Quantized + tuned (4-bit)TBD ± TBD——
AkidaTBD ± TBDTBDTBD
+ +Standard error of the mean on the Akida figure is TBD. Parameters: TBD. Activation sparsity: TBD. + +> **Read the table above, not the one below.** Individual folds range from TBD to TBD — a spread many times larger than the difference between any two architectures anyone would want to compare. Quoting one fold's score as a model's performance is quoting noise. See [Why cross-validation is not optional](#why-cross-validation-is-not-optional). + +Distribution of per-fold AUROC + +**Reference model** — fold TBD only, the weights shipped in `pretrained_models/`. + + + + + + + + + + + + + + + + + + + + + + + + +
Float AUROCQAT AUROCAkida AUROCinnerouterballcage
TBDTBDTBDTBDTBDTBDTBD
+ +Fold TBD is the *first* of the 100 evaluation folds. It was picked because it is first, not because of its score. It scores TBD against the TBD mean above. Every hardware number below comes from this one model on this one fold — **it is not the model's performance**, it is a fixed, reproducible artefact to benchmark. + +**AKD1500 hardware benchmark** + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
MappingNPsPassesCyclesLatency (ms)Total Power (mW)Total Energy (mJ/inf)Dyn. Power (mW)Dyn. Energy (mJ/inf)
MinimalTBDTBDTBDTBDTBDTBDTBDTBD
AllNPsTBDTBDTBDTBDTBDTBDTBDTBD
+ +Full model benchmark + +`Minimal` mapping uses the fewest neural processors that will hold the model; `AllNPs` spreads it over everything available, which usually trades power for latency. This model is an unusual case: it already saturates the device in `Minimal` mode, so the two mappings are nearly identical. + +The model maps entirely to hardware — 1 HRC input convolution, 41 CNP1 convolutional processors, one FNP2 and one FNP3 for the two fully-connected layers, in a single sequence over two passes. One caveat worth knowing before reading the per-layer plot: the `fc` layer's 2432 × 100 weight matrix does not fit in on-chip memory and needs 184 kB of external memory, so unlike most examples here there is real DMA traffic on every inference, and `fc` dominates the latency breakdown. + +Per-layer benchmark + +## Why cross-validation is not optional + +This dataset has 60 recordings from **20 physical bearings**. A fold trains on 12 bearings and is tested on the 8 held out. That is a small enough test set that the choice of which bearings are held out matters more than almost anything about the model: + +- The **across-fold** standard deviation is TBD, and folds range from TBD to TBD with the architecture, the recipe and the seed all held fixed. +- The **within-fold** standard deviation across random seeds is TBD (measured over TBD folds at 3 seeds each) — comparable to the entire across-fold spread. + +So a single-fold, single-seed AUROC cannot distinguish two architectures on this dataset. For scale: the WDCNN network this model descends from and this model itself differ by about 0.03 in their 100-fold means, which is *less* than one fold's seed-to-seed noise. Any comparison made on one fold is a coin toss dressed up as a result. + +The practical rules this example follows: + +1. **The reported number is the mean over all TBD evaluation folds**, produced by `uored_vafcls_cross_validate.py`. The per-fold results are committed in [`docs/cv_results.csv`](docs/cv_results.csv) so the mean is auditable without re-running anything. +2. **Folds 0–4 are the tuning budget.** Every hyperparameter — epochs, learning rate, the QAT schedule, the input encoding constants — was chosen there. Folds 5–104 are never tuned on. +3. **Single-fold artefacts are labelled as such.** The pretrained weights and every hardware measurement come from fold TBD, and are presented as a fixed reference point, not a performance claim. + +## Requirements + +See the [requirements section](../../../README.md#requirements) of the top-level README for the environment used throughout this repository. + +This example additionally uses `scikit-learn` (for `roc_auc_score`, the reported metric), `pooch` (dataset download) and `pandas` (results bookkeeping; also `pyarrow` if you cross-check the split against the published fixtures). + +## Dataset + +**UORED-VAFCLS** — the University of Ottawa Rolling-element Dataset, Vibration and Acoustic Faults under Constant Load and Speed. 60 accelerometer recordings, each 10 s at 42 kHz, from 20 bearings. Every bearing contributes three recordings: one healthy, plus two severities of a single fault mode. + +| | | +|---|---| +| Recordings | 60 (20 bearings × 3) | +| Length | 10 s at 42 kHz (420,000 samples) | +| Model input | 1 s window, framed to 1200 × 35 | +| Labels | 4 independent: inner, outer, ball, cage (healthy = all zero) | +| Windows per fold | 720 training (random crops), 240 held-out (tiled) | + +Downloaded from [Mendeley Data, doi:10.17632/y2px5tg92h.5](https://data.mendeley.com/datasets/y2px5tg92h/5), licensed **CC BY 4.0** (https://creativecommons.org/licenses/by/4.0/): + +> Sehri, Maryam; Dumond, Patrick (2023), *"University of Ottawa Rolling-element Dataset – Vibration and Acoustic Faults under Constant Load and Speed conditions (UORED-VAFCLS)"*, Mendeley Data, V5, doi: 10.17632/y2px5tg92h.5 + +*Changes made, as the licence requires us to state:* the `Accelerometer` column of the raw CSVs is repacked unmodified into a single float32 `.npz`; the pipeline in this example then removes a per-recording DC offset and rescales each recording to an 8-bit range (see [Input encoding](#input-encoding)). + +The evaluation protocol, and the leakage argument behind it, follow: + +> J. P. Vieira, V. A. Bauler, R. K. Rosa, D. Silva, *"Towards a more realistic evaluation of machine learning models for bearing fault diagnosis"*, Mechanical Systems and Signal Processing 258:114640, 2026. doi:10.1016/j.ymssp.2026.114640 ([arXiv:2509.22267](https://arxiv.org/abs/2509.22267), [code](https://github.com/gama-ufsc/bearing-data-leakage)) + +That work reports 0.9100 ± 0.0553 macro AUROC for a WDCNN on this protocol; a local reimplementation scored 0.9034 ± 0.0528 over the same 100 folds. Those figures are quoted for context only — they were measured on a different input pipeline, and are not comparable line-for-line with the Model Card (see [Input encoding](#input-encoding)). + +## Leakage and the bearing-level split + +**In this dataset, fault mode is a property of the bearing.** Bearings 1–5 have inner-race faults, 6–10 outer-race, 11–15 ball, 16–20 cage. No bearing ever exhibits two modes. The consequence is severe: under any split that is not bearing-disjoint, a model that simply re-identifies *which bearing it is listening to* scores near-perfectly without learning anything about faults at all. Near-duplicate slices of the same 10 s recording on both sides of the split are enough to make that trivial. + +The split here partitions on `bearing_id` **before any windowing**, holding out 2 bearings from each of the 4 fault modes — 8 of 20 bearings, and all three of each held-out bearing's recordings together. Enumerating every such choice gives C(5,2)⁴ = 10,000 folds, shuffled once with a fixed seed and indexed by fold number, so fold *N* here is fold *N* in the reference implementation. + +Verify it yourself, over all 105 tuning and evaluation folds: + +```bash +python uored_vafcls_data.py --check-splits +``` + +**There is a second, subtler leak in the raw signals.** Amplitude is partly a recording fingerprint rather than a fault signature. All ten inner-race recordings sit at a mean of 46.6–55.6 while every outer-race and ball recording sits at 0.9–3.1. Used alone, with no model at all, the per-recording *mean* scores 0.776 AUROC on the inner-race label and 0.744 on cage; the per-recording *standard deviation* scores 0.798 on inner-race. A pipeline that feeds unnormalised signals lets the network read a large part of the answer off the DC offset. The input encoding below removes it — which lowers the reported score, and should. + +## Input encoding + +Akida takes 8-bit inputs. That is not merely an implementation detail here, because per-recording signal standard deviation spans **69×** across this dataset (1.87 to 129.4) and 8 bits is 256 levels. A single global scale wide enough for the loudest recording puts the quietest one below a single quantisation level. Per-recording scaling is *forced* by the hardware; it is not a modelling choice. + +So each window is encoded from its own recording's statistics — the centre and the 99.9th percentile of absolute deviation, both computed once over the whole 10 s trace: + +| Headroom | uint8 levels per signal σ (min / median) | clipped at gain 1.0 | at 1.7 | at 2.4 | +|---|---|---|---|---| +| 1.0 | 13.4 / 30.3 | 0.0997% | 4.31% | 11.65% | +| **2.0** (used) | **6.7 / 15.1** | **0.0003%** | **0.026%** | **0.56%** | +| 3.0 | 4.5 / 10.1 | ~0% | 0.001% | 0.016% | + +Regenerate that table for your own cache with `python uored_vafcls_data.py --report-encoding`. + +Two details that are easy to get wrong: + +- **The statistics are per recording, never per window.** A window-dependent scale — min-max normalisation, say — is scale-invariant, which would silently turn the training pipeline's random gain augmentation into a no-op. +- **The gain is applied before encoding**, to the float waveform, so it survives the rescale. The zero point sits at mid-scale, so the ~8% of gains that come out negative are a genuine polarity inversion rather than a clamp. + +The inverse lives *inside* the model, as a `Rescaling` layer, so full-precision training, quantization-aware tuning and Akida inference all see identical values; `cnn2snn` folds it into the first convolution at conversion. + +**This is a deviation from the source pipeline, and it costs accuracy.** The reference implementation applied no normalisation at all and scored 0.9317 ± 0.0436 over these same 100 folds. That number is *not* in the Model Card and could not honestly be put there: it was measured on raw float inputs that still contained the amplitude confound described above. The figures in the Model Card were all re-measured with this encoding. What mitigates the change is that the protocol's own augmentation is a random per-window rescale with σ = 0.7 — the model is *explicitly* trained to be amplitude-invariant, so a fixed per-recording rescale falls inside the augmentation's own hypothesis class. + +## Dataset setup + +The prepared cache downloads automatically on first use, to `--data` (default `./data/uored_vafcls`). To fetch it ahead of time: + +```bash +python uored_vafcls_data.py -d ./data/uored_vafcls +``` + +If you already hold the raw dataset, rebuild the cache from it instead. Download `1_CSV_Raw_Data_Files (.csv)` from the Mendeley record and point at the directory containing the five `1_Healthy` … `5_Cage_Faults` subfolders: + +```bash +python uored_vafcls_data.py --prepare-raw /path/to/1_CSV_Raw_Data_Files +``` + +To share one copy between checkouts, symlink it: + +```bash +ln -s /path/to/shared/uored_vafcls ./data/uored_vafcls +``` + +## Pipeline + +| Stage | Description | +|---|---| +| Full-precision training | 30 epochs, Adam, batch 120 (6 steps/epoch), cosine decay with 5% warmup to a peak LR of 2e-4 | +| Post-training quantization | `cnn2snn quantize` reduces to 4-bit weights and activations (8-bit input) | +| Quantization-aware tuning | 30 epochs at a peak LR of 2e-4, recovering part of the quantization loss | +| Conversion to Akida | `cnn2snn convert` produces the `.fbz` model that runs on hardware | + +Two notes on the recipe, both settled on folds 0–4: + +- **The learning-rate schedule spans the real step count.** The reference implementation hard-coded it and undershot by a sixth, leaving the last few epochs at a floor of zero — effectively training for fewer epochs than it appeared to. Here `total_steps` is derived from the dataset, so it cannot drift if the batch size or window count changes. +- **The QAT budget is larger than this repository's usual 2 epochs.** At 6 steps per epoch, 2 epochs is 12 optimizer steps, which recovers nothing. Even so, 4-bit quantization costs real accuracy on this architecture — compare the float and Akida rows of the Model Card. That cost is a property worth reporting, not a bug to tune away. + +## Reference Models + +The trained models in `pretrained_models/` are stored with Git LFS. See the [trained models section](../../../README.md#trained-models) of the top-level README if they arrive as text pointer files rather than real weights. + +## Usage + +### Notebook + +[`uored_vafcls_notebook_training.ipynb`](uored_vafcls_notebook_training.ipynb) walks through the whole pipeline on a single fold: the dataset and its two leakage traps, how a 1 s waveform becomes a framed uint8 tensor, training, quantization, tuning and conversion. It also loads `docs/cv_results.csv` to show the fold distribution without needing a two-hour run. + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Brainchip-Inc/brainchip_devhub/blob/main/akida1/model_zoo/uored_vafcls/uored_vafcls_notebook_training.ipynb) + +[`uored_vafcls_notebook_benchmark.ipynb`](uored_vafcls_notebook_benchmark.ipynb) evaluates the converted model, measures activation sparsity, and runs the hardware latency and power benchmarks. + +> **Note:** the benchmark notebook needs a physical AKD1500 device, so it does not run in Colab. The power measurements additionally need the FTDI current sensor. + +### Script + +Run the full pipeline on one fold — build, train, quantize, tune, convert, evaluate at each stage, and benchmark: + +```bash +bash uored_vafcls_train.sh [DATADIR] [FOLD] [SEED] +``` + +`FOLD` defaults to TBD and `SEED` to 0. It takes about a minute on a single GPU, plus benchmarking. The nine steps are the standard Akida 1 sequence; see the script for the exact commands. + +### Cross-validation + +**This is the command that produces the headline number.** + +```bash +python uored_vafcls_cross_validate.py --save-metrics +``` + +It runs the whole chain on each of the 100 evaluation folds, appending to `docs/cv_results.csv` after every fold, and takes roughly 30–60 minutes. It resumes by default, so an interrupted run picks up where it stopped. Add `--skip-akida` for a roughly 4× faster float-only sweep when comparing recipes, and `--first-fold`/`--last-fold` to split the work across machines. + +## Contributing and Maintenance + +`README.md` in this folder is **generated** — edit [`docs/README.md.template`](docs/README.md.template), never `README.md` directly. The performance tables are filled from `docs/metrics.json`, which is written by the `--save-metrics` flags: + +```bash +python uored_vafcls_eval.py -l pretrained_models/akdcnn_uored_vafcls.h5 --save-metrics +python uored_vafcls_eval.py -l pretrained_models/akdcnn_uored_vafcls_qat.h5 --save-metrics +python uored_vafcls_eval.py -l pretrained_models/akdcnn_uored_vafcls_qat.fbz --save-metrics +python uored_vafcls_benchmark.py -l pretrained_models/akdcnn_uored_vafcls_qat.fbz --save-metrics +python uored_vafcls_cross_validate.py --save-metrics # 30-60 min +python uored_vafcls_cross_validate.py --first-fold 5 --last-fold 29 --seeds 3 \ + -o docs/cv_seed_std.csv --save-seed-std # the seed-noise figure +python update_readme.py +``` + +Each key in `metrics.json` has exactly one writer, so the scripts can be run in any order without racing. + +**The standing rule:** any change to the architecture, the training recipe or the `ENCODE_*` constants invalidates the cross-validated table, and requires a fresh 100-fold sweep. Re-running the fixed fold is *not* a substitute — that is the whole point of this example. diff --git a/akida1/model_zoo/uored_vafcls/colab_setup.py b/akida1/model_zoo/uored_vafcls/colab_setup.py new file mode 100644 index 0000000..9289aec --- /dev/null +++ b/akida1/model_zoo/uored_vafcls/colab_setup.py @@ -0,0 +1,34 @@ +#This handles the set up necessary to run this notebook in Google Colab. + +import sys, os + +def setup(): + if 'google.colab' not in sys.modules: + print('Not running in Google Colab. No setup needed.') + return + + import subprocess + + def _run(cmd): + print(f'$ {cmd}') + subprocess.run(cmd, shell=True, check=True) + + REPO_URL = "https://github.com/Brainchip-Inc/brainchip_devhub.git" + REPO_DIR = "brainchip_devhub" + + if not os.path.exists(REPO_DIR): + _run(f"git clone --depth 1 {REPO_URL} {REPO_DIR}") + + EXAMPLE_SUBDIR = "akida1/model_zoo/uored_vafcls" + os.chdir(os.path.join(REPO_DIR, EXAMPLE_SUBDIR)) + + repo_root = os.path.abspath(os.path.join(os.getcwd(), "..", "..", "..")) + sys.path.insert(0, repo_root) + sys.path.insert(0, os.getcwd()) + + #install packages + _run("pip install -q akida_models==1.14.0 tf_keras pooch scikit-learn") + + print('Colab setup complete. Working directory:', os.getcwd()) + print('If TensorFlow was just installed, restart the runtime ' + '(Runtime > Restart runtime) and re-run this cell before continuing.') \ No newline at end of file diff --git a/akida1/model_zoo/uored_vafcls/data/.gitignore b/akida1/model_zoo/uored_vafcls/data/.gitignore new file mode 100644 index 0000000..f353a40 --- /dev/null +++ b/akida1/model_zoo/uored_vafcls/data/.gitignore @@ -0,0 +1,4 @@ +# Git to Ignore everything in this directory +* +# Except this .gitignore file +!.gitignore diff --git a/akida1/model_zoo/uored_vafcls/docs/README.md.template b/akida1/model_zoo/uored_vafcls/docs/README.md.template new file mode 100644 index 0000000..f19e151 --- /dev/null +++ b/akida1/model_zoo/uored_vafcls/docs/README.md.template @@ -0,0 +1,298 @@ +BrainChip Dev Hub + +# Bearing Fault Diagnosis (UORED-VAFCLS) + +Multi-label detection of rolling-element bearing faults — inner race, outer race, ball and cage — from one second of raw 42 kHz accelerometer data. The four faults are independent labels rather than classes, and a healthy bearing is the all-zero vector, not a fifth class. The reported metric is therefore macro AUROC, which needs no decision threshold. + +The interesting part of this example is not the model. It is the evaluation: this dataset makes it extremely easy to report a number that is far better than the model deserves, in at least two different ways, and most of what follows is about not doing that. + +## Model Card + +**Cross-validated performance** — {cv_folds} bearing-disjoint folds × {cv_seeds} seed. **This is the model's performance.** + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
StageMacro AUROC (mean ± std)Worst foldBest fold
Full precision{cv_float_auroc_mean} ± {cv_float_auroc_std}{cv_float_auroc_min}{cv_float_auroc_max}
Quantized + tuned (4-bit){cv_qat_auroc_mean} ± {cv_qat_auroc_std}——
Akida{cv_akida_auroc_mean} ± {cv_akida_auroc_std}{cv_akida_auroc_min}{cv_akida_auroc_max}
+ +Standard error of the mean on the Akida figure is {cv_akida_auroc_sem}. Parameters: {params}. Activation sparsity: {sparsity}. + +> **Read the table above, not the one below.** Individual folds range from {cv_akida_auroc_min} to {cv_akida_auroc_max} — a spread many times larger than the difference between any two architectures anyone would want to compare. Quoting one fold's score as a model's performance is quoting noise. See [Why cross-validation is not optional](#why-cross-validation-is-not-optional). + +Distribution of per-fold AUROC + +**Reference model** — fold {fixed_fold} only, the weights shipped in `pretrained_models/`. + + + + + + + + + + + + + + + + + + + + + + + + +
Float AUROCQAT AUROCAkida AUROCinnerouterballcage
{float_auroc}{qat_auroc}{akida_auroc}{akida_auroc_inner}{akida_auroc_outer}{akida_auroc_ball}{akida_auroc_cage}
+ +Fold {fixed_fold} is the *first* of the 100 evaluation folds. It was picked because it is first, not because of its score. It scores {cv_fixed_fold_akida_auroc} against the {cv_akida_auroc_mean} mean above. Every hardware number below comes from this one model on this one fold — **it is not the model's performance**, it is a fixed, reproducible artefact to benchmark. + +**AKD1500 hardware benchmark** + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
MappingNPsPassesCyclesLatency (ms)Total Power (mW)Total Energy (mJ/inf)Dyn. Power (mW)Dyn. Energy (mJ/inf)
Minimal{minimal_nps}{minimal_passes}{minimal_cycles}{minimal_latency_ms}{minimal_total_P}{minimal_total_E}{minimal_dyn_P}{minimal_dyn_E}
AllNPs{allnps_nps}{allnps_passes}{allnps_cycles}{allnps_latency_ms}{allnps_total_P}{allnps_total_E}{allnps_dyn_P}{allnps_dyn_E}
+ +Full model benchmark + +`Minimal` mapping uses the fewest neural processors that will hold the model; `AllNPs` spreads it over everything available, which usually trades power for latency. This model is an unusual case: it already saturates the device in `Minimal` mode, so the two mappings are nearly identical. + +The model maps entirely to hardware — 1 HRC input convolution, 41 CNP1 convolutional processors, one FNP2 and one FNP3 for the two fully-connected layers, in a single sequence over two passes. One caveat worth knowing before reading the per-layer plot: the `fc` layer's 2432 × 100 weight matrix does not fit in on-chip memory and needs 184 kB of external memory, so unlike most examples here there is real DMA traffic on every inference, and `fc` dominates the latency breakdown. + +Per-layer benchmark + +## Why cross-validation is not optional + +This dataset has 60 recordings from **20 physical bearings**. A fold trains on 12 bearings and is tested on the 8 held out. That is a small enough test set that the choice of which bearings are held out matters more than almost anything about the model: + +- The **across-fold** standard deviation is {cv_akida_auroc_std}, and folds range from {cv_akida_auroc_min} to {cv_akida_auroc_max} with the architecture, the recipe and the seed all held fixed. +- The **within-fold** standard deviation across random seeds is {cv_seed_std} (measured over {cv_seed_std_folds} folds at 3 seeds each) — comparable to the entire across-fold spread. + +So a single-fold, single-seed AUROC cannot distinguish two architectures on this dataset. For scale: the WDCNN network this model descends from and this model itself differ by about 0.03 in their 100-fold means, which is *less* than one fold's seed-to-seed noise. Any comparison made on one fold is a coin toss dressed up as a result. + +The practical rules this example follows: + +1. **The reported number is the mean over all {cv_folds} evaluation folds**, produced by `uored_vafcls_cross_validate.py`. The per-fold results are committed in [`docs/cv_results.csv`](docs/cv_results.csv) so the mean is auditable without re-running anything. +2. **Folds 0–4 are the tuning budget.** Every hyperparameter — epochs, learning rate, the QAT schedule, the input encoding constants — was chosen there. Folds 5–104 are never tuned on. +3. **Single-fold artefacts are labelled as such.** The pretrained weights and every hardware measurement come from fold {fixed_fold}, and are presented as a fixed reference point, not a performance claim. + +## Requirements + +See the [requirements section](../../../README.md#requirements) of the top-level README for the environment used throughout this repository. + +This example additionally uses `scikit-learn` (for `roc_auc_score`, the reported metric), `pooch` (dataset download) and `pandas` (results bookkeeping; also `pyarrow` if you cross-check the split against the published fixtures). + +## Dataset + +**UORED-VAFCLS** — the University of Ottawa Rolling-element Dataset, Vibration and Acoustic Faults under Constant Load and Speed. 60 accelerometer recordings, each 10 s at 42 kHz, from 20 bearings. Every bearing contributes three recordings: one healthy, plus two severities of a single fault mode. + +| | | +|---|---| +| Recordings | 60 (20 bearings × 3) | +| Length | 10 s at 42 kHz (420,000 samples) | +| Model input | 1 s window, framed to 1200 × 35 | +| Labels | 4 independent: inner, outer, ball, cage (healthy = all zero) | +| Windows per fold | 720 training (random crops), 240 held-out (tiled) | + +Downloaded from [Mendeley Data, doi:10.17632/y2px5tg92h.5](https://data.mendeley.com/datasets/y2px5tg92h/5), licensed **CC BY 4.0** (https://creativecommons.org/licenses/by/4.0/): + +> Sehri, Maryam; Dumond, Patrick (2023), *"University of Ottawa Rolling-element Dataset – Vibration and Acoustic Faults under Constant Load and Speed conditions (UORED-VAFCLS)"*, Mendeley Data, V5, doi: 10.17632/y2px5tg92h.5 + +*Changes made, as the licence requires us to state:* the `Accelerometer` column of the raw CSVs is repacked unmodified into a single float32 `.npz`; the pipeline in this example then removes a per-recording DC offset and rescales each recording to an 8-bit range (see [Input encoding](#input-encoding)). + +The evaluation protocol, and the leakage argument behind it, follow: + +> J. P. Vieira, V. A. Bauler, R. K. Rosa, D. Silva, *"Towards a more realistic evaluation of machine learning models for bearing fault diagnosis"*, Mechanical Systems and Signal Processing 258:114640, 2026. doi:10.1016/j.ymssp.2026.114640 ([arXiv:2509.22267](https://arxiv.org/abs/2509.22267), [code](https://github.com/gama-ufsc/bearing-data-leakage)) + +That work reports 0.9100 ± 0.0553 macro AUROC for a WDCNN on this protocol; a local reimplementation scored 0.9034 ± 0.0528 over the same 100 folds. Those figures are quoted for context only — they were measured on a different input pipeline, and are not comparable line-for-line with the Model Card (see [Input encoding](#input-encoding)). + +## Leakage and the bearing-level split + +**In this dataset, fault mode is a property of the bearing.** Bearings 1–5 have inner-race faults, 6–10 outer-race, 11–15 ball, 16–20 cage. No bearing ever exhibits two modes. The consequence is severe: under any split that is not bearing-disjoint, a model that simply re-identifies *which bearing it is listening to* scores near-perfectly without learning anything about faults at all. Near-duplicate slices of the same 10 s recording on both sides of the split are enough to make that trivial. + +The split here partitions on `bearing_id` **before any windowing**, holding out 2 bearings from each of the 4 fault modes — 8 of 20 bearings, and all three of each held-out bearing's recordings together. Enumerating every such choice gives C(5,2)⁴ = 10,000 folds, shuffled once with a fixed seed and indexed by fold number, so fold *N* here is fold *N* in the reference implementation. + +Verify it yourself, over all 105 tuning and evaluation folds: + +```bash +python uored_vafcls_data.py --check-splits +``` + +**There is a second, subtler leak in the raw signals.** Amplitude is partly a recording fingerprint rather than a fault signature. All ten inner-race recordings sit at a mean of 46.6–55.6 while every outer-race and ball recording sits at 0.9–3.1. Used alone, with no model at all, the per-recording *mean* scores 0.776 AUROC on the inner-race label and 0.744 on cage; the per-recording *standard deviation* scores 0.798 on inner-race. A pipeline that feeds unnormalised signals lets the network read a large part of the answer off the DC offset. The input encoding below removes it — which lowers the reported score, and should. + +## Input encoding + +Akida takes 8-bit inputs. That is not merely an implementation detail here, because per-recording signal standard deviation spans **69×** across this dataset (1.87 to 129.4) and 8 bits is 256 levels. A single global scale wide enough for the loudest recording puts the quietest one below a single quantisation level. Per-recording scaling is *forced* by the hardware; it is not a modelling choice. + +So each window is encoded from its own recording's statistics — the centre and the 99.9th percentile of absolute deviation, both computed once over the whole 10 s trace: + +| Headroom | uint8 levels per signal σ (min / median) | clipped at gain 1.0 | at 1.7 | at 2.4 | +|---|---|---|---|---| +| 1.0 | 13.4 / 30.3 | 0.0997% | 4.31% | 11.65% | +| **2.0** (used) | **6.7 / 15.1** | **0.0003%** | **0.026%** | **0.56%** | +| 3.0 | 4.5 / 10.1 | ~0% | 0.001% | 0.016% | + +Regenerate that table for your own cache with `python uored_vafcls_data.py --report-encoding`. + +Two details that are easy to get wrong: + +- **The statistics are per recording, never per window.** A window-dependent scale — min-max normalisation, say — is scale-invariant, which would silently turn the training pipeline's random gain augmentation into a no-op. +- **The gain is applied before encoding**, to the float waveform, so it survives the rescale. The zero point sits at mid-scale, so the ~8% of gains that come out negative are a genuine polarity inversion rather than a clamp. + +The inverse lives *inside* the model, as a `Rescaling` layer, so full-precision training, quantization-aware tuning and Akida inference all see identical values; `cnn2snn` folds it into the first convolution at conversion. + +**This is a deviation from the source pipeline, and it costs accuracy.** The reference implementation applied no normalisation at all and scored 0.9317 ± 0.0436 over these same 100 folds. That number is *not* in the Model Card and could not honestly be put there: it was measured on raw float inputs that still contained the amplitude confound described above. The figures in the Model Card were all re-measured with this encoding. What mitigates the change is that the protocol's own augmentation is a random per-window rescale with σ = 0.7 — the model is *explicitly* trained to be amplitude-invariant, so a fixed per-recording rescale falls inside the augmentation's own hypothesis class. + +## Dataset setup + +The prepared cache downloads automatically on first use, to `--data` (default `./data/uored_vafcls`). To fetch it ahead of time: + +```bash +python uored_vafcls_data.py -d ./data/uored_vafcls +``` + +If you already hold the raw dataset, rebuild the cache from it instead. Download `1_CSV_Raw_Data_Files (.csv)` from the Mendeley record and point at the directory containing the five `1_Healthy` … `5_Cage_Faults` subfolders: + +```bash +python uored_vafcls_data.py --prepare-raw /path/to/1_CSV_Raw_Data_Files +``` + +To share one copy between checkouts, symlink it: + +```bash +ln -s /path/to/shared/uored_vafcls ./data/uored_vafcls +``` + +## Pipeline + +| Stage | Description | +|---|---| +| Full-precision training | 30 epochs, Adam, batch 120 (6 steps/epoch), cosine decay with 5% warmup to a peak LR of 2e-4 | +| Post-training quantization | `cnn2snn quantize` reduces to 4-bit weights and activations (8-bit input) | +| Quantization-aware tuning | 30 epochs at a peak LR of 2e-4, recovering part of the quantization loss | +| Conversion to Akida | `cnn2snn convert` produces the `.fbz` model that runs on hardware | + +Two notes on the recipe, both settled on folds 0–4: + +- **The learning-rate schedule spans the real step count.** The reference implementation hard-coded it and undershot by a sixth, leaving the last few epochs at a floor of zero — effectively training for fewer epochs than it appeared to. Here `total_steps` is derived from the dataset, so it cannot drift if the batch size or window count changes. +- **The QAT budget is larger than this repository's usual 2 epochs.** At 6 steps per epoch, 2 epochs is 12 optimizer steps, which recovers nothing. Even so, 4-bit quantization costs real accuracy on this architecture — compare the float and Akida rows of the Model Card. That cost is a property worth reporting, not a bug to tune away. + +## Reference Models + +The trained models in `pretrained_models/` are stored with Git LFS. See the [trained models section](../../../README.md#trained-models) of the top-level README if they arrive as text pointer files rather than real weights. + +## Usage + +### Notebook + +[`uored_vafcls_notebook_training.ipynb`](uored_vafcls_notebook_training.ipynb) walks through the whole pipeline on a single fold: the dataset and its two leakage traps, how a 1 s waveform becomes a framed uint8 tensor, training, quantization, tuning and conversion. It also loads `docs/cv_results.csv` to show the fold distribution without needing a two-hour run. + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Brainchip-Inc/brainchip_devhub/blob/main/akida1/model_zoo/uored_vafcls/uored_vafcls_notebook_training.ipynb) + +[`uored_vafcls_notebook_benchmark.ipynb`](uored_vafcls_notebook_benchmark.ipynb) evaluates the converted model, measures activation sparsity, and runs the hardware latency and power benchmarks. + +> **Note:** the benchmark notebook needs a physical AKD1500 device, so it does not run in Colab. The power measurements additionally need the FTDI current sensor. + +### Script + +Run the full pipeline on one fold — build, train, quantize, tune, convert, evaluate at each stage, and benchmark: + +```bash +bash uored_vafcls_train.sh [DATADIR] [FOLD] [SEED] +``` + +`FOLD` defaults to {fixed_fold} and `SEED` to 0. It takes about a minute on a single GPU, plus benchmarking. The nine steps are the standard Akida 1 sequence; see the script for the exact commands. + +### Cross-validation + +**This is the command that produces the headline number.** + +```bash +python uored_vafcls_cross_validate.py --save-metrics +``` + +It runs the whole chain on each of the 100 evaluation folds, appending to `docs/cv_results.csv` after every fold, and takes roughly 30–60 minutes. It resumes by default, so an interrupted run picks up where it stopped. Add `--skip-akida` for a roughly 4× faster float-only sweep when comparing recipes, and `--first-fold`/`--last-fold` to split the work across machines. + +## Contributing and Maintenance + +`README.md` in this folder is **generated** — edit [`docs/README.md.template`](docs/README.md.template), never `README.md` directly. The performance tables are filled from `docs/metrics.json`, which is written by the `--save-metrics` flags: + +```bash +python uored_vafcls_eval.py -l pretrained_models/akdcnn_uored_vafcls.h5 --save-metrics +python uored_vafcls_eval.py -l pretrained_models/akdcnn_uored_vafcls_qat.h5 --save-metrics +python uored_vafcls_eval.py -l pretrained_models/akdcnn_uored_vafcls_qat.fbz --save-metrics +python uored_vafcls_benchmark.py -l pretrained_models/akdcnn_uored_vafcls_qat.fbz --save-metrics +python uored_vafcls_cross_validate.py --save-metrics # 30-60 min +python uored_vafcls_cross_validate.py --first-fold 5 --last-fold 29 --seeds 3 \ + -o docs/cv_seed_std.csv --save-seed-std # the seed-noise figure +python update_readme.py +``` + +Each key in `metrics.json` has exactly one writer, so the scripts can be run in any order without racing. + +**The standing rule:** any change to the architecture, the training recipe or the `ENCODE_*` constants invalidates the cross-validated table, and requires a fresh 100-fold sweep. Re-running the fixed fold is *not* a substitute — that is the whole point of this example. diff --git a/akida1/model_zoo/uored_vafcls/docs/metrics.json b/akida1/model_zoo/uored_vafcls/docs/metrics.json new file mode 100644 index 0000000..8b7473a --- /dev/null +++ b/akida1/model_zoo/uored_vafcls/docs/metrics.json @@ -0,0 +1,44 @@ +{ + "cv_folds": "TBD", + "cv_seeds": "TBD", + "cv_float_auroc_mean": "TBD", + "cv_float_auroc_std": "TBD", + "cv_float_auroc_min": "TBD", + "cv_float_auroc_max": "TBD", + "cv_qat_auroc_mean": "TBD", + "cv_qat_auroc_std": "TBD", + "cv_akida_auroc_mean": "TBD", + "cv_akida_auroc_std": "TBD", + "cv_akida_auroc_min": "TBD", + "cv_akida_auroc_max": "TBD", + "cv_akida_auroc_sem": "TBD", + "params": "TBD", + "sparsity": "TBD", + "fixed_fold": "TBD", + "float_auroc": "TBD", + "qat_auroc": "TBD", + "akida_auroc": "TBD", + "akida_auroc_inner": "TBD", + "akida_auroc_outer": "TBD", + "akida_auroc_ball": "TBD", + "akida_auroc_cage": "TBD", + "cv_fixed_fold_akida_auroc": "TBD", + "minimal_nps": "TBD", + "minimal_passes": "TBD", + "minimal_cycles": "TBD", + "minimal_latency_ms": "TBD", + "minimal_total_P": "TBD", + "minimal_total_E": "TBD", + "minimal_dyn_P": "TBD", + "minimal_dyn_E": "TBD", + "allnps_nps": "TBD", + "allnps_passes": "TBD", + "allnps_cycles": "TBD", + "allnps_latency_ms": "TBD", + "allnps_total_P": "TBD", + "allnps_total_E": "TBD", + "allnps_dyn_P": "TBD", + "allnps_dyn_E": "TBD", + "cv_seed_std": "TBD", + "cv_seed_std_folds": "TBD" +} diff --git a/akida1/model_zoo/uored_vafcls/docs/signal_mosaic.png b/akida1/model_zoo/uored_vafcls/docs/signal_mosaic.png new file mode 100644 index 0000000000000000000000000000000000000000..66a870866502050421dabcbd25500993ee97a3c6 GIT binary patch literal 277535 zcmeFZXH-<#)-H@Wp|*;mB4C!RB8X(vRz#8#B!h@#D3qKrqHO?0f`WjG6glUNqJn^c zq>`+XvywC4v*eE64;^(j?rMGFIKJ6%hCI$2%oD`pnfX2zFx+Ui?c8Cy_} z@EktObC_$Vk+t;|E1`o2&Hw!yJQkLQ2QBNDOyFHsT{)v>MMuZDk^HmBFt}2IZV?@w 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quantized; everything + after is left as-is and re-attached to the quantized output. Useful for + bisecting which layer introduces a quantization accuracy drop. + + Note: assumes a linear (single input/output per layer) topology from + `target_layer` onwards, since remaining layers are re-called in sequence. + ''' + layer_names = [layer.name for layer in model.layers] + target_idx = layer_names.index(target_layer) + + # Get sub-model up to and including named layer + sub_model = Model(inputs=model.input, outputs=model.layers[target_idx].output) + # Quantize that sub-model + qsub_model = quantize(sub_model, weight_quantization=4, activ_quantization=4, input_weight_quantization=8) + + # Stitch remaining unquantized layers to the end of that + x = qsub_model.output + for layer in model.layers[target_idx + 1:]: + x = layer(x) + + # Return complete partially-quantized model + pq_model = Model(inputs=qsub_model.input, outputs=x, name='quantized_until_' + target_layer) + return pq_model \ No newline at end of file diff --git a/akida1/model_zoo/uored_vafcls/models/.gitignore b/akida1/model_zoo/uored_vafcls/models/.gitignore new file mode 100644 index 0000000..f353a40 --- /dev/null +++ b/akida1/model_zoo/uored_vafcls/models/.gitignore @@ -0,0 +1,4 @@ +# Git to Ignore everything in this directory +* +# Except this .gitignore file +!.gitignore diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_benchmark.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_benchmark.py new file mode 100644 index 0000000..65bdab0 --- /dev/null +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_benchmark.py @@ -0,0 +1,193 @@ +#!/usr/bin/env python +# Copyright 2025 Brainchip Holdings Ltd. Apache 2.0 License +""" +UORED-VAFCLS per-layer hardware benchmark. + +Runs a per-layer timing benchmark on an Akida UORED-VAFCLS model, prints a +summary table, and generates summary plots. + +The numbers describe one model trained on one fold. They are a property of the +architecture on this hardware, not of the model's diagnostic performance - for +that see uored_vafcls_cross_validate.py. + +Example +------- + python uored_vafcls_benchmark.py -l models/akdcnn_uored_vafcls_qat.fbz +""" +import argparse +import json +import pathlib +import sys +import time + +import akida +import numpy as np +from akida_models.sparsity import compute_sparsity + +from brainchip_utils.hardware_utils import (full_model_benchmark, + get_akida_device, + get_mapping_stats, + per_layer_benchmark) +from brainchip_utils.plot_utils import (plot_full_model_results, + plot_per_layer_results, + pretty_print_sparsity) +from uored_vafcls_data import FIXED_FOLD, get_samples + +if __name__ == '__main__': + parser = argparse.ArgumentParser( + description='Per-layer hardware benchmark for an Akida UORED-VAFCLS model') + parser.add_argument('-l', '--loadmodel', required=True, + help='Model to load (.fbz akida model)') + parser.add_argument('-d', '--data', default='./data/uored_vafcls', + help='Directory holding the prepared .npz cache') + parser.add_argument('--fold', type=int, default=FIXED_FOLD, + help='Fold whose training recordings the benchmark ' + 'windows are drawn from') + parser.add_argument('--seed', type=int, default=0, + help='Random seed for the benchmark sample draw') + parser.add_argument('--save-metrics', action='store_true', + help='Write benchmark values to metrics.json. Only ' + 'valid on the fixed fold') + args = parser.parse_args() + + NUM_SAMPLES = 100 + + if args.save_metrics and args.fold != FIXED_FOLD: + sys.exit(f'--save-metrics writes the README hardware table, which is ' + f'fold {FIXED_FOLD}; refusing to write fold {args.fold}.') + + # ------------------------------------------------------------------------- + # Model + # ------------------------------------------------------------------------- + ak_model = akida.Model(args.loadmodel) + imsize = tuple(ak_model.input_shape) + + # ------------------------------------------------------------------------- + # Device + # ------------------------------------------------------------------------- + device = get_akida_device(target_version=ak_model.ip_version) + if device is None: + sys.exit('No compatible Akida hardware device found. Skipping benchmarking') + + # TODO: Add a check to get clock frequency specific to device + CLOCK_FREQUENCY = 400e6 # 400 MHz for AKD1500 + + # ------------------------------------------------------------------------- + # Sample + # ------------------------------------------------------------------------- + # Processing in Akida is activity dependent (because it exploits sparsity) + # and that activity is dependent on the input. + # That makes it imperative to use real inputs when benchmarking Akida, + # rather than synthetic random samples. + samples = get_samples(args.data, imsize, num_samples=NUM_SAMPLES, + fold=args.fold, seed=args.seed) + + # ------------------------------------------------------------------------- + # Benchmarks + # ------------------------------------------------------------------------- + # Simple benchmarking + # Run a simple benchmark at batch-size=1, Minimal mapping mode + print('Running Simple Benchmark, Minimal map mode, batch-size=1') + + ak_model.map(device, mode=akida.MapMode.Minimal, hw_only=True) + + inf_clks = [] + inf_times = [] + for rr in range(NUM_SAMPLES): + start_t = time.perf_counter_ns() + ak_model.forward(samples[rr:rr + 1], batch_size=1) + inf_times.append(time.perf_counter_ns() - start_t) + # Get the number of on-device clock cycles for that inference + inf_clks.append(ak_model.metrics['inference_clk']) + + mean_inf_clk = np.mean(inf_clks)/CLOCK_FREQUENCY*1e3 # s to ms + mean_inf_time = np.mean(inf_times)*1e-6 # ns to ms + # The timing reported by the device should be very close to that + # measured on the system + print(f'\n Mean inference time (system clock): {mean_inf_time:.3f} ms ') + print(f' Mean on-chip time (via chip clock cycles): {mean_inf_clk:.3f} ms ') + + # ------------------------------------------------------------------------- + # Full-model benchmark (latency + optional power) + # ------------------------------------------------------------------------- + map_modes = ['Minimal', 'AllNps'] + POWER_REPEATS = 10 + full_results = dict() + for mm in map_modes: + map_mode = getattr(akida.MapMode, mm) + print(f'\nRunning full-model benchmark (MapMode={mm}, {POWER_REPEATS} repeat(s))...') + full_results[mm] = full_model_benchmark(ak_model, device, samples, + map_mode=map_mode, + repeats=POWER_REPEATS) + if full_results[mm] is None: + sys.exit(f'The model did not map fully to hardware in {mm} mode; ' + f'benchmarking cannot proceed.') + # Re-map without hw_only to populate ak_model.sequences for stats + ak_model.map(device, mode=map_mode) + num_nps, num_passes, num_sequences = get_mapping_stats(ak_model) + full_results[mm]['num_nps'] = num_nps + full_results[mm]['num_passes'] = num_passes + print(f' Mapping: {num_nps} NP(s), {num_passes} pass(es), {num_sequences} sequence(s)') + if num_sequences > 1: + print('WARNING: note, model not completely mapped to hardware') + + + # ------------------------------------------------------------------------- + # Per-layer Benchmark. Minimal mapping mode, batch-size 1 + # ------------------------------------------------------------------------- + ak_model.map(device, mode=akida.MapMode.Minimal, hw_only=True) + ak_model.summary() + + # Check sparsity per-layer + sparsity_dict = compute_sparsity(ak_model, samples=samples) + pretty_print_sparsity(sparsity_dict) + + print(f'Running per-layer benchmark ({NUM_SAMPLES} samples)...') + per_layer_results = per_layer_benchmark(ak_model, device, samples, repeats=NUM_SAMPLES) + + # ------------------------------------------------------------------------- + # Plots + # ------------------------------------------------------------------------- + # Map without hw_only so ak_model.sequences is available for plot_mapping + ak_model.map(device, mode=akida.MapMode.Minimal) + perlayer_savepath = 'benchmark_results_layers.png' + if args.save_metrics: + perlayer_savepath = pathlib.Path(__file__).parent / 'docs' / ('ref_'+perlayer_savepath) + plot_per_layer_results(per_layer_results, ak_model, sparsity_dict, + model_name=args.loadmodel, + savepath=perlayer_savepath) + print('\nPer-layer results plot saved to ' + str(perlayer_savepath)) + + full_savepath = 'benchmark_results_full.png' + if args.save_metrics: + full_savepath = pathlib.Path(__file__).parent / 'docs' / ('ref_'+full_savepath) + plot_full_model_results(full_results, ak_model, device, + model_name=args.loadmodel, + savepath=full_savepath) + print('Full model results plot saved to ' + str(full_savepath)) + + + if args.save_metrics: + # The is used to update the stored metrics that are used to generate the + # performance tables in the README of this folder. + # This should only be used for code maintenance, when the model or training + # pipeline is updated and a new trained model integrated. + # + # Note: 'sparsity' is deliberately NOT written here - uored_vafcls_eval.py + # owns that key, so that the accuracy table can be regenerated without + # hardware and the two scripts never race to set it. + metrics_path = pathlib.Path(__file__).parent / 'docs' / 'metrics.json' + metrics = json.loads(metrics_path.read_text()) if metrics_path.exists() else {} + for mm, res in full_results.items(): + prefix = mm.lower() + metrics[f'{prefix}_nps'] = str(res['num_nps']) + metrics[f'{prefix}_passes'] = str(res['num_passes']) + metrics[f'{prefix}_cycles'] = f'{res["mean_inf_clk"]:.0f}' + metrics[f'{prefix}_latency_ms'] = f'{res["mean_clk_ms"]:.3f}' + if res['power'] is not None: + metrics[f'{prefix}_total_P'] = f'{res["power"]["avg_total_mw"]:.1f}' + metrics[f'{prefix}_total_E'] = f'{res["power"]["avg_energy_mj"]:.3f}' + metrics[f'{prefix}_dyn_P'] = f'{res["power"]["avg_dynamic_mw"]:.1f}' + metrics[f'{prefix}_dyn_E'] = f'{res["power"]["avg_dynamic_energy_mj"]:.3f}' + metrics_path.write_text(json.dumps(metrics, indent=4) + '\n') + print(f'Metrics saved to {metrics_path}') diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_cross_validate.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_cross_validate.py new file mode 100644 index 0000000..260d8b1 --- /dev/null +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_cross_validate.py @@ -0,0 +1,432 @@ +#!/usr/bin/env python +# Copyright 2025 Brainchip Holdings Ltd. Apache 2.0 License +""" +UORED-VAFCLS cross-validation sweep. + +Runs the whole pipeline - float training, quantization, quantization-aware +tuning, conversion, Akida evaluation - independently on every bearing-disjoint +fold, and reports the mean and spread of macro AUROC for each stage. + +This is the script that produces the headline number for this example, and the +reason it exists is that a single fold does not support a conclusion. There are +only 20 bearings; a fold trains on 12 of them and is tested on 8. The AUROC +spread across folds is 0.04-0.05, and individual folds range from about 0.70 to +about 0.99 with the model and the seed held fixed. Two architectures differing +by a couple of AUROC points cannot be distinguished by one fold, and reporting +one fold's score as a model's performance is reporting noise. + +Folds 0-4 are the tuning budget - the only folds a hyperparameter may be chosen +on. Folds 5-104 are the 100 evaluation folds, and the mean over them is the +number to quote. + +Results accumulate in docs/cv_results.csv, one row per (fold, seed), flushed +after every fold. A run resumes by default, skipping rows already present, so an +interrupted sweep loses at most one fold. + +Runtime is roughly 30-60 s per fold, so about 1-2 hours for the default +100-fold full chain. --skip-akida drops the quantize/convert/Akida stages and is +about four times faster; use it for recipe sweeps. + +Example +------- + # the headline run + python uored_vafcls_cross_validate.py --save-metrics + + # a quick recipe comparison on the tuning folds + python uored_vafcls_cross_validate.py --first-fold 0 --last-fold 4 \\ + --skip-akida -o /tmp/tuning.csv +""" + +import argparse +import csv +import os +import pathlib +import time + +import numpy as np +import tensorflow as tf +from cnn2snn import AkidaVersion, convert, quantize, set_akida_version +from local_utils import quantize_until +from tqdm import tqdm + +from uored_vafcls_data import (BATCH_SIZE, EVAL_FOLDS, FIXED_FOLD, + LABEL_COLUMNS, get_data) +from uored_vafcls_eval import (auroc_scores, evaluate_akida_model, + predict_keras_model) +from uored_vafcls_model import build_uored_vafcls_model +from uored_vafcls_train import train_uored_vafcls + +# Same reason as in the training and evaluation scripts, and it matters most +# here: without deterministic ops, a fold's score moves by up to 0.024 between +# identical runs, which would swamp any recipe comparison this script is used for. +tf.config.experimental.enable_op_determinism() + +DEFAULT_OUT = pathlib.Path(__file__).parent / 'docs' / 'cv_results.csv' + +FIELDNAMES = ( + ['fold', 'seed', 'float_auroc', 'qat_auroc', 'akida_auroc'] + + [f'float_auroc_{name}' for name in LABEL_COLUMNS] + + [f'akida_auroc_{name}' for name in LABEL_COLUMNS] + + ['params', 'n_train_windows', 'n_test_windows', 'seconds'] +) + + +def run_fold(fold, seed=0, data_path='./data/uored_vafcls', epochs=30, + learning_rate=2e-4, qat_epochs=10, qat_learning_rate=5e-5, + batch_size=BATCH_SIZE, skip_akida=False, models_dir=None, + verbose=0): + """Run the full pipeline on one fold and return a result row. + + The stage order reproduces uored_vafcls_train.sh exactly, so a fold's + numbers here match what the shell pipeline would produce for the same + (fold, seed). + + Args: + fold (int): bearing-disjoint fold index. + seed (int): random seed for weights, crops, gains and shuffling. + data_path (str): directory holding the prepared .npz cache. + epochs (int): float training epochs. + learning_rate (float): peak LR for float training. + qat_epochs (int): quantization-aware tuning epochs. + qat_learning_rate (float): peak LR for tuning. + batch_size (int): batch size. + skip_akida (bool): stop after float training, leaving the quantized and + Akida columns empty. + models_dir (str, optional): write the per-fold model files here. Off by + default - a hundred folds' worth of checkpoints is a lot of noise. + verbose (int): passed through to model.fit. + + Returns: + dict: one row keyed by FIELDNAMES. + """ + started = time.time() + + with set_akida_version(AkidaVersion.v1): + model = build_uored_vafcls_model(seed=seed) + train_ds, test_ds = get_data(data_path, model.input_shape[1:], batch_size, + fold=fold, seed=seed) + + row = {name: '' for name in FIELDNAMES} + row.update(fold=fold, seed=seed, params=model.count_params(), + n_train_windows=len(train_ds) * batch_size, + n_test_windows=sum(1 for _ in test_ds.unbatch())) + + # --------------------------------------------------------------------------- + # Full precision + # --------------------------------------------------------------------------- + train_uored_vafcls(model, train_ds, test_ds, epochs, learning_rate, seed=seed) + float_scores = auroc_scores(*predict_keras_model(model, test_ds)) + row['float_auroc'] = f'{float_scores["macro"]:.6f}' + for name in LABEL_COLUMNS: + row[f'float_auroc_{name}'] = f'{float_scores[name]:.6f}' + + if models_dir is not None: + os.makedirs(models_dir, exist_ok=True) + model.save(os.path.join(models_dir, + f'akdcnn_uored_vafcls_fold{fold:03d}_' + f'seed{seed}.h5'), include_optimizer=False) + + if not skip_akida: + # ----------------------------------------------------------------------- + # Quantization, tuning and conversion + # ----------------------------------------------------------------------- + with set_akida_version(AkidaVersion.v1): + # qmodel = quantize(model, input_weight_quantization=8, + # weight_quantization=4, activ_quantization=4) + + # qmodel = quantize_until(model, 'fc_relu') + qmodel = quantize_until(model, 'block6_relu') + + qmodel.summary() + train_uored_vafcls(qmodel, train_ds, test_ds, qat_epochs, + qat_learning_rate, seed=seed) + qat_scores = auroc_scores(*predict_keras_model(qmodel, test_ds)) + row['qat_auroc'] = f'{qat_scores["macro"]:.6f}' + + # ak_model = convert(qmodel) + + # if models_dir is not None: + # qmodel.save(os.path.join(models_dir, + # f'akdcnn_uored_vafcls_fold{fold:03d}_' + # f'seed{seed}_qat.h5'), + # include_optimizer=False) + # ak_model.save(os.path.join(models_dir, + # f'akdcnn_uored_vafcls_fold{fold:03d}_' + # f'seed{seed}_qat.fbz')) + + # # ----------------------------------------------------------------------- + # # Akida + # # ----------------------------------------------------------------------- + # akida_scores = auroc_scores(*evaluate_akida_model(ak_model, test_ds)) + # row['akida_auroc'] = f'{akida_scores["macro"]:.6f}' + # for name in LABEL_COLUMNS: + # row[f'akida_auroc_{name}'] = f'{akida_scores[name]:.6f}' + + row['seconds'] = f'{time.time() - started:.1f}' + return row + + +def _existing_rows(out_path): + """Return the (fold, seed) pairs already present in the results file.""" + if not os.path.exists(out_path): + return set() + with open(out_path, newline='') as handle: + return {(int(r['fold']), int(r['seed'])) for r in csv.DictReader(handle) + if r.get('fold')} + + +def cross_validate(folds, seeds, out_path=DEFAULT_OUT, resume=True, **kwargs): + """Run every (fold, seed) combination, appending results as they complete. + + Args: + folds (iterable): fold indices to run. + seeds (iterable): seeds to run for each fold. + out_path (str): CSV to append results to. + resume (bool): skip (fold, seed) pairs already in out_path. When False, + the file is overwritten. + **kwargs: passed to run_fold. + + Returns: + list: the rows produced by this invocation. + """ + folds, seeds = list(folds), list(seeds) + out_path = str(out_path) + os.makedirs(os.path.dirname(out_path) or '.', exist_ok=True) + + if not resume and os.path.exists(out_path): + os.remove(out_path) + done = _existing_rows(out_path) if resume else set() + + todo = [(f, s) for f in folds for s in seeds if (f, s) not in done] + if done: + print(f'Resuming: {len(done)} of {len(folds) * len(seeds)} ' + f'(fold, seed) results already in {out_path}') + if not todo: + print('Nothing to do.') + return [] + + write_header = not os.path.exists(out_path) + rows = [] + progress = tqdm(todo, desc='Cross-validating', unit='fold') + with open(out_path, 'a', newline='') as handle: + writer = csv.DictWriter(handle, fieldnames=FIELDNAMES) + if write_header: + writer.writeheader() + for fold, seed in progress: + row = run_fold(fold, seed=seed, **kwargs) + writer.writerow(row) + # Flushed per fold so an interrupted sweep loses at most one fold. + handle.flush() + rows.append(row) + recent = [float(r['float_auroc']) for r in rows if r['float_auroc']] + progress.set_postfix(float_mean=f'{np.mean(recent):.4f}') + + return rows + + +def _read_results(out_path): + """Read the results CSV into a dict of column -> list.""" + with open(out_path, newline='') as handle: + return [row for row in csv.DictReader(handle) if row.get('fold')] + + +def _per_fold(rows, column): + """Seed-average a column, returning one value per fold. + + Averaging over seeds before averaging over folds is what the protocol does, + and it matters: it keeps each fold weighted equally regardless of how many + seeds happen to have been run for it. + """ + by_fold = {} + for row in rows: + if row.get(column): + by_fold.setdefault(int(row['fold']), []).append(float(row[column])) + return {fold: float(np.mean(values)) for fold, values in sorted(by_fold.items())} + + +def summarise(rows, fixed_fold=FIXED_FOLD, quiet=False): + """Report the fold distribution for each stage and return the cv_* metrics. + + Args: + rows (list): result rows, as read from the CSV. + fixed_fold (int): the fold the pretrained models come from, reported so + the README can say where it sits in the distribution. + quiet (bool): suppress printing. + + Returns: + dict: the cv_* metrics keys. + """ + seeds = {int(row['seed']) for row in rows} + folds = {int(row['fold']) for row in rows} + metrics = {'cv_folds': str(len(folds)), 'cv_seeds': str(len(seeds))} + + for stage in ('float', 'qat', 'akida'): + per_fold = _per_fold(rows, f'{stage}_auroc') + if not per_fold: + continue + values = np.array(list(per_fold.values())) + mean, std = values.mean(), values.std(ddof=0) + sem = std / len(values) ** 0.5 + + metrics[f'cv_{stage}_auroc_mean'] = f'{mean:.4f}' + metrics[f'cv_{stage}_auroc_std'] = f'{std:.4f}' + if stage in ('float', 'akida'): + metrics[f'cv_{stage}_auroc_min'] = f'{values.min():.4f}' + metrics[f'cv_{stage}_auroc_max'] = f'{values.max():.4f}' + if stage == 'akida': + metrics['cv_akida_auroc_sem'] = f'{sem:.4f}' + if fixed_fold in per_fold: + metrics['cv_fixed_fold_akida_auroc'] = \ + f'{per_fold[fixed_fold]:.4f}' + + if not quiet: + print(f'\n{stage} macro AUROC over {len(values)} folds x ' + f'{len(seeds)} seed(s), seed-averaged per fold:') + print(f' mean {mean:.4f} std {std:.4f} sem {sem:.4f}') + print(f' min {values.min():.4f} median {np.median(values):.4f} ' + f'max {values.max():.4f}') + if len(seeds) > 1: + within = np.mean([np.std(v, ddof=0) for v in + _by_fold_values(rows, f'{stage}_auroc')]) + print(f' mean within-fold seed std {within:.4f} ' + f'<- the spread a single seed hides') + metrics[f'cv_{stage}_auroc_seed_std'] = f'{within:.4f}' + + return metrics + + +def _by_fold_values(rows, column): + """Group a column's raw values by fold, for within-fold seed statistics.""" + by_fold = {} + for row in rows: + if row.get(column): + by_fold.setdefault(int(row['fold']), []).append(float(row[column])) + return [v for v in by_fold.values() if len(v) > 1] + + +def plot_distribution(rows, fixed_fold, savepath): + """Histogram the per-fold AUROCs, marking the fixed fold and the mean.""" + import matplotlib + matplotlib.use('Agg') + import matplotlib.pyplot as plt + + per_fold = _per_fold(rows, 'akida_auroc') or _per_fold(rows, 'float_auroc') + values = np.array(list(per_fold.values())) + + fig, ax = plt.subplots(figsize=(8, 4.5)) + ax.hist(values, bins=20, color='#4C72B0', edgecolor='white') + ax.axvline(values.mean(), color='#C44E52', linewidth=2, + label=f'mean {values.mean():.4f}') + if fixed_fold in per_fold: + ax.axvline(per_fold[fixed_fold], color='#55A868', linestyle='--', + linewidth=2, + label=f'fold {fixed_fold} (pretrained) ' + f'{per_fold[fixed_fold]:.4f}') + ax.set_xlabel('macro AUROC on the held-out bearings') + ax.set_ylabel('folds') + ax.set_title(f'Per-fold performance over {len(values)} ' + f'bearing-disjoint folds') + ax.legend() + fig.tight_layout() + fig.savefig(savepath, dpi=150) + print(f'Distribution plot saved to {savepath}') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('-d', '--data', default='./data/uored_vafcls', + help='Directory holding the prepared .npz cache') + parser.add_argument('--first-fold', type=int, default=EVAL_FOLDS.start, + help='First fold to run (inclusive)') + parser.add_argument('--last-fold', type=int, default=EVAL_FOLDS.stop - 1, + help='Last fold to run (inclusive)') + parser.add_argument('--seeds', type=int, default=1, + help='Number of seeds per fold; seeds are range(N)') + parser.add_argument('-b', '--batch_size', type=int, default=BATCH_SIZE) + parser.add_argument('-e', '--epochs', type=int, default=30) + parser.add_argument('-lr', '--learning_rate', type=float, default=2e-4) + parser.add_argument('--qat-epochs', type=int, default=10) + parser.add_argument('--qat-learning-rate', type=float, default=5e-5) + parser.add_argument('-o', '--out', default=str(DEFAULT_OUT), + help='CSV to append per-fold results to. Two machines ' + 'must not append to the same file') + parser.add_argument('--no-resume', action='store_true', + help='Overwrite the results file instead of resuming') + parser.add_argument('--skip-akida', action='store_true', + help='Float training only - no quantization, tuning or ' + 'Akida evaluation. About four times faster') + parser.add_argument('--keep-models', action='store_true', + help='Write each fold\'s models to models/cv/') + parser.add_argument('--summarise-only', action='store_true', + help='Summarise an existing results file without ' + 'running anything') + parser.add_argument('--save-metrics', action='store_true', + help='Write the cv_* keys and the distribution plot to ' + 'docs/. Requires a complete evaluation sweep') + parser.add_argument('--save-seed-std', action='store_true', + help='Write only cv_seed_std, the mean within-fold ' + 'spread across seeds. For the multi-seed subset ' + 'run, which deliberately covers few folds') + args = parser.parse_args() + + folds = range(args.first_fold, args.last_fold + 1) + + if not args.summarise_only: + cross_validate( + folds, range(args.seeds), out_path=args.out, + resume=not args.no_resume, data_path=args.data, + epochs=args.epochs, learning_rate=args.learning_rate, + qat_epochs=args.qat_epochs, + qat_learning_rate=args.qat_learning_rate, + batch_size=args.batch_size, skip_akida=args.skip_akida, + models_dir='./models/cv' if args.keep_models else None) + + rows = _read_results(args.out) + metrics = summarise(rows) + + if args.save_seed_std: + import json + + # Quoted in the README's cross-validation section as the measured + # single-seed uncertainty. It comes from the multi-seed subset run + # rather than the headline sweep, which is one seed per fold. + groups = _by_fold_values(rows, 'akida_auroc') \ + or _by_fold_values(rows, 'float_auroc') + if not groups: + raise SystemExit('--save-seed-std needs at least two seeds for at ' + 'least one fold; run with --seeds 3.') + seed_std = float(np.mean([np.std(v, ddof=0) for v in groups])) + + metrics_path = pathlib.Path(__file__).parent / 'docs' / 'metrics.json' + stored = json.loads(metrics_path.read_text()) if metrics_path.exists() else {} + stored['cv_seed_std'] = f'{seed_std:.4f}' + stored['cv_seed_std_folds'] = str(len(groups)) + metrics_path.write_text(json.dumps(stored, indent=4) + '\n') + print(f'\nMean within-fold seed std {seed_std:.4f} over {len(groups)} ' + f'folds, saved to {metrics_path}') + + if args.save_metrics: + import json + + # The published aggregate must describe the whole evaluation range, or + # the README would quote a mean over whichever folds happened to run. + have = {int(row['fold']) for row in rows} + missing = set(EVAL_FOLDS) - have + if missing: + raise SystemExit( + f'--save-metrics needs every evaluation fold ' + f'({EVAL_FOLDS.start}-{EVAL_FOLDS.stop - 1}); ' + f'{len(missing)} are missing, e.g. {sorted(missing)[:5]}.') + if not any(row.get('akida_auroc') for row in rows): + raise SystemExit('--save-metrics needs the Akida column; this ' + 'looks like a --skip-akida run.') + + docs = pathlib.Path(__file__).parent / 'docs' + metrics_path = docs / 'metrics.json' + stored = json.loads(metrics_path.read_text()) if metrics_path.exists() else {} + stored.update(metrics) + metrics_path.write_text(json.dumps(stored, indent=4) + '\n') + print(f'\nMetrics saved to {metrics_path}') + + plot_distribution(rows, FIXED_FOLD, + docs / 'ref_cv_auroc_distribution.png') diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_data.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_data.py new file mode 100644 index 0000000..b8bb0f4 --- /dev/null +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_data.py @@ -0,0 +1,783 @@ +#!/usr/bin/env python +# Copyright 2025 Brainchip Holdings Ltd. Apache 2.0 License +""" +UORED-VAFCLS bearing fault data pipeline. + +Turns 10 s raw accelerometer recordings into 1 s uint8 "images" that an Akida +convolutional model can consume directly: + + raw vibration (42 kHz) -> 1 s window (42000 samples) + -> random gain (training only) + -> per-recording DC removal + rescale to uint8 + -> reshape to (1200, 35, 1) + +The reshape is a plain framing, not a spectrogram: row j is samples +[35j, 35j+35), so the row axis is coarse time (1200 frames, 0.833 ms apart) and +the column axis is fine phase within a frame. It happens here rather than in the +model because an Akida graph cannot contain a reshape - the deployed model +receives the already-framed tensor. + +The split is bearing-disjoint and drawn *before* any windowing. Fault mode is +determined by bearing id in this dataset (inner=1-5, outer=6-10, ball=11-15, +cage=16-20), so a model given any split that is not bearing-disjoint can score +near-perfectly by re-identifying the bearing without learning anything about +faults. Each bearing contributes three recordings - its own healthy one plus two +severities of a single fault mode - and holding a bearing out removes all three. + +Folds are enumerated, not tabulated: every way of holding out 2 bearings from +each of the 4 fault modes gives C(5,2)^4 = 10,000 folds, shuffled once with a +fixed seed and indexed by fold number. Folds 0-4 are the tuning budget; folds +5-104 are the 100 evaluation folds. FIXED_FOLD (5) is the one the pretrained +models and the hardware benchmark use. + +Labels are multi-label, not one-of-N: [inner, outer, ball, cage], with healthy +encoded as the all-zero vector rather than a fifth class. The reported metric is +macro AUROC (see uored_vafcls_eval.py) - there is no argmax prediction here, so +an accuracy figure would depend on an arbitrary threshold. + +Usage: + from uored_vafcls_data import get_data, get_test_data, get_samples + + train_ds, test_ds = get_data('./data/uored_vafcls', (1200, 35, 1), + batch_size=120, fold=5, seed=0) + test_ds = get_test_data('./data/uored_vafcls', (1200, 35, 1), fold=5) + samples = get_samples('./data/uored_vafcls', (1200, 35, 1), num_samples=100) + +Check the split protocol, or the input encoding, without training anything: + python uored_vafcls_data.py --check-splits + python uored_vafcls_data.py --report-encoding + +Rebuild the cache from the raw Mendeley CSVs: + python uored_vafcls_data.py --prepare-raw /path/to/1_CSV_Raw_Data_Files +""" + +import functools +import os +import random +from itertools import combinations, product + +import numpy as np +import tensorflow as tf + +# --------------------------------------------------------------------------- +# Dataset definition +# --------------------------------------------------------------------------- +DEFAULT_DATA_PATH = './data/uored_vafcls' +CACHE_NAME = 'uored_vafcls_42khz_60rec.npz' + +# The prepared cache is mirrored so the example runs without a manual download. +# The raw dataset is distributed as per-recording CSVs from Mendeley, which is +# not fetchable as a single file; --prepare-raw rebuilds this .npz from them. +MIRROR_URL = ('https://data.brainchip.com/dataset-mirror/uored_vafcls/' + + CACHE_NAME) +MIRROR_HASH = None # TODO: sha256 once the mirror is uploaded + +SAMPLE_RATE = 42_000 # Hz +SIGNAL_LENGTH = 420_000 # 10 s per recording +N_RECORDINGS = 60 # 20 bearings x (1 healthy + 2 fault severities) + +# Fault mode is a property of the bearing in this dataset. This mapping is what +# makes a non-bearing-disjoint split leak, and it is why the split enumerates +# bearings rather than recordings. +FAULT_BEARING_IDS = { + 'inner': (1, 2, 3, 4, 5), + 'outer': (6, 7, 8, 9, 10), + 'ball': (11, 12, 13, 14, 15), + 'cage': (16, 17, 18, 19, 20), +} +LABEL_COLUMNS = ('inner', 'outer', 'ball', 'cage') +NUM_LABELS = 4 + +# --------------------------------------------------------------------------- +# Split protocol +# --------------------------------------------------------------------------- +N_BEARINGS_PER_FAULT_MODE = 2 # 2 of 5 held out per mode -> 8 of 20 bearings +SPLIT_SEED = 42 # fixes the fold numbering; see the note below +TUNING_FOLDS = range(0, 5) # the only folds a hyperparameter may be chosen on +EVAL_FOLDS = range(5, 105) # the 100 folds the reported mean comes from +FIXED_FOLD = 5 # pretrained models + hardware benchmark + +# --------------------------------------------------------------------------- +# Windowing +# --------------------------------------------------------------------------- +WINDOW_LENGTH = 42_000 # 1.0 s +FRAME_WIDTH = 35 # samples per frame; 42000 / 35 = 1200 frames +INPUT_SHAPE = (WINDOW_LENGTH // FRAME_WIDTH, FRAME_WIDTH, 1) # (1200, 35, 1) + +TRAIN_WINDOWS_PER_RECORDING = 20 # 36 recordings x 20 = 720 windows per epoch +EVAL_WINDOWS_PER_RECORDING = 10 # 24 recordings x 10 = 240 windows, tiled +GAIN_STD = 0.7 # random scalar gain, N(1, GAIN_STD) +BATCH_SIZE = 120 # exact divisor of 720 -> 6 steps per epoch + +# --------------------------------------------------------------------------- +# uint8 input encoding +# --------------------------------------------------------------------------- +# Akida takes 8-bit inputs, and per-recording std in this dataset spans 69x +# (1.87 to 129.4). No single global scale fits that into 256 levels: one wide +# enough for the loudest recording leaves the quietest under one level. So the +# encoding is per *recording* - computed from the whole 10 s trace, never from +# the window being encoded, because a window-dependent scale (min-max, say) is +# scale-invariant and would silently turn the gain augmentation into a no-op. +# +# Removing the per-recording mean is not only a concession to 8 bits. The raw +# signals carry a DC offset that is itself a fault-mode fingerprint: all ten +# inner-race recordings sit at mean 46.6-55.6 while every outer and ball +# recording sits at 0.9-3.1. On its own the per-recording mean scores 0.776 +# macro AUROC on inner-race and the std scores 0.798 - a shortcut the model can +# read without looking at the vibration at all. Centring and rescaling per +# recording deletes it. See the README's "Input encoding" section: this is a +# deliberate deviation from the source pipeline, and the reason the source's +# published figure is not the figure in the Model Card. +ZERO_POINT = 128 # mid-scale, so a negative gain is a real polarity flip +ENCODE_PERCENTILE = 99.9 # robust peak of |x - center|, per recording +ENCODE_HEADROOM = 2.0 # room for the gain augmentation before it clips + + +# --------------------------------------------------------------------------- +# Split protocol +# --------------------------------------------------------------------------- +def held_out_bearings_for_fold(fold, n_per_mode=N_BEARINGS_PER_FAULT_MODE, + seed=SPLIT_SEED): + """Return the sorted bearing ids held out for `fold`. + + Enumerates every way of choosing `n_per_mode` bearings from each of the four + fault modes (2 -> C(5,2)^4 = 10,000 folds), shuffles that list once with a + fixed seed, and indexes it by `fold`. + + The enumeration order and the shuffle are reproduced exactly from the + protocol's reference implementation, so fold N here holds out the same + bearings as fold N there. Do not "tidy" this function: any change to the + ordering silently renumbers all 10,000 folds and every published per-fold + number stops meaning what it says. + + Args: + fold (int): fold index, 0 to 9999. + n_per_mode (int): bearings held out per fault mode. + seed (int): seed for the one-off shuffle of the enumeration. + + Returns: + list: the held-out bearing ids, sorted ascending. + """ + per_mode = [list(combinations(ids, n_per_mode)) + for ids in FAULT_BEARING_IDS.values()] + all_combos = list(product(*per_mode)) + random.Random(seed).shuffle(all_combos) + + if not 0 <= fold < len(all_combos): + raise IndexError(f'fold {fold} out of range for {len(all_combos)} folds') + + selected = all_combos[fold] + return sorted({bearing for mode in selected for bearing in mode}) + + +def fold_rows(bearing_id, fold, n_per_mode=N_BEARINGS_PER_FAULT_MODE, + seed=SPLIT_SEED): + """Split recording indices into train and test rows for `fold`. + + Args: + bearing_id (np.ndarray): per-recording bearing id, shape (N_RECORDINGS,). + fold (int): fold index. + n_per_mode (int): bearings held out per fault mode. + seed (int): seed for the fold enumeration shuffle. + + Returns: + np.ndarray, np.ndarray: training row indices, test row indices. + """ + test_bearings = held_out_bearings_for_fold(fold, n_per_mode, seed) + is_test = np.isin(bearing_id, test_bearings) + return np.flatnonzero(~is_test), np.flatnonzero(is_test) + + +# --------------------------------------------------------------------------- +# Input encoding +# --------------------------------------------------------------------------- +def encoding_constants(signals): + """Compute the per-recording centre and full-scale value. + + Both are derived from the entire 10 s recording, so they are a fixed + property of the recording rather than of any window drawn from it. + + Args: + signals (np.ndarray): float32, shape (N_RECORDINGS, SIGNAL_LENGTH). + + Returns: + np.ndarray, np.ndarray: centre and scale, both float32 (N_RECORDINGS,). + """ + center = signals.mean(axis=1).astype(np.float32) + deviation = np.abs(signals - center[:, None]) + scale = ENCODE_HEADROOM * np.percentile(deviation, ENCODE_PERCENTILE, axis=1) + # return center, scale.astype(np.float32) + # HACK: Hard coding a single scaling factor + center = np.zeros_like(center) + scale = np.ones_like(scale)*127. + return center, scale + + + +def encode_uint8(window, center, scale): + """Encode a float window as uint8 centred on ZERO_POINT. + + The model's Rescaling layer inverts this exactly, so float training, QAT and + Akida inference all see the same values. + + Args: + window (np.ndarray): float32 samples, any shape. + center (float): the recording's centre, from encoding_constants(). + scale (float): the recording's full-scale value. + + Returns: + np.ndarray: uint8, same shape as `window`. + """ + normalised = (window - center) / scale + return np.clip(np.round(ZERO_POINT + 127.0 * normalised), 0, 255).astype(np.uint8) + + +def frame_window(x, width=FRAME_WIDTH): + """Frame the trailing sample axis into (frames, width, 1). + + Leaves any leading batch axes alone, so it works on one window or a stack. + + Args: + x (np.ndarray): shape (..., WINDOW_LENGTH). + width (int): samples per frame. + + Returns: + np.ndarray: shape (..., WINDOW_LENGTH // width, width, 1). + """ + if x.shape[-1] % width: + raise ValueError(f'window length {x.shape[-1]} is not divisible by the ' + f'frame width {width}') + return np.ascontiguousarray(x.reshape(*x.shape[:-1], -1, width))[..., None] + + +# --------------------------------------------------------------------------- +# Cache +# --------------------------------------------------------------------------- +def _cache_path(data_path): + return os.path.join(data_path, CACHE_NAME) + + +@functools.lru_cache(maxsize=1) +def _load_cache(data_path): + """Load the prepared recordings and derive the encoding constants. + + lru_cached because uored_vafcls_cross_validate.py calls get_data() a hundred + times in one process; without this it would re-read ~100 MB and recompute 60 + percentiles on every fold. + + Args: + data_path (str): directory holding (or to receive) the .npz cache. + + Returns: + dict: signals, labels, bearing_id, fault_type, severity, rpm, + waveform_id, center, scale. + """ + path = _cache_path(data_path) + if not os.path.exists(path): + _download_cache(data_path) + + with np.load(path, allow_pickle=False) as data: + cache = {key: data[key] for key in data.files} + + signals = cache['signals'] + if signals.shape != (N_RECORDINGS, SIGNAL_LENGTH): + raise ValueError(f'{path}: expected signals of shape ' + f'{(N_RECORDINGS, SIGNAL_LENGTH)}, got {signals.shape}') + + cache['center'], cache['scale'] = encoding_constants(signals) + return cache + + +def _download_cache(data_path): + """Fetch the prepared .npz from the BrainChip dataset mirror.""" + import pooch + + if MIRROR_HASH is None: + print('Note: MIRROR_HASH is not set yet, so the download cannot be ' + 'checksum-verified.') + os.makedirs(data_path, exist_ok=True) + print(f'Downloading the prepared UORED-VAFCLS cache to {data_path} ...') + pooch.retrieve(url=MIRROR_URL, known_hash=MIRROR_HASH, path=data_path, + fname=CACHE_NAME) + + +def _check_input_shape(input_shape): + if tuple(input_shape) != INPUT_SHAPE: + raise ValueError( + f'This example is built around a fixed input shape of {INPUT_SHAPE} ' + f'(a {WINDOW_LENGTH}-sample window framed {FRAME_WIDTH} samples ' + f'wide), but got {tuple(input_shape)}.') + + +def _labels(cache, rows): + return cache['labels'][rows].astype(np.float32) + + +def _describe(name, cache, rows): + """Print what a set of recordings contains, by bearing and fault mode.""" + bearings = sorted(set(cache['bearing_id'][rows].tolist())) + counts = {} + for fault_type in cache['fault_type'][rows]: + counts[str(fault_type)] = counts.get(str(fault_type), 0) + 1 + breakdown = ', '.join(f'{k} {v}' for k, v in sorted(counts.items())) + print(f'{name}: {len(rows)} recordings from {len(bearings)} bearings ' + f'{bearings} ({breakdown})') + + +# --------------------------------------------------------------------------- +# Window generation +# --------------------------------------------------------------------------- +def _train_dataset(cache, rows, batch_size, dtype, seed): + """Random 1 s crops with a random gain, re-drawn every epoch.""" + signals, center, scale = cache['signals'], cache['center'], cache['scale'] + labels = _labels(cache, rows) + n_items = len(rows) * TRAIN_WINDOWS_PER_RECORDING + + # An independent generator, not the global Keras RNG. The shell pipeline + # re-seeds in a fresh process between building and training, so only an + # independent data RNG makes the in-process cross-validation path and the + # multi-process shell path draw the same crops for a given (fold, seed). + rng = np.random.default_rng(seed) + + def generator(): + for idx in range(n_items): + i = idx % len(rows) + row = rows[i] + start = rng.integers(0, SIGNAL_LENGTH - WINDOW_LENGTH) + window = np.array(signals[row, start:start + WINDOW_LENGTH], + dtype=np.float32) + # Gain is applied to the float waveform, before encoding, so that it + # survives the per-recording rescale instead of cancelling out. + window *= np.float32(rng.normal(1.0, GAIN_STD)) + window = encode_uint8(window, center[row], scale[row]) + yield frame_window(window), labels[i] + + signature = (tf.TensorSpec(shape=INPUT_SHAPE, dtype=tf.uint8), + tf.TensorSpec(shape=(NUM_LABELS,), dtype=tf.float32)) + return ( + tf.data.Dataset.from_generator(generator, output_signature=signature) + # Declared so that len(train_ds) works, which is how the training script + # derives steps_per_epoch for the LR schedule. + .apply(tf.data.experimental.assert_cardinality(n_items)) + .shuffle(n_items, seed=seed, reshuffle_each_iteration=True) + .batch(batch_size, drop_remainder=True) + .map(lambda x, y: (tf.cast(x, dtype), y)) + .prefetch(tf.data.AUTOTUNE) + ) + + +def _eval_arrays(cache, rows): + """Deterministic contiguous tiling: EVAL_WINDOWS_PER_RECORDING per recording. + + Small enough to materialise (240 x 1200 x 35 uint8 is about 10 MB). + """ + signals, center, scale = cache['signals'], cache['center'], cache['scale'] + labels = _labels(cache, rows) + windows, targets = [], [] + for i, row in enumerate(rows): + for j in range(EVAL_WINDOWS_PER_RECORDING): + start = j * WINDOW_LENGTH + window = np.array(signals[row, start:start + WINDOW_LENGTH], + dtype=np.float32) + windows.append(encode_uint8(window, center[row], scale[row])) + targets.append(labels[i]) + return frame_window(np.stack(windows)), np.stack(targets) + + +def _eval_dataset(cache, rows, batch_size, dtype): + x, y = _eval_arrays(cache, rows) + return ( + tf.data.Dataset.from_tensor_slices((x, y)) + .batch(batch_size) + .map(lambda a, b: (tf.cast(a, dtype), b)) + .prefetch(tf.data.AUTOTUNE) + ) + + +# --------------------------------------------------------------------------- +# Public API +# --------------------------------------------------------------------------- +def get_data(data_path=DEFAULT_DATA_PATH, input_shape=INPUT_SHAPE, + batch_size=BATCH_SIZE, dtype=tf.uint8, fold=FIXED_FOLD, seed=0): + """Load the training and held-out datasets for one bearing-disjoint fold. + + There is deliberately no validation split. With 36 training recordings, + carving one out would cost about a sixth of the data and would itself have to + be bearing-disjoint to mean anything; nothing in the pipeline consumes it + (the recipe is a fixed epoch count with no early stopping and no best-weight + restore); and the protocol reserves folds 0-4 for exactly the tuning a + validation set would otherwise serve. The second dataset returned is the + held-out fold - the test set - and no decision may be taken from it. + + Args: + data_path (str): directory holding the .npz cache. Downloaded if absent. + input_shape (tuple): model input shape; must be INPUT_SHAPE. + batch_size (int): the batch size. BATCH_SIZE divides the 720 training + windows exactly, giving 6 steps per epoch. + dtype (tf.dtypes.DType, optional): input data type. Defaults to tf.uint8. + fold (int): bearing-disjoint fold index. Folds 0-4 are the tuning + budget; 5-104 are the evaluation folds. Defaults to FIXED_FOLD. + seed (int): seed for the crop positions, the gains and the shuffle + order. The split itself does not depend on it. + + Returns: + tf.data.Dataset, tf.data.Dataset: training dataset, held-out dataset. + """ + _check_input_shape(input_shape) + cache = _load_cache(data_path) + + train_rows, test_rows = fold_rows(cache['bearing_id'], fold) + print(f'Fold {fold}: holding out bearings ' + f'{held_out_bearings_for_fold(fold)}') + _describe('Train', cache, train_rows) + _describe('Held out', cache, test_rows) + + return (_train_dataset(cache, train_rows, batch_size, dtype, seed), + _eval_dataset(cache, test_rows, batch_size, dtype)) + + +def get_test_data(data_path=DEFAULT_DATA_PATH, input_shape=INPUT_SHAPE, + batch_size=BATCH_SIZE, dtype=tf.uint8, fold=FIXED_FOLD): + """Load only the held-out dataset for one fold. + + Deterministic: EVAL_WINDOWS_PER_RECORDING contiguous windows per recording, + no augmentation, so repeated evaluations of one model agree exactly. + + Args: + data_path (str): directory holding the .npz cache. + input_shape (tuple): model input shape; must be INPUT_SHAPE. + batch_size (int): the batch size. + dtype (tf.dtypes.DType, optional): input data type. Defaults to tf.uint8. + fold (int): bearing-disjoint fold index. Defaults to FIXED_FOLD. + + Returns: + tf.data.Dataset: held-out dataset yielding (uint8 windows, float labels). + """ + _check_input_shape(input_shape) + cache = _load_cache(data_path) + + _, test_rows = fold_rows(cache['bearing_id'], fold) + _describe(f'Held out (fold {fold})', cache, test_rows) + + return _eval_dataset(cache, test_rows, batch_size, dtype) + + +def get_samples(data_path=DEFAULT_DATA_PATH, input_shape=INPUT_SHAPE, + num_samples=1024, fold=FIXED_FOLD, seed=0): + """Load a block of training windows as a plain uint8 numpy array. + + Used for activation-sparsity measurement and hardware benchmarking, both of + which need uint8 numpy input rather than a dataset. The draw is over the + fold's *training* recordings: Akida timings depend on input activity, so the + windows have to be real, but they must not come from held-out data. + + Up to EVAL_WINDOWS_PER_RECORDING per training recording (360 for a standard + fold) are the deterministic tiled windows; beyond that the remainder are + seeded random crops. + + Args: + data_path (str): directory holding the .npz cache. + input_shape (tuple): model input shape; must be INPUT_SHAPE. + num_samples (int): number of windows to return. Defaults to 1024. + fold (int): bearing-disjoint fold index. Defaults to FIXED_FOLD. + seed (int): seed for the draw order and for any random crops. Akida + timings and sparsity depend on input activity, so varying this + varies the measurement. + + Returns: + np.ndarray: shape (num_samples,) + INPUT_SHAPE, dtype uint8. + """ + _check_input_shape(input_shape) + cache = _load_cache(data_path) + signals, center, scale = cache['signals'], cache['center'], cache['scale'] + + train_rows, _ = fold_rows(cache['bearing_id'], fold) + rng = np.random.default_rng(seed) + + tiled, _ = _eval_arrays(cache, train_rows) + idx = rng.permutation(len(tiled))[:num_samples] + samples = tiled[idx] + + shortfall = num_samples - len(samples) + if shortfall > 0: + extra = np.empty((shortfall, WINDOW_LENGTH), dtype=np.uint8) + for k in range(shortfall): + row = train_rows[rng.integers(0, len(train_rows))] + start = rng.integers(0, SIGNAL_LENGTH - WINDOW_LENGTH) + window = np.array(signals[row, start:start + WINDOW_LENGTH], + dtype=np.float32) + extra[k] = encode_uint8(window, center[row], scale[row]) + samples = np.concatenate([samples, frame_window(extra)]) + + return samples.astype(np.uint8) + + +# --------------------------------------------------------------------------- +# Raw data preparation +# --------------------------------------------------------------------------- +FOLDER_FAULT_TYPE = { + '1_Healthy': 'Healthy', + '2_Inner_Race_Faults': 'Inner', + '3_Outer_Race_Faults': 'Outer', + '4_Ball_Faults': 'Ball', + '5_Cage_Faults': 'Cage', +} +FAULT_TYPE_TO_LABEL = {'Inner': 'inner', 'Outer': 'outer', + 'Ball': 'ball', 'Cage': 'cage'} + + +def prepare_raw(raw_dir, data_path=DEFAULT_DATA_PATH): + """Rebuild the .npz cache from the raw Mendeley CSV tree. + + Only needed to reproduce the mirrored cache from the original dataset, or + after changing what the cache stores. Expects the layout of + `1_CSV_Raw_Data_Files (.csv)` from https://data.mendeley.com/datasets/y2px5tg92h/5: + + /1_Healthy/H__0.csv + /2_Inner_Race_Faults/I__.csv + /3_Outer_Race_Faults/O__.csv + /4_Ball_Faults/B__.csv + /5_Cage_Faults/C__.csv + + Folder order and the sorted glob within each folder fix the row ordering, + which is load-bearing: the fold logic indexes recordings by row. + + Args: + raw_dir (str): directory holding the five fault-type subfolders. + data_path (str): directory to write the .npz cache into. + + Returns: + str: path to the written cache. + """ + import glob + + import pandas as pd + + signals = np.zeros((N_RECORDINGS, SIGNAL_LENGTH), dtype=np.float32) + records = [] + + for folder, fault_type in FOLDER_FAULT_TYPE.items(): + directory = os.path.join(raw_dir, folder) + if not os.path.isdir(directory): + raise FileNotFoundError(f'missing raw folder {directory}') + + for path in sorted(glob.glob(os.path.join(directory, '*.csv'))): + stem = os.path.splitext(os.path.basename(path))[0] + _, bearing_id, severity = stem.split('_') + frame = pd.read_csv(path, usecols=['Accelerometer', 'Speed']) + vibration = frame['Accelerometer'].to_numpy(dtype=np.float32) + if vibration.size != SIGNAL_LENGTH: + raise ValueError(f'{os.path.basename(path)}: expected ' + f'{SIGNAL_LENGTH} samples, got {vibration.size}') + + row_idx = len(records) + signals[row_idx] = vibration + label = np.zeros(NUM_LABELS, dtype=np.float32) + if fault_type != 'Healthy': + label[LABEL_COLUMNS.index(FAULT_TYPE_TO_LABEL[fault_type])] = 1.0 + + records.append({ + 'waveform_id': f'{bearing_id}_{fault_type}_{severity}', + 'bearing_id': int(bearing_id), + 'fault_type': fault_type, + 'severity': int(severity), + # The tachometer channel is sparsely populated; the first row's + # value is what the protocol uses. Analysis only - never an input. + 'rpm': int(frame['Speed'].iloc[0]), + 'label': label, + }) + + if len(records) != N_RECORDINGS: + raise ValueError(f'expected {N_RECORDINGS} recordings, ' + f'found {len(records)}') + + os.makedirs(data_path, exist_ok=True) + path = _cache_path(data_path) + np.savez( + path, + signals=signals, + labels=np.stack([r['label'] for r in records]), + row_idx=np.arange(N_RECORDINGS, dtype=np.int32), + bearing_id=np.array([r['bearing_id'] for r in records], dtype=np.int32), + severity=np.array([r['severity'] for r in records], dtype=np.int32), + rpm=np.array([r['rpm'] for r in records], dtype=np.int32), + waveform_id=np.array([r['waveform_id'] for r in records]), + fault_type=np.array([r['fault_type'] for r in records]), + label_columns=np.array(LABEL_COLUMNS), + fs=np.array(SAMPLE_RATE, dtype=np.int32), + ) + return path + + +# --------------------------------------------------------------------------- +# Self-checks +# --------------------------------------------------------------------------- +def check_splits(data_path=DEFAULT_DATA_PATH, folds=None, fixtures=None): + """Assert the split protocol holds for every fold. + + This is the port-fidelity test: if the fold enumeration were renumbered, or + the split drawn at the wrong granularity, these assertions catch it. + + Args: + data_path (str): directory holding the .npz cache. + folds (iterable, optional): folds to check. Defaults to every tuning and + evaluation fold. + fixtures (str, optional): directory of published run_*.parquet split + fixtures to cross-check the held-out bearing sets against. + + Returns: + int: the number of folds checked. + """ + cache = _load_cache(data_path) + bearing_id = cache['bearing_id'] + fault_type = cache['fault_type'] + all_bearings = set(bearing_id.tolist()) + folds = list(folds) if folds is not None else (list(TUNING_FOLDS) + + list(EVAL_FOLDS)) + + n_train = (len(all_bearings) - NUM_LABELS * N_BEARINGS_PER_FAULT_MODE) * 3 + n_test = NUM_LABELS * N_BEARINGS_PER_FAULT_MODE * 3 + + for fold in folds: + held_out = held_out_bearings_for_fold(fold) + train_rows, test_rows = fold_rows(bearing_id, fold) + + train_bearings = set(bearing_id[train_rows].tolist()) + test_bearings = set(bearing_id[test_rows].tolist()) + + assert not train_bearings & test_bearings, \ + f'fold {fold}: bearings on both sides: {train_bearings & test_bearings}' + assert train_bearings | test_bearings == all_bearings, \ + f'fold {fold}: bearings missing from the split' + assert test_bearings == set(held_out), \ + f'fold {fold}: test bearings {sorted(test_bearings)} != {held_out}' + assert len(train_rows) == n_train, \ + f'fold {fold}: {len(train_rows)} train recordings, expected {n_train}' + assert len(test_rows) == n_test, \ + f'fold {fold}: {len(test_rows)} test recordings, expected {n_test}' + + for mode, ids in FAULT_BEARING_IDS.items(): + n_held = len(test_bearings & set(ids)) + assert n_held == N_BEARINGS_PER_FAULT_MODE, \ + (f'fold {fold}: {n_held} {mode} bearings held out, ' + f'expected {N_BEARINGS_PER_FAULT_MODE}') + + # Every fault mode must appear on both sides, or a label column is + # constant in the test set and its AUROC is undefined. + for rows, side in ((train_rows, 'train'), (test_rows, 'test')): + present = {str(f) for f in fault_type[rows]} + assert present == {'Healthy', 'Inner', 'Outer', 'Ball', 'Cage'}, \ + f'fold {fold}: {side} side is missing fault types: {present}' + + print(f'Split protocol holds for {len(folds)} folds ' + f'({n_train} train / {n_test} test recordings each).') + + if fixtures is not None: + _check_against_fixtures(fixtures, folds) + + return len(folds) + + +def _check_against_fixtures(fixtures, folds): + """Cross-check held-out bearing sets against published split fixtures. + + Each run_.parquet holds a single row with `train_ids` and `test_ids` + columns listing bearing ids. Matching them fold-for-fold is the strongest + available evidence that the fold enumeration was ported without renumbering. + """ + import glob + + import pandas as pd + + checked = 0 + for path in sorted(glob.glob(os.path.join(fixtures, 'run_*.parquet'))): + fold = int(os.path.splitext(os.path.basename(path))[0].split('_')[1]) + if fold not in folds: + continue + frame = pd.read_parquet(path) + expected = sorted(int(b) for b in frame['test_ids'].iloc[0]) + actual = held_out_bearings_for_fold(fold) + assert actual == expected, \ + f'fold {fold}: held out {actual}, fixture says {expected}' + checked += 1 + + if checked: + print(f'Held-out bearings match the published fixtures for ' + f'{checked} folds.') + else: + print(f'No run_*.parquet fixtures found in {fixtures}.') + + +def report_encoding(data_path=DEFAULT_DATA_PATH): + """Print the per-recording encoding constants and their cost. + + This is how ENCODE_HEADROOM gets re-justified after any change: it reports + how many uint8 levels one signal standard deviation spans, and what fraction + of samples clip at plausible augmentation gains. + """ + cache = _load_cache(data_path) + signals, center, scale = cache['signals'], cache['center'], cache['scale'] + + std = signals.std(axis=1) + levels = 127.0 * std / scale + + print(f'Per-recording encoding, headroom {ENCODE_HEADROOM}, ' + f'percentile {ENCODE_PERCENTILE}, zero point {ZERO_POINT}') + print(f' centre min {center.min():9.3f} max {center.max():9.3f}') + print(f' signal std min {std.min():9.3f} max {std.max():9.3f} ' + f'ratio {std.max() / std.min():.1f}x') + print(f' uint8 levels per signal sigma: min {levels.min():.1f} ' + f'median {np.median(levels):.1f} max {levels.max():.1f}') + + print('\n clipped samples by augmentation gain:') + for gain in (1.0, 1.0 + GAIN_STD, 1.0 + 2 * GAIN_STD): + normalised = gain * (signals - center[:, None]) / scale[:, None] + clipped = np.abs(ZERO_POINT + 127.0 * normalised - ZERO_POINT) > 127.0 + per_recording = clipped.mean(axis=1) + print(f' gain {gain:4.1f} mean {per_recording.mean() * 100:7.4f}% ' + f'worst recording {per_recording.max() * 100:8.4f}%') + + +if __name__ == '__main__': + import argparse + + parser = argparse.ArgumentParser( + description='Build, check and report on the UORED-VAFCLS data pipeline') + parser.add_argument('-d', '--data', default=DEFAULT_DATA_PATH, + help='Directory holding the prepared .npz cache') + parser.add_argument('--fold', type=int, default=FIXED_FOLD, + help='Bearing-disjoint fold to summarise') + parser.add_argument('--seed', type=int, default=0, + help='Seed for the crop positions, gains and shuffle') + parser.add_argument('--prepare-raw', metavar='RAW_DIR', default=None, + help='Rebuild the .npz cache from the raw Mendeley CSV ' + 'tree instead of downloading it') + parser.add_argument('--check-splits', action='store_true', + help='Assert bearing-disjointness and the recording ' + 'counts for all 105 tuning and evaluation folds') + parser.add_argument('--fixtures', default=None, + help='Directory of published run_*.parquet split ' + 'fixtures to cross-check --check-splits against') + parser.add_argument('--report-encoding', action='store_true', + help='Report the per-recording uint8 encoding constants ' + 'and how much they clip') + args = parser.parse_args() + + if args.prepare_raw is not None: + path = prepare_raw(args.prepare_raw, args.data) + print(f'Wrote {path} ({os.path.getsize(path) / 1e6:.0f} MB)') + _load_cache.cache_clear() + + if args.check_splits: + check_splits(args.data, fixtures=args.fixtures) + + if args.report_encoding: + report_encoding(args.data) + + if not (args.check_splits or args.report_encoding): + train_ds, test_ds = get_data(args.data, fold=args.fold, seed=args.seed) + samples = get_samples(args.data, num_samples=100, fold=args.fold, + seed=args.seed) + print(f'\nBatches: train {len(train_ds)}, held out {len(test_ds)}') + print(f'Samples for benchmarking: {samples.shape} {samples.dtype} ' + f'range [{samples.min()}, {samples.max()}]') diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_eval.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_eval.py new file mode 100644 index 0000000..41fca1d --- /dev/null +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_eval.py @@ -0,0 +1,218 @@ +#!/usr/bin/env python +# Copyright 2025 Brainchip Holdings Ltd. Apache 2.0 License +""" +UORED-VAFCLS evaluation for tf_keras or akida models. + +The reported metric is macro AUROC over the four fault labels, not accuracy. +Healthy bearings are encoded as the all-zero label vector rather than a fifth +class, so there is no argmax prediction to be right or wrong about: any accuracy +or F1 figure would depend on a decision threshold nobody has chosen. AUROC is +threshold-free and is what the protocol this example follows reports. + +Scores from a single fold are of limited use on their own - the spread across +folds is far wider than the difference between models worth comparing. Use +uored_vafcls_cross_validate.py for a number that means something. + +Example +------- + python uored_vafcls_eval.py -d ./data/uored_vafcls \\ + -l models/akdcnn_uored_vafcls.h5 +""" +import argparse +import json +import pathlib + +import akida +import numpy as np +import tensorflow as tf +from cnn2snn import load_quantized_model +from sklearn.metrics import roc_auc_score +from tqdm import tqdm + +from akida_models.sparsity import compute_sparsity +from brainchip_utils.hardware_utils import get_akida_device +from brainchip_utils.plot_utils import pretty_print_sparsity +from uored_vafcls_data import (FIXED_FOLD, LABEL_COLUMNS, BATCH_SIZE, + get_samples, get_test_data) + +tf.config.experimental.enable_op_determinism() + +# Windows used to measure activation sparsity. Held under the 360 deterministic +# training windows a fold provides, so the measurement is repeatable. +NUM_SPARSITY_SAMPLES = 240 + + +# --------------------------------------------------------------------------- +# Metrics +# --------------------------------------------------------------------------- +def auroc_scores(logits, labels): + """Macro and per-label AUROC. + + Uses the same scikit-learn function the reference protocol uses, so the + metric can never be the source of a discrepancy between this port and the + published numbers. + + Args: + logits (np.ndarray): raw model outputs, shape (N, len(LABEL_COLUMNS)). + labels (np.ndarray): binary indicator labels, same shape. + + Returns: + dict: 'macro' plus one entry per label in LABEL_COLUMNS. + """ + per_label = roc_auc_score(labels, logits, average=None) + scores = {'macro': float(roc_auc_score(labels, logits, average='macro'))} + scores.update({name: float(v) for name, v in zip(LABEL_COLUMNS, per_label)}) + return scores + + +def report(scores, split_label): + """Print the macro and per-label AUROC.""" + print(f'\n{split_label} macro AUROC: {scores["macro"]:.4f}') + print('Per-label AUROC:') + for name in LABEL_COLUMNS: + print(f' {name:<6} {scores[name]:.4f}') + + +# --------------------------------------------------------------------------- +# Inference +# --------------------------------------------------------------------------- +def predict_keras_model(model, dataset): + """Run inference with a tf_keras model and return (logits, labels).""" + logits_all, labels_all = [], [] + for batch, label_batch in dataset: + logits_all.append(model.predict(batch, verbose=0)) + labels_all.append(label_batch.numpy()) + return np.concatenate(logits_all), np.concatenate(labels_all) + + +def evaluate_akida_model(akida_model, val_dataset): + """Run inference with an Akida model and return (logits, labels). + + Returns logits rather than predictions: AUROC is rank-based and multi-label, + so thresholding here would throw away exactly the information it needs. + """ + device = get_akida_device(target_version=akida_model.ip_version) + if device is not None: + akida_model.map(device, mode=akida.MapMode.Minimal) + print('Running inference on Akida hardware device') + akida_model.summary() + + labels_all = None + logits_all = None + + # Akida can't directly digest the tensorflow dataset, we need to + # manually iterate over the dataset to deliver inputs as numpy arrays + for batch, label_batch in tqdm(val_dataset, desc="Evaluating on Akida"): + if not isinstance(batch, np.ndarray): + batch = batch.numpy() + + # Inference on Akida + logits_batch = akida_model.predict(batch) + logits_batch = logits_batch.squeeze(axis=(1, 2)) # (B, 1, 1, C) -> (B, C) + + if labels_all is None: + labels_all = label_batch + logits_all = logits_batch + else: + labels_all = np.concatenate([labels_all, label_batch]) + logits_all = np.concatenate([logits_all, logits_batch]) + + return logits_all, np.asarray(labels_all) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('-l', '--loadmodel', required=True, + help='Model to load (.h5 tf_keras or .fbz akida model)') + parser.add_argument('-d', '--data', default='./data/uored_vafcls', + help='Directory holding the prepared .npz cache') + parser.add_argument('-b', '--batch_size', type=int, default=BATCH_SIZE) + parser.add_argument('--fold', type=int, default=FIXED_FOLD, + help='Bearing-disjoint fold to evaluate on') + parser.add_argument('--seed', type=int, default=0, + help='Random seed for the sparsity sample draw') + parser.add_argument('--save-metrics', action='store_true', + help='Write AUROC (and param count for .h5) to ' + 'metrics.json. Only valid on the fixed fold') + args = parser.parse_args() + + # --------------------------------------------------------------------------- + # Model + # --------------------------------------------------------------------------- + if args.loadmodel.endswith('.h5'): + model = load_quantized_model(args.loadmodel) + isakida = False + imsize = model.input_shape[1:] + elif args.loadmodel.endswith('.fbz'): + model = akida.Model(args.loadmodel) + isakida = True + imsize = tuple(model.input_shape) + else: + raise ValueError(f'Unsupported model file: {args.loadmodel}') + + # --------------------------------------------------------------------------- + # Data loading + # --------------------------------------------------------------------------- + test_ds = get_test_data(args.data, imsize, batch_size=args.batch_size, + fold=args.fold) + + # --------------------------------------------------------------------------- + # Evaluation + # --------------------------------------------------------------------------- + if isakida: + logits, labels = evaluate_akida_model(model, test_ds) + else: + logits, labels = predict_keras_model(model, test_ds) + + scores = auroc_scores(logits, labels) + report(scores, f'Fold {args.fold} held-out') + + # --------------------------------------------------------------------------- + # Activation sparsity + # --------------------------------------------------------------------------- + sparsity = None + if isakida: + samples = get_samples(args.data, imsize, + num_samples=NUM_SPARSITY_SAMPLES, + fold=args.fold, seed=args.seed) + sparsity_dict = compute_sparsity(model, samples=samples) + pretty_print_sparsity(sparsity_dict) + sparsity = float(np.mean(list(sparsity_dict.values()))) + print(f'Mean activation sparsity over {NUM_SPARSITY_SAMPLES} windows: ' + f'{sparsity * 100:.2f}%') + + # --------------------------------------------------------------------------- + # Persist metrics + # --------------------------------------------------------------------------- + if args.save_metrics: + # The is used to update the stored metrics that are used to generate the + # performance tables in the README of this folder. + # This should only be used for code maintenance, when the model or training + # pipeline is updated and a new trained model integrated. + # + # Guarded on the fold: the README's reference table is specifically the + # fixed fold, and a stray --fold would otherwise silently replace it + # with a number from a different held-out set. + if args.fold != FIXED_FOLD: + raise SystemExit( + f'--save-metrics writes the README reference table, which is ' + f'fold {FIXED_FOLD}; refusing to write results from fold ' + f'{args.fold}.') + + metrics_path = pathlib.Path(__file__).parent / 'docs' / 'metrics.json' + metrics = json.loads(metrics_path.read_text()) if metrics_path.exists() else {} + auroc_str = f'{scores["macro"]:.4f}' + metrics['fixed_fold'] = str(args.fold) + if isakida: + metrics['akida_auroc'] = auroc_str + metrics['sparsity'] = f'{sparsity * 100:.2f}%' + # The per-label breakdown is only reported for the deployed model. + for name in LABEL_COLUMNS: + metrics[f'akida_auroc_{name}'] = f'{scores[name]:.4f}' + elif 'qat' in pathlib.Path(args.loadmodel).stem: + metrics['qat_auroc'] = auroc_str + else: + metrics['float_auroc'] = auroc_str + metrics['params'] = f'{model.count_params():,}' + metrics_path.write_text(json.dumps(metrics, indent=4) + '\n') + print(f'Metrics saved to {metrics_path}') diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_model.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_model.py new file mode 100644 index 0000000..2e9bc69 --- /dev/null +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_model.py @@ -0,0 +1,151 @@ +#!/usr/bin/env python +# Copyright 2025 Brainchip Holdings Ltd. Apache 2.0 License +""" +UORED-VAFCLS bearing fault model (akdcnn). + +A 2D convolutional network over a framed 1 s vibration window, descended from +WDCNN but rebuilt to map wholly onto Akida 1 hardware. + +Input representation +-------------------- +The model takes the 42000-sample window already framed to (1200, 35, 1) by the +data pipeline, not the raw waveform. The framing is outside the graph because an +Akida model cannot contain a reshape - the deployed network receives the framed +tensor directly. Row j is samples [35j, 35j+35), so the row axis is coarse time +(1200 frames, 0.833 ms apart) and the column axis is fine phase within a frame, +much like the framing of a speech signal. The 7x7 stem therefore correlates 7 +consecutive samples across 7 consecutive frames: a lag/phase view. + +(The source repo's docstring claimed this stem was equivalent to a (49, 1) +kernel at stride 7. That was true only of an earlier variant which transposed +the two axes before framing; that transpose is not applied here, and the +equivalence does not hold for this layout.) + +Input scaling is part of the model: a Rescaling layer inverts the data +pipeline's uint8 encoding, mapping [0, 255] back to the roughly [-1, 1] range +the network trains on. Keeping it in the graph means float training, quantized +training and Akida inference all see identical values, and cnn2snn folds the +layer into the first convolution at conversion time. + +Hardware constraints that fixed the geometry +-------------------------------------------- +Two AKD1500 constraints drove the design, and both are load-bearing: + +* The long dimension has to come first, hence (1200, 35) rather than (35, 1200). +* Every layer's second spatial dimension must stay above 1, which is why the + last two blocks omit their max-pool: the height runs 35 -> 29 -> 14 -> 7 -> 4 + -> 2 -> 1, and a sixth pool would take it to 0. + +Initializers +------------ +The source repo used custom initializers that reproduced PyTorch's defaults, to +keep a Keras port numerically comparable with a PyTorch reference. That +reference is not part of this example, and the custom bias initializer is what +forced a custom_object_scope workaround around every quantize and convert call +(Keras resolves initializers by name when it clones a model, including inside +the `cnn2snn convert` CLI where no scope can be installed). Keras defaults are +used here instead, which removes that whole class of problem. + +Example +------- + python uored_vafcls_model.py -s models/akdcnn_uored_vafcls_untrained.h5 +""" + +import argparse + +from cnn2snn import AkidaVersion, set_akida_version +from tf_keras import Model +from tf_keras.layers import (BatchNormalization, Conv2D, Dense, Flatten, Input, + MaxPooling2D, ReLU, Rescaling) +from tf_keras.utils import set_random_seed + +from uored_vafcls_data import INPUT_SHAPE, NUM_LABELS, ZERO_POINT + +# BatchNorm settings carried over from the source model. +BN_MOMENTUM = 0.9 +BN_EPSILON = 1e-5 + +# Filters per block, and whether the block pools. The last two do not: the +# height is already 1 by then (see the module docstring). +BLOCK_FILTERS = (32, 64, 64, 64, 64, 64) +BLOCK_POOLING = (True, True, True, True, False, False) + +STEM_FILTERS = 16 +STEM_KERNEL = (7, 7) +DENSE_UNITS = 100 +RELU_MAX = 6.0 + + +def _conv_block(x, filters, name, kernel=(3, 3), padding='same', pool=True, + pool_padding='same'): + """Conv -> BatchNorm -> ReLU6 [-> MaxPool], with no convolution bias. + + The bias is omitted because the BatchNorm that follows immediately + reintroduces a per-channel offset, so a conv bias would be redundant, and + cnn2snn folds the BatchNorm into the convolution at conversion. + """ + x = Conv2D(filters, kernel, padding=padding, use_bias=False, + name=f'{name}_conv')(x) + x = BatchNormalization(momentum=BN_MOMENTUM, epsilon=BN_EPSILON, + name=f'{name}_bn')(x) + x = ReLU(max_value=RELU_MAX, name=f'{name}_relu')(x) + if pool: + x = MaxPooling2D((2, 2), strides=(2, 2), padding=pool_padding, + name=f'{name}_pool')(x) + return x + + +def build_uored_vafcls_model(seed=0): + """Build the untrained akdcnn model. + + Args: + seed (int): random seed for weight initialization. + + Returns: + tf_keras.Model: the model, expecting uint8 inputs of shape INPUT_SHAPE + and returning NUM_LABELS raw logits (one per fault mode; a healthy + bearing is all four logits low, not a fifth class). + """ + set_random_seed(seed) + + with set_akida_version(AkidaVersion.v1): + inputs = Input(shape=INPUT_SHAPE, name='input') + + # Inverts the data pipeline's uint8 encoding. Folded into stem_conv at + # conversion, giving input_scaling = (127, ZERO_POINT). + x = Rescaling(1.0 / 127.0, -ZERO_POINT / 127.0, name='rescaling')(inputs) + + # Stem: a dense convolution with no padding, so the first layer sees the + # raw framed signal. + x = _conv_block(x, STEM_FILTERS, 'stem', kernel=STEM_KERNEL, + padding='valid', pool=True, pool_padding='valid') + + for i, (filters, pool) in enumerate(zip(BLOCK_FILTERS, BLOCK_POOLING), + start=1): + x = _conv_block(x, filters, f'block{i}', pool=pool) + + x = Flatten(name='flatten')(x) + x = Dense(DENSE_UNITS, name='fc')(x) + x = BatchNormalization(momentum=BN_MOMENTUM, epsilon=BN_EPSILON, + name='fc_bn')(x) + x = ReLU(name='fc_relu')(x) + outputs = Dense(NUM_LABELS, name='predictions')(x) + + model = Model(inputs, outputs, name='akdcnn_uored_vafcls') + + return model + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('-s', '--savepath', + default='./models/akdcnn_uored_vafcls_untrained.h5', + help='Model save path') + parser.add_argument('--seed', type=int, default=0, + help='Random seed for reproducibility') + args = parser.parse_args() + + model = build_uored_vafcls_model(seed=args.seed) + model.summary() + model.save(args.savepath, include_optimizer=False) + print(f'Model saved to {args.savepath}') diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_train.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_train.py new file mode 100644 index 0000000..2e64c78 --- /dev/null +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_train.py @@ -0,0 +1,172 @@ +#!/usr/bin/env python +# Copyright 2025 Brainchip Holdings Ltd. Apache 2.0 License +""" +UORED-VAFCLS bearing fault training. + +Used for both stages of the pipeline: full-precision training from the untrained +model, and quantization-aware tuning of the quantized one. Only the loaded +model, the epoch count and the learning rate differ between the two. + +The loss is binary cross-entropy over four independent fault labels, not a +softmax over classes: a bearing can in principle show more than one fault, and +a healthy bearing is the all-zero label vector rather than a fifth class. The +model emits raw logits, so `from_logits=True` throughout. + +There is no validation set, and consequently no early stopping, no +learning-rate-on-plateau and no best-weight restore. This is deliberate. The +only data not used for training is the held-out fold, and selecting weights on +that would be exactly the leakage the bearing-disjoint split exists to prevent. +The recipe is instead fixed - a set epoch count, evaluated at the final epoch - +and folds 0-4 are reserved as the budget for choosing it. Please do not "fix" +this by adding a callback that monitors the held-out fold. + +Example +------- + python uored_vafcls_train.py -l models/akdcnn_uored_vafcls_untrained.h5 \\ + -s models/akdcnn_uored_vafcls.h5 -e 30 -lr 2e-4 --fold 5 +""" + +import argparse + +import tensorflow as tf +from cnn2snn import load_quantized_model +from tf_keras import regularizers +from tf_keras.layers import ReLU +from tf_keras.losses import BinaryCrossentropy +from tf_keras.metrics import AUC +from tf_keras.optimizers.legacy import Adam +from tf_keras.optimizers.schedules import CosineDecay +from tf_keras.utils import set_random_seed + +from uored_vafcls_data import FIXED_FOLD, NUM_LABELS, BATCH_SIZE, get_data + +# Must be called before any TF ops to make GPU ops deterministic. It matters +# more here than in most examples: without it, re-running one unchanged fold +# moved its AUROC by up to 0.024, which is as large as the architecture +# differences this example exists to compare. Costs some throughput. +tf.config.experimental.enable_op_determinism() + + +def get_lr_schedule(initial_lr, total_steps, warmup_fraction=0.05): + """Cosine decay with a short linear warmup, over the real step count. + + The schedule has to span the steps actually taken. The source implementation + hard-coded the step count and undershot it by a sixth, which left the last + few epochs running at the schedule's floor of zero - training for fewer + epochs than it appeared to. Deriving total_steps from the dataset avoids + that, and avoids it silently recurring if the batch size or the number of + windows per recording ever changes. + + Args: + initial_lr (float): the peak learning rate, reached after warmup. + total_steps (int): optimizer steps over the whole run. + warmup_fraction (float): fraction of total_steps spent warming up. + + Returns: + tf_keras.optimizers.schedules.LearningRateSchedule: the schedule. + """ + warmup_steps = max(1, int(warmup_fraction * total_steps)) + return CosineDecay(initial_learning_rate=initial_lr * 0.01, + warmup_target=initial_lr, + warmup_steps=warmup_steps, + decay_steps=max(1, total_steps - warmup_steps)) + + +def train_uored_vafcls(model, train_ds, val_ds, epochs, learning_rate, + regularization=None, seed=0): + """Train or quantization-aware tune the model on one fold. + + Args: + model (tf_keras.Model): the model to train, float or quantized. + train_ds (tf.data.Dataset): training dataset. + val_ds (tf.data.Dataset): optional dataset to report per-epoch metrics + on. Pass None, which is the default in the pipeline: the only + candidate is the held-out fold, and nothing may be decided from it. + epochs (int): number of epochs. + learning_rate (float): peak learning rate for the cosine schedule. + regularization (float, optional): activity regularization on the ReLU + layers, to increase activation sparsity. + seed (int): random seed. + + Returns: + tf_keras.callbacks.History: the fit history. + """ + set_random_seed(seed) + + # --------------------------------------------------------------------------- + # Model + # --------------------------------------------------------------------------- + if regularization is not None: + print('Adding Activity Regularization to ReLU layers') + regularizer = regularizers.L1L2(regularization, regularization) + for layer in model.layers: + if isinstance(layer, ReLU): + layer.activity_regularizer = regularizer + + steps_per_epoch = len(train_ds) + lr_schedule = get_lr_schedule(learning_rate, steps_per_epoch * epochs) + + model.compile(optimizer=Adam(learning_rate=lr_schedule), + loss=BinaryCrossentropy(from_logits=True), + metrics=[AUC(name='auroc', multi_label=True, + num_labels=NUM_LABELS)]) + + # --------------------------------------------------------------------------- + # Training + # --------------------------------------------------------------------------- + print(f'Training for {epochs} epochs, {steps_per_epoch} steps per epoch ' + f'({steps_per_epoch * epochs} steps), peak LR {learning_rate}') + + # No callbacks: the recipe is a fixed number of epochs and the final-epoch + # weights are what gets evaluated. See the module docstring. + return model.fit(train_ds, epochs=epochs, validation_data=val_ds) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('-l', '--loadmodel', required=True, + help='Model to load (.h5 tf_keras or quantized model)') + parser.add_argument('-s', '--savemodel', required=True, + help='Model save path') + parser.add_argument('-d', '--data', default='./data/uored_vafcls', + help='Directory holding the prepared .npz cache') + parser.add_argument('-b', '--batch_size', type=int, default=BATCH_SIZE, + help='Batch size. The default divides the 720 training ' + 'windows exactly, giving 6 steps per epoch') + parser.add_argument('-e', '--epochs', type=int, default=30) + parser.add_argument('-lr', '--learning_rate', type=float, default=2e-4, + help='Peak learning rate for the cosine schedule') + parser.add_argument('-reg', '--regularization', type=float, default=None, + help='Activity Regularization to increase sparsity') + parser.add_argument('--fold', type=int, default=FIXED_FOLD, + help='Bearing-disjoint fold index. Folds 0-4 are the ' + 'tuning folds, the only folds any hyperparameter ' + 'may be chosen on. Folds 5-104 are the 100 ' + 'evaluation folds; 5 is the fixed fold the ' + 'pretrained models and hardware benchmarks use') + parser.add_argument('--monitor-heldout', action='store_true', + help='Report per-epoch metrics on the held-out fold. ' + 'For eyeballing the training curve only - no ' + 'decision may be taken from it') + parser.add_argument('--seed', type=int, default=0, + help='Random seed for reproducibility') + args = parser.parse_args() + + model = load_quantized_model(args.loadmodel) + train_ds, test_ds = get_data(args.data, model.input_shape[1:], + args.batch_size, fold=args.fold, + seed=args.seed) + + val_ds = None + if args.monitor_heldout: + print('WARNING: --monitor-heldout reports metrics on the held-out ' + 'bearings. It is for watching the curve only; choosing anything ' + 'on the basis of it - epochs, learning rate, weights - leaks the ' + 'test fold. Use folds 0-4 for that.') + val_ds = test_ds + + train_uored_vafcls(model=model, train_ds=train_ds, val_ds=val_ds, + epochs=args.epochs, learning_rate=args.learning_rate, + regularization=args.regularization, seed=args.seed) + model.save(args.savemodel, include_optimizer=False) + print(f'Model saved as {args.savemodel}.') diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_train.sh b/akida1/model_zoo/uored_vafcls/uored_vafcls_train.sh new file mode 100644 index 0000000..9892e90 --- /dev/null +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_train.sh @@ -0,0 +1,57 @@ +#!/usr/bin/env bash +# Copyright 2025 Brainchip Holdings Ltd. Apache 2.0 License +# +# Full UORED-VAFCLS pipeline on ONE bearing-disjoint fold: build, train, +# quantize, tune, convert, evaluate at each stage, and benchmark on hardware. +# +# Usage: bash uored_vafcls_train.sh [DATADIR] [FOLD] [SEED] +# +# FOLD defaults to 5, the first of the 100 evaluation folds and the fold the +# pretrained models and the published hardware numbers come from. +# +# A single fold tells you very little about this model. The across-fold spread +# is 0.04-0.05 macro AUROC and folds range from about 0.70 to about 0.99 with +# everything else held fixed. For the number that means something, run: +# +# python uored_vafcls_cross_validate.py +# +# Folds 0-4 are the tuning folds. Any change to the recipe must be evaluated +# there, never on folds 5-104. + +DATADIR="${1:-}" +DATA_ARG=${DATADIR:+-d "$DATADIR"} +FOLD="${2:-${FOLD:-5}}" +SEED="${3:-${SEED:-0}}" +COMMON="--fold $FOLD --seed $SEED $DATA_ARG" + +echo "UORED-VAFCLS pipeline: fold ${FOLD}, seed ${SEED}" + +# 1 - Build the untrained model +python uored_vafcls_model.py -s models/akdcnn_uored_vafcls_untrained.h5 --seed "$SEED" + +# 2 - Full-precision training +python uored_vafcls_train.py -l models/akdcnn_uored_vafcls_untrained.h5 \ + -s models/akdcnn_uored_vafcls.h5 -e 30 -lr 2e-4 -b 120 $COMMON + +# 3 - Evaluate the float model +python uored_vafcls_eval.py -l models/akdcnn_uored_vafcls.h5 $COMMON + +# 4 - Post-training quantization: 4-bit weights and activations, 8-bit input +cnn2snn quantize -m models/akdcnn_uored_vafcls.h5 -i 8 -w 4 -a 4 + +# 5 - Quantization-aware tuning. Larger than this repository's usual 2 epochs: +# at 6 steps per epoch, 2 epochs is 12 optimizer steps and recovers nothing. +python uored_vafcls_train.py -l models/akdcnn_uored_vafcls_iq8_wq4_aq4.h5 \ + -s models/akdcnn_uored_vafcls_qat.h5 -e 30 -lr 2e-4 -b 120 $COMMON + +# 6 - Evaluate the tuned quantized model +python uored_vafcls_eval.py -l models/akdcnn_uored_vafcls_qat.h5 $COMMON + +# 7 - Convert to Akida +cnn2snn convert -m models/akdcnn_uored_vafcls_qat.h5 + +# 8 - Evaluate on Akida +python uored_vafcls_eval.py -l models/akdcnn_uored_vafcls_qat.fbz $COMMON + +# 9 - Hardware benchmark (needs a physical AKD1500) +python uored_vafcls_benchmark.py -l models/akdcnn_uored_vafcls_qat.fbz $COMMON diff --git a/akida1/model_zoo/uored_vafcls/update_readme.py b/akida1/model_zoo/uored_vafcls/update_readme.py new file mode 100644 index 0000000..82e42d9 --- /dev/null +++ b/akida1/model_zoo/uored_vafcls/update_readme.py @@ -0,0 +1,11 @@ +#!/usr/bin/env python +# Copyright 2025 Brainchip Holdings Ltd. Apache 2.0 License +"""Regenerate README.md from README.md.template + metrics.json.""" +import json +import pathlib + +here = pathlib.Path(__file__).parent +metrics = json.loads((here / "docs" / "metrics.json").read_text()) +template = (here / "docs" / "README.md.template").read_text() +(here / "README.md").write_text(template.format_map(metrics)) +print("README.md updated.") From fddbab8a0edbfe6e2a67727fbc97597f83c1b16e Mon Sep 17 00:00:00 2001 From: Douglas McLelland Date: Mon, 14 Sep 2026 17:14:56 +0200 Subject: [PATCH 02/15] Good quantized performance --- .../uored_vafcls_cross_validate.py | 58 +++++++++++-------- .../uored_vafcls/uored_vafcls_model.py | 52 +++++++++-------- 2 files changed, 62 insertions(+), 48 deletions(-) diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_cross_validate.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_cross_validate.py index 260d8b1..ab73468 100644 --- a/akida1/model_zoo/uored_vafcls/uored_vafcls_cross_validate.py +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_cross_validate.py @@ -103,6 +103,7 @@ def run_fold(fold, seed=0, data_path='./data/uored_vafcls', epochs=30, with set_akida_version(AkidaVersion.v1): model = build_uored_vafcls_model(seed=seed) + # model.summary() train_ds, test_ds = get_data(data_path, model.input_shape[1:], batch_size, fold=fold, seed=seed) @@ -114,7 +115,10 @@ def run_fold(fold, seed=0, data_path='./data/uored_vafcls', epochs=30, # --------------------------------------------------------------------------- # Full precision # --------------------------------------------------------------------------- - train_uored_vafcls(model, train_ds, test_ds, epochs, learning_rate, seed=seed) + if verbose>0: + train_uored_vafcls(model, train_ds, test_ds, epochs, learning_rate, seed=seed) + else: + train_uored_vafcls(model, train_ds, None, epochs, learning_rate, seed=seed) float_scores = auroc_scores(*predict_keras_model(model, test_ds)) row['float_auroc'] = f'{float_scores["macro"]:.6f}' for name in LABEL_COLUMNS: @@ -131,36 +135,40 @@ def run_fold(fold, seed=0, data_path='./data/uored_vafcls', epochs=30, # Quantization, tuning and conversion # ----------------------------------------------------------------------- with set_akida_version(AkidaVersion.v1): - # qmodel = quantize(model, input_weight_quantization=8, - # weight_quantization=4, activ_quantization=4) + qmodel = quantize(model, input_weight_quantization=8, + weight_quantization=4, activ_quantization=4) # qmodel = quantize_until(model, 'fc_relu') - qmodel = quantize_until(model, 'block6_relu') + # qmodel = quantize_until(model, 'block6_relu') - qmodel.summary() - train_uored_vafcls(qmodel, train_ds, test_ds, qat_epochs, - qat_learning_rate, seed=seed) + # qmodel.summary() + if verbose>0: + train_uored_vafcls(qmodel, train_ds, test_ds, qat_epochs, + qat_learning_rate, seed=seed) + else: + train_uored_vafcls(qmodel, train_ds, None, qat_epochs, + qat_learning_rate, seed=seed) qat_scores = auroc_scores(*predict_keras_model(qmodel, test_ds)) row['qat_auroc'] = f'{qat_scores["macro"]:.6f}' - # ak_model = convert(qmodel) - - # if models_dir is not None: - # qmodel.save(os.path.join(models_dir, - # f'akdcnn_uored_vafcls_fold{fold:03d}_' - # f'seed{seed}_qat.h5'), - # include_optimizer=False) - # ak_model.save(os.path.join(models_dir, - # f'akdcnn_uored_vafcls_fold{fold:03d}_' - # f'seed{seed}_qat.fbz')) - - # # ----------------------------------------------------------------------- - # # Akida - # # ----------------------------------------------------------------------- - # akida_scores = auroc_scores(*evaluate_akida_model(ak_model, test_ds)) - # row['akida_auroc'] = f'{akida_scores["macro"]:.6f}' - # for name in LABEL_COLUMNS: - # row[f'akida_auroc_{name}'] = f'{akida_scores[name]:.6f}' + ak_model = convert(qmodel) + + if models_dir is not None: + qmodel.save(os.path.join(models_dir, + f'akdcnn_uored_vafcls_fold{fold:03d}_' + f'seed{seed}_qat.h5'), + include_optimizer=False) + ak_model.save(os.path.join(models_dir, + f'akdcnn_uored_vafcls_fold{fold:03d}_' + f'seed{seed}_qat.fbz')) + + # ----------------------------------------------------------------------- + # Akida + # ----------------------------------------------------------------------- + akida_scores = auroc_scores(*evaluate_akida_model(ak_model, test_ds)) + row['akida_auroc'] = f'{akida_scores["macro"]:.6f}' + for name in LABEL_COLUMNS: + row[f'akida_auroc_{name}'] = f'{akida_scores[name]:.6f}' row['seconds'] = f'{time.time() - started:.1f}' return row diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_model.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_model.py index 2e9bc69..55b27d0 100644 --- a/akida1/model_zoo/uored_vafcls/uored_vafcls_model.py +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_model.py @@ -56,7 +56,7 @@ from cnn2snn import AkidaVersion, set_akida_version from tf_keras import Model from tf_keras.layers import (BatchNormalization, Conv2D, Dense, Flatten, Input, - MaxPooling2D, ReLU, Rescaling) + MaxPooling2D, ReLU, Rescaling, GlobalAveragePooling2D) from tf_keras.utils import set_random_seed from uored_vafcls_data import INPUT_SHAPE, NUM_LABELS, ZERO_POINT @@ -67,12 +67,14 @@ # Filters per block, and whether the block pools. The last two do not: the # height is already 1 by then (see the module docstring). -BLOCK_FILTERS = (32, 64, 64, 64, 64, 64) -BLOCK_POOLING = (True, True, True, True, False, False) +BLOCK_FILTERS = (32, 64, 64, 64, 96) +BLOCK_POOLING = (True, True, True, True, True) +# BLOCK_FILTERS = (32, 64, 64, 64, 96, 128) +# BLOCK_POOLING = (True, True, True, True, True, False) STEM_FILTERS = 16 STEM_KERNEL = (7, 7) -DENSE_UNITS = 100 +DENSE_UNITS = 512 RELU_MAX = 6.0 @@ -108,30 +110,34 @@ def build_uored_vafcls_model(seed=0): """ set_random_seed(seed) - with set_akida_version(AkidaVersion.v1): - inputs = Input(shape=INPUT_SHAPE, name='input') + inputs = Input(shape=INPUT_SHAPE, name='input') - # Inverts the data pipeline's uint8 encoding. Folded into stem_conv at - # conversion, giving input_scaling = (127, ZERO_POINT). - x = Rescaling(1.0 / 127.0, -ZERO_POINT / 127.0, name='rescaling')(inputs) + # Normalize incoming data from the uint8 range to [-1, 1] + # Folded into stem_conv at conversion + x = Rescaling(1.0 / 127.0, -1.0, name='rescaling')(inputs) - # Stem: a dense convolution with no padding, so the first layer sees the - # raw framed signal. - x = _conv_block(x, STEM_FILTERS, 'stem', kernel=STEM_KERNEL, - padding='valid', pool=True, pool_padding='valid') + # Stem: a dense convolution with no padding + x = _conv_block(x, STEM_FILTERS, 'stem', kernel=STEM_KERNEL, + padding='valid', pool=True, pool_padding='valid') - for i, (filters, pool) in enumerate(zip(BLOCK_FILTERS, BLOCK_POOLING), - start=1): - x = _conv_block(x, filters, f'block{i}', pool=pool) + for i, (filters, pool) in enumerate(zip(BLOCK_FILTERS, BLOCK_POOLING), + start=1): + x = _conv_block(x, filters, f'block{i}', pool=pool) - x = Flatten(name='flatten')(x) - x = Dense(DENSE_UNITS, name='fc')(x) - x = BatchNormalization(momentum=BN_MOMENTUM, epsilon=BN_EPSILON, - name='fc_bn')(x) - x = ReLU(name='fc_relu')(x) - outputs = Dense(NUM_LABELS, name='predictions')(x) + x = Conv2D(128, 3, padding='same', use_bias=False, + name='block6_conv')(x) + x = BatchNormalization(momentum=BN_MOMENTUM, epsilon=BN_EPSILON, + name='block6_bn')(x) + x = GlobalAveragePooling2D(name='gap')(x) + x = ReLU(max_value=RELU_MAX, name='block6_relu')(x) + + x = Dense(DENSE_UNITS, name='fc')(x) + x = BatchNormalization(momentum=BN_MOMENTUM, epsilon=BN_EPSILON, + name='fc_bn')(x) + x = ReLU(name='fc_relu')(x) + outputs = Dense(NUM_LABELS, name='predictions')(x) - model = Model(inputs, outputs, name='akdcnn_uored_vafcls') + model = Model(inputs, outputs, name='akdcnn_uored_vafcls') return model From 68befc9253090348225a9aec52f5416fc7403526 Mon Sep 17 00:00:00 2001 From: Douglas McLelland Date: Wed, 16 Sep 2026 16:55:44 +0200 Subject: [PATCH 03/15] First pass tidying and simplification --- akida1/model_zoo/uored_vafcls/local_utils.py | 30 -- .../uored_vafcls_cross_validate.py | 21 +- .../uored_vafcls/uored_vafcls_data.py | 312 ++++-------------- .../uored_vafcls/uored_vafcls_eval.py | 13 +- .../uored_vafcls/uored_vafcls_model.py | 135 ++++---- .../uored_vafcls/uored_vafcls_train.py | 103 ++---- .../uored_vafcls/uored_vafcls_train.sh | 2 +- 7 files changed, 165 insertions(+), 451 deletions(-) delete mode 100644 akida1/model_zoo/uored_vafcls/local_utils.py diff --git a/akida1/model_zoo/uored_vafcls/local_utils.py b/akida1/model_zoo/uored_vafcls/local_utils.py deleted file mode 100644 index 5c3196c..0000000 --- a/akida1/model_zoo/uored_vafcls/local_utils.py +++ /dev/null @@ -1,30 +0,0 @@ -from cnn2snn import quantize -from tf_keras import Model - - -def quantize_until(model, target_layer): - '''Quantize a model up to the named target layer only. - - Layers up to and including `target_layer` are quantized; everything - after is left as-is and re-attached to the quantized output. Useful for - bisecting which layer introduces a quantization accuracy drop. - - Note: assumes a linear (single input/output per layer) topology from - `target_layer` onwards, since remaining layers are re-called in sequence. - ''' - layer_names = [layer.name for layer in model.layers] - target_idx = layer_names.index(target_layer) - - # Get sub-model up to and including named layer - sub_model = Model(inputs=model.input, outputs=model.layers[target_idx].output) - # Quantize that sub-model - qsub_model = quantize(sub_model, weight_quantization=4, activ_quantization=4, input_weight_quantization=8) - - # Stitch remaining unquantized layers to the end of that - x = qsub_model.output - for layer in model.layers[target_idx + 1:]: - x = layer(x) - - # Return complete partially-quantized model - pq_model = Model(inputs=qsub_model.input, outputs=x, name='quantized_until_' + target_layer) - return pq_model \ No newline at end of file diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_cross_validate.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_cross_validate.py index ab73468..24f91d9 100644 --- a/akida1/model_zoo/uored_vafcls/uored_vafcls_cross_validate.py +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_cross_validate.py @@ -46,7 +46,6 @@ import numpy as np import tensorflow as tf from cnn2snn import AkidaVersion, convert, quantize, set_akida_version -from local_utils import quantize_until from tqdm import tqdm from uored_vafcls_data import (BATCH_SIZE, EVAL_FOLDS, FIXED_FOLD, @@ -101,8 +100,7 @@ def run_fold(fold, seed=0, data_path='./data/uored_vafcls', epochs=30, """ started = time.time() - with set_akida_version(AkidaVersion.v1): - model = build_uored_vafcls_model(seed=seed) + model = build_uored_vafcls_model(seed=seed) # model.summary() train_ds, test_ds = get_data(data_path, model.input_shape[1:], batch_size, fold=fold, seed=seed) @@ -115,10 +113,7 @@ def run_fold(fold, seed=0, data_path='./data/uored_vafcls', epochs=30, # --------------------------------------------------------------------------- # Full precision # --------------------------------------------------------------------------- - if verbose>0: - train_uored_vafcls(model, train_ds, test_ds, epochs, learning_rate, seed=seed) - else: - train_uored_vafcls(model, train_ds, None, epochs, learning_rate, seed=seed) + train_uored_vafcls(model, train_ds, epochs, learning_rate, seed=seed) float_scores = auroc_scores(*predict_keras_model(model, test_ds)) row['float_auroc'] = f'{float_scores["macro"]:.6f}' for name in LABEL_COLUMNS: @@ -138,16 +133,8 @@ def run_fold(fold, seed=0, data_path='./data/uored_vafcls', epochs=30, qmodel = quantize(model, input_weight_quantization=8, weight_quantization=4, activ_quantization=4) - # qmodel = quantize_until(model, 'fc_relu') - # qmodel = quantize_until(model, 'block6_relu') - - # qmodel.summary() - if verbose>0: - train_uored_vafcls(qmodel, train_ds, test_ds, qat_epochs, - qat_learning_rate, seed=seed) - else: - train_uored_vafcls(qmodel, train_ds, None, qat_epochs, - qat_learning_rate, seed=seed) + train_uored_vafcls(qmodel, train_ds, qat_epochs, + qat_learning_rate, seed=seed) qat_scores = auroc_scores(*predict_keras_model(qmodel, test_ds)) row['qat_auroc'] = f'{qat_scores["macro"]:.6f}' diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_data.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_data.py index b8bb0f4..83c25ee 100644 --- a/akida1/model_zoo/uored_vafcls/uored_vafcls_data.py +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_data.py @@ -8,21 +8,24 @@ raw vibration (42 kHz) -> 1 s window (42000 samples) -> random gain (training only) - -> per-recording DC removal + rescale to uint8 + -> rescale to uint8-range using a + single per-dataset scale factor -> reshape to (1200, 35, 1) -The reshape is a plain framing, not a spectrogram: row j is samples -[35j, 35j+35), so the row axis is coarse time (1200 frames, 0.833 ms apart) and -the column axis is fine phase within a frame. It happens here rather than in the -model because an Akida graph cannot contain a reshape - the deployed model -receives the already-framed tensor. +DATA SPLIT PROTOCOL -The split is bearing-disjoint and drawn *before* any windowing. Fault mode is -determined by bearing id in this dataset (inner=1-5, outer=6-10, ball=11-15, -cage=16-20), so a model given any split that is not bearing-disjoint can score -near-perfectly by re-identifying the bearing without learning anything about -faults. Each bearing contributes three recordings - its own healthy one plus two -severities of a single fault mode - and holding a bearing out removes all three. +The dataset split implemented here is derived from the method proposed by +Vieira et al (2026), "Towards a more realistic evaluation of machine learning +models for bearing fault diagnosis" (https://arxiv.org/abs/2509.22267) +The split is bearing-disjoint and drawn *before* any windowing to avoid the +data-leakage that compromises the majority of naive approaches to this dataset. + +Fault mode is determined by bearing id in this dataset (inner=1-5, outer=6-10, +ball=11-15, cage=16-20), so a model given any split that is not bearing-disjoint +can score near-perfectly by re-identifying the bearing without learning anything +about faults. Each bearing contributes three recordings - its own healthy one +plus two severities of a single fault mode - and holding a bearing out removes +all three. Folds are enumerated, not tabulated: every way of holding out 2 bearings from each of the 4 fault modes gives C(5,2)^4 = 10,000 folds, shuffled once with a @@ -34,6 +37,15 @@ encoded as the all-zero vector rather than a fifth class. The reported metric is macro AUROC (see uored_vafcls_eval.py) - there is no argmax prediction here, so an accuracy figure would depend on an arbitrary threshold. + +DATA PREPARATION + +The raw input vectors are reshaped to a 2D input, (1200, 35, 1). Note that's a +plain reshape, not a spectrogram: row j is samples [35j, 35j+35), so the row axis +is coarse time (1200 frames, 0.833 ms apart) and the column axis is fine phase +within a frame. It happens here rather than in the model because an Akida graph +cannot contain a reshape - the deployed model receives the already-framed tensor. + Usage: from uored_vafcls_data import get_data, get_test_data, get_samples @@ -43,10 +55,6 @@ test_ds = get_test_data('./data/uored_vafcls', (1200, 35, 1), fold=5) samples = get_samples('./data/uored_vafcls', (1200, 35, 1), num_samples=100) -Check the split protocol, or the input encoding, without training anything: - python uored_vafcls_data.py --check-splits - python uored_vafcls_data.py --report-encoding - Rebuild the cache from the raw Mendeley CSVs: python uored_vafcls_data.py --prepare-raw /path/to/1_CSV_Raw_Data_Files """ @@ -112,26 +120,18 @@ # --------------------------------------------------------------------------- # uint8 input encoding # --------------------------------------------------------------------------- -# Akida takes 8-bit inputs, and per-recording std in this dataset spans 69x -# (1.87 to 129.4). No single global scale fits that into 256 levels: one wide -# enough for the loudest recording leaves the quietest under one level. So the -# encoding is per *recording* - computed from the whole 10 s trace, never from -# the window being encoded, because a window-dependent scale (min-max, say) is -# scale-invariant and would silently turn the gain augmentation into a no-op. +# Akida takes 8-bit inputs (uint8 range). Inspection of the UORED-VAFCLS data +# shows that amplitude varies widely across recordings (with, predictably, +# vibration increasing markedly when bearings are faulty). Rather than set the +# range according to the global min-max of the data (which would completely +# flatten the low amplitude vibrations of some recordings when reduced to 8-bit) +# we choose a range that preserves the majority of data points, while clipping +# the worst outliers to preserve resolution for low-amplitude signals. We +# chose a range of [-128, 127], which looks suitable for the data, and has the +# advantage of requiring minimal transformation of the input signal: no rescaling, +# a simple offset of +128 plus rounding and clipping to [0, 255]. # -# Removing the per-recording mean is not only a concession to 8 bits. The raw -# signals carry a DC offset that is itself a fault-mode fingerprint: all ten -# inner-race recordings sit at mean 46.6-55.6 while every outer and ball -# recording sits at 0.9-3.1. On its own the per-recording mean scores 0.776 -# macro AUROC on inner-race and the std scores 0.798 - a shortcut the model can -# read without looking at the vibration at all. Centring and rescaling per -# recording deletes it. See the README's "Input encoding" section: this is a -# deliberate deviation from the source pipeline, and the reason the source's -# published figure is not the figure in the Model Card. -ZERO_POINT = 128 # mid-scale, so a negative gain is a real polarity flip -ENCODE_PERCENTILE = 99.9 # robust peak of |x - center|, per recording -ENCODE_HEADROOM = 2.0 # room for the gain augmentation before it clips - +ZERO_POINT = 128 # --------------------------------------------------------------------------- # Split protocol @@ -191,45 +191,15 @@ def fold_rows(bearing_id, fold, n_per_mode=N_BEARINGS_PER_FAULT_MODE, # --------------------------------------------------------------------------- # Input encoding # --------------------------------------------------------------------------- -def encoding_constants(signals): - """Compute the per-recording centre and full-scale value. - - Both are derived from the entire 10 s recording, so they are a fixed - property of the recording rather than of any window drawn from it. - - Args: - signals (np.ndarray): float32, shape (N_RECORDINGS, SIGNAL_LENGTH). - - Returns: - np.ndarray, np.ndarray: centre and scale, both float32 (N_RECORDINGS,). - """ - center = signals.mean(axis=1).astype(np.float32) - deviation = np.abs(signals - center[:, None]) - scale = ENCODE_HEADROOM * np.percentile(deviation, ENCODE_PERCENTILE, axis=1) - # return center, scale.astype(np.float32) - # HACK: Hard coding a single scaling factor - center = np.zeros_like(center) - scale = np.ones_like(scale)*127. - return center, scale - - - -def encode_uint8(window, center, scale): +def encode_uint8(window): """Encode a float window as uint8 centred on ZERO_POINT. - - The model's Rescaling layer inverts this exactly, so float training, QAT and - Akida inference all see the same values. - Args: window (np.ndarray): float32 samples, any shape. - center (float): the recording's centre, from encoding_constants(). - scale (float): the recording's full-scale value. Returns: np.ndarray: uint8, same shape as `window`. """ - normalised = (window - center) / scale - return np.clip(np.round(ZERO_POINT + 127.0 * normalised), 0, 255).astype(np.uint8) + return np.clip(np.round(ZERO_POINT + window), 0, 255).astype(np.uint8) def frame_window(x, width=FRAME_WIDTH): @@ -262,15 +232,14 @@ def _load_cache(data_path): """Load the prepared recordings and derive the encoding constants. lru_cached because uored_vafcls_cross_validate.py calls get_data() a hundred - times in one process; without this it would re-read ~100 MB and recompute 60 - percentiles on every fold. + times in one process; without this it would re-read ~100 MB Args: data_path (str): directory holding (or to receive) the .npz cache. Returns: dict: signals, labels, bearing_id, fault_type, severity, rpm, - waveform_id, center, scale. + waveform_id """ path = _cache_path(data_path) if not os.path.exists(path): @@ -284,7 +253,6 @@ def _load_cache(data_path): raise ValueError(f'{path}: expected signals of shape ' f'{(N_RECORDINGS, SIGNAL_LENGTH)}, got {signals.shape}') - cache['center'], cache['scale'] = encoding_constants(signals) return cache @@ -329,7 +297,7 @@ def _describe(name, cache, rows): # --------------------------------------------------------------------------- def _train_dataset(cache, rows, batch_size, dtype, seed): """Random 1 s crops with a random gain, re-drawn every epoch.""" - signals, center, scale = cache['signals'], cache['center'], cache['scale'] + signals = cache['signals'] labels = _labels(cache, rows) n_items = len(rows) * TRAIN_WINDOWS_PER_RECORDING @@ -346,10 +314,10 @@ def generator(): start = rng.integers(0, SIGNAL_LENGTH - WINDOW_LENGTH) window = np.array(signals[row, start:start + WINDOW_LENGTH], dtype=np.float32) - # Gain is applied to the float waveform, before encoding, so that it - # survives the per-recording rescale instead of cancelling out. + # Gain is applied to the float waveform window *= np.float32(rng.normal(1.0, GAIN_STD)) - window = encode_uint8(window, center[row], scale[row]) + # window = encode_uint8(window, center[row], scale[row]) + window = encode_uint8(window) yield frame_window(window), labels[i] signature = (tf.TensorSpec(shape=INPUT_SHAPE, dtype=tf.uint8), @@ -371,7 +339,7 @@ def _eval_arrays(cache, rows): Small enough to materialise (240 x 1200 x 35 uint8 is about 10 MB). """ - signals, center, scale = cache['signals'], cache['center'], cache['scale'] + signals = cache['signals'] labels = _labels(cache, rows) windows, targets = [], [] for i, row in enumerate(rows): @@ -379,7 +347,7 @@ def _eval_arrays(cache, rows): start = j * WINDOW_LENGTH window = np.array(signals[row, start:start + WINDOW_LENGTH], dtype=np.float32) - windows.append(encode_uint8(window, center[row], scale[row])) + windows.append(encode_uint8(window)) targets.append(labels[i]) return frame_window(np.stack(windows)), np.stack(targets) @@ -436,30 +404,31 @@ def get_data(data_path=DEFAULT_DATA_PATH, input_shape=INPUT_SHAPE, _eval_dataset(cache, test_rows, batch_size, dtype)) -def get_test_data(data_path=DEFAULT_DATA_PATH, input_shape=INPUT_SHAPE, - batch_size=BATCH_SIZE, dtype=tf.uint8, fold=FIXED_FOLD): - """Load only the held-out dataset for one fold. +# def get_test_data(data_path=DEFAULT_DATA_PATH, input_shape=INPUT_SHAPE, +# batch_size=BATCH_SIZE, dtype=tf.uint8, fold=FIXED_FOLD): +# """Load only the held-out dataset for one fold. - Deterministic: EVAL_WINDOWS_PER_RECORDING contiguous windows per recording, - no augmentation, so repeated evaluations of one model agree exactly. +# Deterministic: EVAL_WINDOWS_PER_RECORDING contiguous windows per recording. +# Evaluation of the dataset at this temporal resolution is defined by the +# reference protocol. - Args: - data_path (str): directory holding the .npz cache. - input_shape (tuple): model input shape; must be INPUT_SHAPE. - batch_size (int): the batch size. - dtype (tf.dtypes.DType, optional): input data type. Defaults to tf.uint8. - fold (int): bearing-disjoint fold index. Defaults to FIXED_FOLD. +# Args: +# data_path (str): directory holding the .npz cache. +# input_shape (tuple): model input shape; must be INPUT_SHAPE. +# batch_size (int): the batch size. +# dtype (tf.dtypes.DType, optional): input data type. Defaults to tf.uint8. +# fold (int): bearing-disjoint fold index. Defaults to FIXED_FOLD. - Returns: - tf.data.Dataset: held-out dataset yielding (uint8 windows, float labels). - """ - _check_input_shape(input_shape) - cache = _load_cache(data_path) +# Returns: +# tf.data.Dataset: held-out dataset yielding (uint8 windows, float labels). +# """ +# _check_input_shape(input_shape) +# cache = _load_cache(data_path) - _, test_rows = fold_rows(cache['bearing_id'], fold) - _describe(f'Held out (fold {fold})', cache, test_rows) +# _, test_rows = fold_rows(cache['bearing_id'], fold) +# _describe(f'Held out (fold {fold})', cache, test_rows) - return _eval_dataset(cache, test_rows, batch_size, dtype) +# return _eval_dataset(cache, test_rows, batch_size, dtype) def get_samples(data_path=DEFAULT_DATA_PATH, input_shape=INPUT_SHAPE, @@ -469,7 +438,8 @@ def get_samples(data_path=DEFAULT_DATA_PATH, input_shape=INPUT_SHAPE, Used for activation-sparsity measurement and hardware benchmarking, both of which need uint8 numpy input rather than a dataset. The draw is over the fold's *training* recordings: Akida timings depend on input activity, so the - windows have to be real, but they must not come from held-out data. + windows have to be real, but to avoid data leakage at quantization, they must + not come from held-out data. Up to EVAL_WINDOWS_PER_RECORDING per training recording (360 for a standard fold) are the deterministic tiled windows; beyond that the remainder are @@ -489,7 +459,7 @@ def get_samples(data_path=DEFAULT_DATA_PATH, input_shape=INPUT_SHAPE, """ _check_input_shape(input_shape) cache = _load_cache(data_path) - signals, center, scale = cache['signals'], cache['center'], cache['scale'] + signals = cache['signals'] train_rows, _ = fold_rows(cache['bearing_id'], fold) rng = np.random.default_rng(seed) @@ -506,7 +476,7 @@ def get_samples(data_path=DEFAULT_DATA_PATH, input_shape=INPUT_SHAPE, start = rng.integers(0, SIGNAL_LENGTH - WINDOW_LENGTH) window = np.array(signals[row, start:start + WINDOW_LENGTH], dtype=np.float32) - extra[k] = encode_uint8(window, center[row], scale[row]) + extra[k] = encode_uint8(window) samples = np.concatenate([samples, frame_window(extra)]) return samples.astype(np.uint8) @@ -609,135 +579,6 @@ def prepare_raw(raw_dir, data_path=DEFAULT_DATA_PATH): return path -# --------------------------------------------------------------------------- -# Self-checks -# --------------------------------------------------------------------------- -def check_splits(data_path=DEFAULT_DATA_PATH, folds=None, fixtures=None): - """Assert the split protocol holds for every fold. - - This is the port-fidelity test: if the fold enumeration were renumbered, or - the split drawn at the wrong granularity, these assertions catch it. - - Args: - data_path (str): directory holding the .npz cache. - folds (iterable, optional): folds to check. Defaults to every tuning and - evaluation fold. - fixtures (str, optional): directory of published run_*.parquet split - fixtures to cross-check the held-out bearing sets against. - - Returns: - int: the number of folds checked. - """ - cache = _load_cache(data_path) - bearing_id = cache['bearing_id'] - fault_type = cache['fault_type'] - all_bearings = set(bearing_id.tolist()) - folds = list(folds) if folds is not None else (list(TUNING_FOLDS) - + list(EVAL_FOLDS)) - - n_train = (len(all_bearings) - NUM_LABELS * N_BEARINGS_PER_FAULT_MODE) * 3 - n_test = NUM_LABELS * N_BEARINGS_PER_FAULT_MODE * 3 - - for fold in folds: - held_out = held_out_bearings_for_fold(fold) - train_rows, test_rows = fold_rows(bearing_id, fold) - - train_bearings = set(bearing_id[train_rows].tolist()) - test_bearings = set(bearing_id[test_rows].tolist()) - - assert not train_bearings & test_bearings, \ - f'fold {fold}: bearings on both sides: {train_bearings & test_bearings}' - assert train_bearings | test_bearings == all_bearings, \ - f'fold {fold}: bearings missing from the split' - assert test_bearings == set(held_out), \ - f'fold {fold}: test bearings {sorted(test_bearings)} != {held_out}' - assert len(train_rows) == n_train, \ - f'fold {fold}: {len(train_rows)} train recordings, expected {n_train}' - assert len(test_rows) == n_test, \ - f'fold {fold}: {len(test_rows)} test recordings, expected {n_test}' - - for mode, ids in FAULT_BEARING_IDS.items(): - n_held = len(test_bearings & set(ids)) - assert n_held == N_BEARINGS_PER_FAULT_MODE, \ - (f'fold {fold}: {n_held} {mode} bearings held out, ' - f'expected {N_BEARINGS_PER_FAULT_MODE}') - - # Every fault mode must appear on both sides, or a label column is - # constant in the test set and its AUROC is undefined. - for rows, side in ((train_rows, 'train'), (test_rows, 'test')): - present = {str(f) for f in fault_type[rows]} - assert present == {'Healthy', 'Inner', 'Outer', 'Ball', 'Cage'}, \ - f'fold {fold}: {side} side is missing fault types: {present}' - - print(f'Split protocol holds for {len(folds)} folds ' - f'({n_train} train / {n_test} test recordings each).') - - if fixtures is not None: - _check_against_fixtures(fixtures, folds) - - return len(folds) - - -def _check_against_fixtures(fixtures, folds): - """Cross-check held-out bearing sets against published split fixtures. - - Each run_.parquet holds a single row with `train_ids` and `test_ids` - columns listing bearing ids. Matching them fold-for-fold is the strongest - available evidence that the fold enumeration was ported without renumbering. - """ - import glob - - import pandas as pd - - checked = 0 - for path in sorted(glob.glob(os.path.join(fixtures, 'run_*.parquet'))): - fold = int(os.path.splitext(os.path.basename(path))[0].split('_')[1]) - if fold not in folds: - continue - frame = pd.read_parquet(path) - expected = sorted(int(b) for b in frame['test_ids'].iloc[0]) - actual = held_out_bearings_for_fold(fold) - assert actual == expected, \ - f'fold {fold}: held out {actual}, fixture says {expected}' - checked += 1 - - if checked: - print(f'Held-out bearings match the published fixtures for ' - f'{checked} folds.') - else: - print(f'No run_*.parquet fixtures found in {fixtures}.') - - -def report_encoding(data_path=DEFAULT_DATA_PATH): - """Print the per-recording encoding constants and their cost. - - This is how ENCODE_HEADROOM gets re-justified after any change: it reports - how many uint8 levels one signal standard deviation spans, and what fraction - of samples clip at plausible augmentation gains. - """ - cache = _load_cache(data_path) - signals, center, scale = cache['signals'], cache['center'], cache['scale'] - - std = signals.std(axis=1) - levels = 127.0 * std / scale - - print(f'Per-recording encoding, headroom {ENCODE_HEADROOM}, ' - f'percentile {ENCODE_PERCENTILE}, zero point {ZERO_POINT}') - print(f' centre min {center.min():9.3f} max {center.max():9.3f}') - print(f' signal std min {std.min():9.3f} max {std.max():9.3f} ' - f'ratio {std.max() / std.min():.1f}x') - print(f' uint8 levels per signal sigma: min {levels.min():.1f} ' - f'median {np.median(levels):.1f} max {levels.max():.1f}') - - print('\n clipped samples by augmentation gain:') - for gain in (1.0, 1.0 + GAIN_STD, 1.0 + 2 * GAIN_STD): - normalised = gain * (signals - center[:, None]) / scale[:, None] - clipped = np.abs(ZERO_POINT + 127.0 * normalised - ZERO_POINT) > 127.0 - per_recording = clipped.mean(axis=1) - print(f' gain {gain:4.1f} mean {per_recording.mean() * 100:7.4f}% ' - f'worst recording {per_recording.max() * 100:8.4f}%') - - if __name__ == '__main__': import argparse @@ -752,15 +593,6 @@ def report_encoding(data_path=DEFAULT_DATA_PATH): parser.add_argument('--prepare-raw', metavar='RAW_DIR', default=None, help='Rebuild the .npz cache from the raw Mendeley CSV ' 'tree instead of downloading it') - parser.add_argument('--check-splits', action='store_true', - help='Assert bearing-disjointness and the recording ' - 'counts for all 105 tuning and evaluation folds') - parser.add_argument('--fixtures', default=None, - help='Directory of published run_*.parquet split ' - 'fixtures to cross-check --check-splits against') - parser.add_argument('--report-encoding', action='store_true', - help='Report the per-recording uint8 encoding constants ' - 'and how much they clip') args = parser.parse_args() if args.prepare_raw is not None: @@ -768,12 +600,6 @@ def report_encoding(data_path=DEFAULT_DATA_PATH): print(f'Wrote {path} ({os.path.getsize(path) / 1e6:.0f} MB)') _load_cache.cache_clear() - if args.check_splits: - check_splits(args.data, fixtures=args.fixtures) - - if args.report_encoding: - report_encoding(args.data) - if not (args.check_splits or args.report_encoding): train_ds, test_ds = get_data(args.data, fold=args.fold, seed=args.seed) samples = get_samples(args.data, num_samples=100, fold=args.fold, diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_eval.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_eval.py index 41fca1d..5ae60c9 100644 --- a/akida1/model_zoo/uored_vafcls/uored_vafcls_eval.py +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_eval.py @@ -33,7 +33,7 @@ from brainchip_utils.hardware_utils import get_akida_device from brainchip_utils.plot_utils import pretty_print_sparsity from uored_vafcls_data import (FIXED_FOLD, LABEL_COLUMNS, BATCH_SIZE, - get_samples, get_test_data) + get_samples, get_data) tf.config.experimental.enable_op_determinism() @@ -47,11 +47,6 @@ # --------------------------------------------------------------------------- def auroc_scores(logits, labels): """Macro and per-label AUROC. - - Uses the same scikit-learn function the reference protocol uses, so the - metric can never be the source of a discrepancy between this port and the - published numbers. - Args: logits (np.ndarray): raw model outputs, shape (N, len(LABEL_COLUMNS)). labels (np.ndarray): binary indicator labels, same shape. @@ -85,7 +80,7 @@ def predict_keras_model(model, dataset): return np.concatenate(logits_all), np.concatenate(labels_all) -def evaluate_akida_model(akida_model, val_dataset): +def evaluate_akida_model(akida_model, dataset): """Run inference with an Akida model and return (logits, labels). Returns logits rather than predictions: AUROC is rank-based and multi-label, @@ -102,7 +97,7 @@ def evaluate_akida_model(akida_model, val_dataset): # Akida can't directly digest the tensorflow dataset, we need to # manually iterate over the dataset to deliver inputs as numpy arrays - for batch, label_batch in tqdm(val_dataset, desc="Evaluating on Akida"): + for batch, label_batch in tqdm(dataset, desc="Evaluating on Akida"): if not isinstance(batch, np.ndarray): batch = batch.numpy() @@ -153,7 +148,7 @@ def evaluate_akida_model(akida_model, val_dataset): # --------------------------------------------------------------------------- # Data loading # --------------------------------------------------------------------------- - test_ds = get_test_data(args.data, imsize, batch_size=args.batch_size, + _, test_ds = get_data(args.data, imsize, batch_size=args.batch_size, fold=args.fold) # --------------------------------------------------------------------------- diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_model.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_model.py index 55b27d0..f54380b 100644 --- a/akida1/model_zoo/uored_vafcls/uored_vafcls_model.py +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_model.py @@ -16,35 +16,22 @@ much like the framing of a speech signal. The 7x7 stem therefore correlates 7 consecutive samples across 7 consecutive frames: a lag/phase view. -(The source repo's docstring claimed this stem was equivalent to a (49, 1) -kernel at stride 7. That was true only of an earlier variant which transposed -the two axes before framing; that transpose is not applied here, and the -equivalence does not hold for this layout.) - -Input scaling is part of the model: a Rescaling layer inverts the data -pipeline's uint8 encoding, mapping [0, 255] back to the roughly [-1, 1] range -the network trains on. Keeping it in the graph means float training, quantized -training and Akida inference all see identical values, and cnn2snn folds the -layer into the first convolution at conversion time. +Input scaling is part of the model: a Rescaling layer normalizes the data from +the uint8 range furnished by the data preprocessing pipeline down to the roughly +[-1, 1] range the network trains on. Defining this Rescaling within the model +graph means float training, quantized training and Akida inference all see +identical values input values. The actual rescaling op itself is folded into the +weights of the first convolution layer by the cnn2snn package at conversion time. Hardware constraints that fixed the geometry -------------------------------------------- -Two AKD1500 constraints drove the design, and both are load-bearing: - -* The long dimension has to come first, hence (1200, 35) rather than (35, 1200). -* Every layer's second spatial dimension must stay above 1, which is why the - last two blocks omit their max-pool: the height runs 35 -> 29 -> 14 -> 7 -> 4 - -> 2 -> 1, and a sixth pool would take it to 0. - -Initializers ------------- -The source repo used custom initializers that reproduced PyTorch's defaults, to -keep a Keras port numerically comparable with a PyTorch reference. That -reference is not part of this example, and the custom bias initializer is what -forced a custom_object_scope workaround around every quantize and convert call -(Keras resolves initializers by name when it clones a model, including inside -the `cnn2snn convert` CLI where no scope can be installed). Keras defaults are -used here instead, which removes that whole class of problem. +Some AKD1500 constraints drove the design: +* Most layers on Akida 1 only accept 4-bit inputs; only the specialised Input + Convolutional layer takes uint8 inputs, and we definitely want to use that + here. However, that layer has some hard size constraints, as follow +* The second input dimension on that layer has a hard limit (256 on Akida 1). + Thus the long input dimension has to go first, (1200, 35) rather than (35, 1200). + Example ------- @@ -53,51 +40,13 @@ import argparse -from cnn2snn import AkidaVersion, set_akida_version from tf_keras import Model -from tf_keras.layers import (BatchNormalization, Conv2D, Dense, Flatten, Input, +from tf_keras.layers import (BatchNormalization, Conv2D, Dense, Input, MaxPooling2D, ReLU, Rescaling, GlobalAveragePooling2D) from tf_keras.utils import set_random_seed -from uored_vafcls_data import INPUT_SHAPE, NUM_LABELS, ZERO_POINT - -# BatchNorm settings carried over from the source model. -BN_MOMENTUM = 0.9 -BN_EPSILON = 1e-5 - -# Filters per block, and whether the block pools. The last two do not: the -# height is already 1 by then (see the module docstring). -BLOCK_FILTERS = (32, 64, 64, 64, 96) -BLOCK_POOLING = (True, True, True, True, True) -# BLOCK_FILTERS = (32, 64, 64, 64, 96, 128) -# BLOCK_POOLING = (True, True, True, True, True, False) - -STEM_FILTERS = 16 -STEM_KERNEL = (7, 7) -DENSE_UNITS = 512 -RELU_MAX = 6.0 - - -def _conv_block(x, filters, name, kernel=(3, 3), padding='same', pool=True, - pool_padding='same'): - """Conv -> BatchNorm -> ReLU6 [-> MaxPool], with no convolution bias. - - The bias is omitted because the BatchNorm that follows immediately - reintroduces a per-channel offset, so a conv bias would be redundant, and - cnn2snn folds the BatchNorm into the convolution at conversion. - """ - x = Conv2D(filters, kernel, padding=padding, use_bias=False, - name=f'{name}_conv')(x) - x = BatchNormalization(momentum=BN_MOMENTUM, epsilon=BN_EPSILON, - name=f'{name}_bn')(x) - x = ReLU(max_value=RELU_MAX, name=f'{name}_relu')(x) - if pool: - x = MaxPooling2D((2, 2), strides=(2, 2), padding=pool_padding, - name=f'{name}_pool')(x) - return x - -def build_uored_vafcls_model(seed=0): +def build_uored_vafcls_model(input_shape=(1200, 35, 1), fault_classes=4, seed=0): """Build the untrained akdcnn model. Args: @@ -105,40 +54,74 @@ def build_uored_vafcls_model(seed=0): Returns: tf_keras.Model: the model, expecting uint8 inputs of shape INPUT_SHAPE - and returning NUM_LABELS raw logits (one per fault mode; a healthy + and returning fault_classes raw logits (one per fault mode; a healthy bearing is all four logits low, not a fifth class). """ set_random_seed(seed) - inputs = Input(shape=INPUT_SHAPE, name='input') + # Model definition parameters + STEM_FILTERS = 16 + STEM_KERNEL_SIZE = 7 + + BLOCK_FILTERS = (32, 64, 64, 64, 96, 128) + BLOCK_POOLING = (True, True, True, True, True, False) + + DENSE_UNITS = 512 + + # BatchNorm settings + # Important in this case - unusually, we find that the tf_keras + # default values (0.99, 0.001) cause problems when the model is quantized + BN_MOMENTUM = 0.9 + BN_EPSILON = 1e-5 + + def _conv_block(x, filters, name, kernel=(3, 3), padding='same', pool=True, + pool_padding='same'): + """Re-usable block with + Conv -> BatchNorm -> ReLU6 [-> MaxPool] + """ + x = Conv2D(filters, kernel, padding=padding, use_bias=False, + name=f'{name}_conv')(x) + x = BatchNormalization(momentum=BN_MOMENTUM, epsilon=BN_EPSILON, + name=f'{name}_bn')(x) + x = ReLU(max_value=6.0, name=f'{name}_relu')(x) + if pool: + x = MaxPooling2D((2, 2), strides=(2, 2), padding=pool_padding, + name=f'{name}_pool')(x) + return x + + + inputs = Input(shape=input_shape, name='input') # Normalize incoming data from the uint8 range to [-1, 1] - # Folded into stem_conv at conversion - x = Rescaling(1.0 / 127.0, -1.0, name='rescaling')(inputs) + # Folded into stem_conv weights at conversion + x = Rescaling(1.0 / 128.0, -1.0, name='rescaling')(inputs) # Stem: a dense convolution with no padding - x = _conv_block(x, STEM_FILTERS, 'stem', kernel=STEM_KERNEL, + x = _conv_block(x, STEM_FILTERS, 'stem', kernel=STEM_KERNEL_SIZE, padding='valid', pool=True, pool_padding='valid') - for i, (filters, pool) in enumerate(zip(BLOCK_FILTERS, BLOCK_POOLING), + # Blocks + for i, (filters, pool) in enumerate(zip(BLOCK_FILTERS[:-1], BLOCK_POOLING[:-1]), start=1): x = _conv_block(x, filters, f'block{i}', pool=pool) + # Final block with Global Average Pooling + # Note that for Akida 1, it needs to be placed before the neighbouring ReLU x = Conv2D(128, 3, padding='same', use_bias=False, name='block6_conv')(x) x = BatchNormalization(momentum=BN_MOMENTUM, epsilon=BN_EPSILON, name='block6_bn')(x) x = GlobalAveragePooling2D(name='gap')(x) - x = ReLU(max_value=RELU_MAX, name='block6_relu')(x) + x = ReLU(max_value=6.0, name='block6_relu')(x) + # Dense Classifier Ending x = Dense(DENSE_UNITS, name='fc')(x) x = BatchNormalization(momentum=BN_MOMENTUM, epsilon=BN_EPSILON, name='fc_bn')(x) - x = ReLU(name='fc_relu')(x) - outputs = Dense(NUM_LABELS, name='predictions')(x) + x = ReLU(max_value=6.0, name='fc_relu')(x) + outputs = Dense(fault_classes, name='predictions')(x) model = Model(inputs, outputs, name='akdcnn_uored_vafcls') - return model diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_train.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_train.py index 2e64c78..facb11f 100644 --- a/akida1/model_zoo/uored_vafcls/uored_vafcls_train.py +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_train.py @@ -3,12 +3,8 @@ """ UORED-VAFCLS bearing fault training. -Used for both stages of the pipeline: full-precision training from the untrained -model, and quantization-aware tuning of the quantized one. Only the loaded -model, the epoch count and the learning rate differ between the two. - The loss is binary cross-entropy over four independent fault labels, not a -softmax over classes: a bearing can in principle show more than one fault, and +softmax over classes: a machine can in principle show more than one fault, and a healthy bearing is the all-zero label vector rather than a fifth class. The model emits raw logits, so `from_logits=True` throughout. @@ -17,7 +13,7 @@ only data not used for training is the held-out fold, and selecting weights on that would be exactly the leakage the bearing-disjoint split exists to prevent. The recipe is instead fixed - a set epoch count, evaluated at the final epoch - -and folds 0-4 are reserved as the budget for choosing it. Please do not "fix" +and folds 0-4 are reserved for tuning that pipeline. Do not "fix" this by adding a callback that monitors the held-out fold. Example @@ -38,50 +34,19 @@ from tf_keras.optimizers.schedules import CosineDecay from tf_keras.utils import set_random_seed -from uored_vafcls_data import FIXED_FOLD, NUM_LABELS, BATCH_SIZE, get_data +from uored_vafcls_data import get_data -# Must be called before any TF ops to make GPU ops deterministic. It matters -# more here than in most examples: without it, re-running one unchanged fold -# moved its AUROC by up to 0.024, which is as large as the architecture -# differences this example exists to compare. Costs some throughput. +# Must be called before any TF ops to make GPU ops deterministic. tf.config.experimental.enable_op_determinism() -def get_lr_schedule(initial_lr, total_steps, warmup_fraction=0.05): - """Cosine decay with a short linear warmup, over the real step count. - - The schedule has to span the steps actually taken. The source implementation - hard-coded the step count and undershot it by a sixth, which left the last - few epochs running at the schedule's floor of zero - training for fewer - epochs than it appeared to. Deriving total_steps from the dataset avoids - that, and avoids it silently recurring if the batch size or the number of - windows per recording ever changes. - - Args: - initial_lr (float): the peak learning rate, reached after warmup. - total_steps (int): optimizer steps over the whole run. - warmup_fraction (float): fraction of total_steps spent warming up. - - Returns: - tf_keras.optimizers.schedules.LearningRateSchedule: the schedule. - """ - warmup_steps = max(1, int(warmup_fraction * total_steps)) - return CosineDecay(initial_learning_rate=initial_lr * 0.01, - warmup_target=initial_lr, - warmup_steps=warmup_steps, - decay_steps=max(1, total_steps - warmup_steps)) - - -def train_uored_vafcls(model, train_ds, val_ds, epochs, learning_rate, +def train_uored_vafcls(model, train_ds, epochs, learning_rate, regularization=None, seed=0): """Train or quantization-aware tune the model on one fold. Args: model (tf_keras.Model): the model to train, float or quantized. train_ds (tf.data.Dataset): training dataset. - val_ds (tf.data.Dataset): optional dataset to report per-epoch metrics - on. Pass None, which is the default in the pipeline: the only - candidate is the held-out fold, and nothing may be decided from it. epochs (int): number of epochs. learning_rate (float): peak learning rate for the cosine schedule. regularization (float, optional): activity regularization on the ReLU @@ -93,9 +58,7 @@ def train_uored_vafcls(model, train_ds, val_ds, epochs, learning_rate, """ set_random_seed(seed) - # --------------------------------------------------------------------------- - # Model - # --------------------------------------------------------------------------- + # Optionaly add activity regularization for additional sparsity if regularization is not None: print('Adding Activity Regularization to ReLU layers') regularizer = regularizers.L1L2(regularization, regularization) @@ -103,23 +66,24 @@ def train_uored_vafcls(model, train_ds, val_ds, epochs, learning_rate, if isinstance(layer, ReLU): layer.activity_regularizer = regularizer + # Learning Rate Scheduler steps_per_epoch = len(train_ds) - lr_schedule = get_lr_schedule(learning_rate, steps_per_epoch * epochs) + total_steps = steps_per_epoch*epochs + warmup_fraction = 0.05 + warmup_steps = max(1, int(warmup_fraction * total_steps)) + lr_schedule = CosineDecay(initial_learning_rate=learning_rate * 0.01, + warmup_target=learning_rate, + warmup_steps=warmup_steps, + decay_steps=max(1, total_steps - warmup_steps)) + num_labels = model.output_shape[-1] model.compile(optimizer=Adam(learning_rate=lr_schedule), loss=BinaryCrossentropy(from_logits=True), metrics=[AUC(name='auroc', multi_label=True, - num_labels=NUM_LABELS)]) + num_labels=num_labels)]) - # --------------------------------------------------------------------------- # Training - # --------------------------------------------------------------------------- - print(f'Training for {epochs} epochs, {steps_per_epoch} steps per epoch ' - f'({steps_per_epoch * epochs} steps), peak LR {learning_rate}') - - # No callbacks: the recipe is a fixed number of epochs and the final-epoch - # weights are what gets evaluated. See the module docstring. - return model.fit(train_ds, epochs=epochs, validation_data=val_ds) + return model.fit(train_ds, epochs=epochs) if __name__ == '__main__': @@ -130,43 +94,32 @@ def train_uored_vafcls(model, train_ds, val_ds, epochs, learning_rate, help='Model save path') parser.add_argument('-d', '--data', default='./data/uored_vafcls', help='Directory holding the prepared .npz cache') - parser.add_argument('-b', '--batch_size', type=int, default=BATCH_SIZE, - help='Batch size. The default divides the 720 training ' - 'windows exactly, giving 6 steps per epoch') + parser.add_argument('-b', '--batch_size', type=int, default=120, + help='Batch size') parser.add_argument('-e', '--epochs', type=int, default=30) parser.add_argument('-lr', '--learning_rate', type=float, default=2e-4, help='Peak learning rate for the cosine schedule') parser.add_argument('-reg', '--regularization', type=float, default=None, help='Activity Regularization to increase sparsity') - parser.add_argument('--fold', type=int, default=FIXED_FOLD, - help='Bearing-disjoint fold index. Folds 0-4 are the ' - 'tuning folds, the only folds any hyperparameter ' - 'may be chosen on. Folds 5-104 are the 100 ' - 'evaluation folds; 5 is the fixed fold the ' - 'pretrained models and hardware benchmarks use') - parser.add_argument('--monitor-heldout', action='store_true', - help='Report per-epoch metrics on the held-out fold. ' - 'For eyeballing the training curve only - no ' - 'decision may be taken from it') + parser.add_argument('--fold', type=int, default=5, + help='Bearing-disjoint fold index.') parser.add_argument('--seed', type=int, default=0, help='Random seed for reproducibility') args = parser.parse_args() + # Model model = load_quantized_model(args.loadmodel) + + # Dataset train_ds, test_ds = get_data(args.data, model.input_shape[1:], args.batch_size, fold=args.fold, seed=args.seed) - val_ds = None - if args.monitor_heldout: - print('WARNING: --monitor-heldout reports metrics on the held-out ' - 'bearings. It is for watching the curve only; choosing anything ' - 'on the basis of it - epochs, learning rate, weights - leaks the ' - 'test fold. Use folds 0-4 for that.') - val_ds = test_ds - - train_uored_vafcls(model=model, train_ds=train_ds, val_ds=val_ds, + # Run Training + train_uored_vafcls(model=model, train_ds=train_ds, epochs=args.epochs, learning_rate=args.learning_rate, regularization=args.regularization, seed=args.seed) + + # Save Trained Model model.save(args.savemodel, include_optimizer=False) print(f'Model saved as {args.savemodel}.') diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_train.sh b/akida1/model_zoo/uored_vafcls/uored_vafcls_train.sh index 9892e90..682c92e 100644 --- a/akida1/model_zoo/uored_vafcls/uored_vafcls_train.sh +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_train.sh @@ -42,7 +42,7 @@ cnn2snn quantize -m models/akdcnn_uored_vafcls.h5 -i 8 -w 4 -a 4 # 5 - Quantization-aware tuning. Larger than this repository's usual 2 epochs: # at 6 steps per epoch, 2 epochs is 12 optimizer steps and recovers nothing. python uored_vafcls_train.py -l models/akdcnn_uored_vafcls_iq8_wq4_aq4.h5 \ - -s models/akdcnn_uored_vafcls_qat.h5 -e 30 -lr 2e-4 -b 120 $COMMON + -s models/akdcnn_uored_vafcls_qat.h5 -e 10 -lr 1e-5 -b 120 $COMMON # 6 - Evaluate the tuned quantized model python uored_vafcls_eval.py -l models/akdcnn_uored_vafcls_qat.h5 $COMMON From e1fed8fdf7a06ce57ed8e0fb35de48b83178008e Mon Sep 17 00:00:00 2001 From: Douglas McLelland Date: Thu, 17 Sep 2026 10:54:09 +0200 Subject: [PATCH 04/15] Comparison to naive split; metrics logged --- akida1/model_zoo/uored_vafcls/README.md | 108 ++++---- .../uored_vafcls/docs/README.md.template | 98 +++---- .../model_zoo/uored_vafcls/docs/metrics.json | 46 ++-- .../docs/ref_cv_auroc_distribution.png | Bin 0 -> 43156 bytes .../uored_vafcls_cross_validate.py | 27 +- .../uored_vafcls/uored_vafcls_data.py | 243 +++++++++++++++--- .../uored_vafcls/uored_vafcls_eval.py | 93 +++++-- .../uored_vafcls/uored_vafcls_naive_split.sh | 66 +++++ .../uored_vafcls/uored_vafcls_train.py | 12 +- .../uored_vafcls/uored_vafcls_train.sh | 2 +- 10 files changed, 465 insertions(+), 230 deletions(-) create mode 100644 akida1/model_zoo/uored_vafcls/docs/ref_cv_auroc_distribution.png create mode 100644 akida1/model_zoo/uored_vafcls/uored_vafcls_naive_split.sh diff --git a/akida1/model_zoo/uored_vafcls/README.md b/akida1/model_zoo/uored_vafcls/README.md index f80c1db..ce6ed8a 100644 --- a/akida1/model_zoo/uored_vafcls/README.md +++ b/akida1/model_zoo/uored_vafcls/README.md @@ -8,74 +8,45 @@ The interesting part of this example is not the model. It is the evaluation: thi ## Model Card -**Cross-validated performance** — TBD bearing-disjoint folds × TBD seed. **This is the model's performance.** +**The same model, the same recipe, two ways of splitting the data.** The top row is a single train-and-evaluate run under a deliberately naive split; the bottom row is the mean over 100 bearing-disjoint folds × 1 seed. Identical architecture, identical training budget — 720 training and 240 held-out windows either way — so the only thing separating the two rows is whether the split was allowed to leak. **The bottom row is the model's performance.** - - - - + + + + + + - - - - - - - - - - + + + + + + - - - - + + + + + +
StageMacro AUROC (mean ± std)Worst foldBest foldSplitFloat AUROCQAT AUROCAkida AUROCParamsActivation sparsity
Full precisionTBD ± TBDTBDTBD
Quantized + tuned (4-bit)TBD ± TBD——Naive segment-level (leaky)0.99900.99580.9963335,44438.08%
AkidaTBD ± TBDTBDTBDBearing-level (protocol)0.92370.91970.9192335,44436.52%
-Standard error of the mean on the Akida figure is TBD. Parameters: TBD. Activation sparsity: TBD. - -> **Read the table above, not the one below.** Individual folds range from TBD to TBD — a spread many times larger than the difference between any two architectures anyone would want to compare. Quoting one fold's score as a model's performance is quoting noise. See [Why cross-validation is not optional](#why-cross-validation-is-not-optional). +> **The gap between those two rows is the point of this example.** The naive row is what this dataset hands you if you split it the obvious way — by time within each recording, so the same bearing appears on both sides. It is not a result, and it is not quoted anywhere else in this README. The bearing-level row is lower because it is honest, not because the model is worse. +> +> Read the bearing-level row as a distribution, not a number. Individual folds range from 0.7408 to 0.9963 — a spread many times larger than the difference between any two architectures anyone would want to compare. Quoting one fold's score as a model's performance is quoting noise. See [Why cross-validation is not optional](#why-cross-validation-is-not-optional). Distribution of per-fold AUROC -**Reference model** — fold TBD only, the weights shipped in `pretrained_models/`. - - - - - - - - - - - - - - - - - - - - - - - - -
Float AUROCQAT AUROCAkida AUROCinnerouterballcage
TBDTBDTBDTBDTBDTBDTBD
- -Fold TBD is the *first* of the 100 evaluation folds. It was picked because it is first, not because of its score. It scores TBD against the TBD mean above. Every hardware number below comes from this one model on this one fold — **it is not the model's performance**, it is a fixed, reproducible artefact to benchmark. - **AKD1500 hardware benchmark** @@ -118,6 +89,8 @@ Fold TBD is the *first* of the 100 evaluation folds. It was picked because it is
+*Measured on the model trained on the bearing-level split at fold 5 — the weights shipped in `pretrained_models/` — which is a fixed, reproducible artefact rather than a performance claim. The architecture, the input shape and the quantization are identical under either split, so these latency, power and mapping figures apply to both rows of the Model Card.* + Full model benchmark `Minimal` mapping uses the fewest neural processors that will hold the model; `AllNPs` spreads it over everything available, which usually trades power for latency. This model is an unusual case: it already saturates the device in `Minimal` mode, so the two mappings are nearly identical. @@ -130,16 +103,16 @@ The model maps entirely to hardware — 1 HRC input convolution, 41 CNP1 convolu This dataset has 60 recordings from **20 physical bearings**. A fold trains on 12 bearings and is tested on the 8 held out. That is a small enough test set that the choice of which bearings are held out matters more than almost anything about the model: -- The **across-fold** standard deviation is TBD, and folds range from TBD to TBD with the architecture, the recipe and the seed all held fixed. -- The **within-fold** standard deviation across random seeds is TBD (measured over TBD folds at 3 seeds each) — comparable to the entire across-fold spread. +- The **across-fold** standard deviation is 0.0546, and folds range from 0.7408 to 0.9963 with the architecture, the recipe and the seed all held fixed. +- The **within-fold** standard deviation across random seeds is 0.0184 (measured over 25 folds at 3 seeds each) — comparable to the entire across-fold spread. So a single-fold, single-seed AUROC cannot distinguish two architectures on this dataset. For scale: the WDCNN network this model descends from and this model itself differ by about 0.03 in their 100-fold means, which is *less* than one fold's seed-to-seed noise. Any comparison made on one fold is a coin toss dressed up as a result. The practical rules this example follows: -1. **The reported number is the mean over all TBD evaluation folds**, produced by `uored_vafcls_cross_validate.py`. The per-fold results are committed in [`docs/cv_results.csv`](docs/cv_results.csv) so the mean is auditable without re-running anything. +1. **The reported number is the mean over all 100 evaluation folds**, produced by `uored_vafcls_cross_validate.py`. The per-fold results are committed in [`docs/cv_results.csv`](docs/cv_results.csv) so the mean is auditable without re-running anything. 2. **Folds 0–4 are the tuning budget.** Every hyperparameter — epochs, learning rate, the QAT schedule, the input encoding constants — was chosen there. Folds 5–104 are never tuned on. -3. **Single-fold artefacts are labelled as such.** The pretrained weights and every hardware measurement come from fold TBD, and are presented as a fixed reference point, not a performance claim. +3. **Single-fold artefacts are labelled as such.** The pretrained weights and every hardware measurement come from fold 5, and are presented as a fixed reference point, not a performance claim. ## Requirements @@ -266,7 +239,7 @@ Run the full pipeline on one fold — build, train, quantize, tune, convert, eva bash uored_vafcls_train.sh [DATADIR] [FOLD] [SEED] ``` -`FOLD` defaults to TBD and `SEED` to 0. It takes about a minute on a single GPU, plus benchmarking. The nine steps are the standard Akida 1 sequence; see the script for the exact commands. +`FOLD` defaults to 5 and `SEED` to 0. It takes about a minute on a single GPU, plus benchmarking. The nine steps are the standard Akida 1 sequence; see the script for the exact commands. ### Cross-validation @@ -283,16 +256,25 @@ It runs the whole chain on each of the 100 evaluation folds, appending to `docs/ `README.md` in this folder is **generated** — edit [`docs/README.md.template`](docs/README.md.template), never `README.md` directly. The performance tables are filled from `docs/metrics.json`, which is written by the `--save-metrics` flags: ```bash -python uored_vafcls_eval.py -l pretrained_models/akdcnn_uored_vafcls.h5 --save-metrics -python uored_vafcls_eval.py -l pretrained_models/akdcnn_uored_vafcls_qat.h5 --save-metrics -python uored_vafcls_eval.py -l pretrained_models/akdcnn_uored_vafcls_qat.fbz --save-metrics -python uored_vafcls_benchmark.py -l pretrained_models/akdcnn_uored_vafcls_qat.fbz --save-metrics -python uored_vafcls_cross_validate.py --save-metrics # 30-60 min +# Bearing-level split (the protocol) - the Model Card's bottom row. +# Its AUROCs come from the sweep, never from a single model. +python uored_vafcls_cross_validate.py --save-metrics # 30-60 min python uored_vafcls_cross_validate.py --first-fold 5 --last-fold 29 --seeds 3 \ - -o docs/cv_seed_std.csv --save-seed-std # the seed-noise figure + -o docs/cv_seed_std.csv --save-seed-std # the seed-noise figure +python uored_vafcls_eval.py -l pretrained_models/akdcnn_uored_vafcls.h5 --save-metrics # params +python uored_vafcls_eval.py -l pretrained_models/akdcnn_uored_vafcls_qat.fbz --save-metrics # sparsity +python uored_vafcls_benchmark.py -l pretrained_models/akdcnn_uored_vafcls_qat.fbz --save-metrics + +# Naive segment-level split (the comparator) - the Model Card's top row. +# One model, one run: the split is singular and its score is stable to ~0.001. +bash uored_vafcls_naive_split.sh +python uored_vafcls_eval.py -l models/akdcnn_uored_vafcls_segment.h5 --split-mode segment --save-metrics +python uored_vafcls_eval.py -l models/akdcnn_uored_vafcls_segment_qat.h5 --split-mode segment --save-metrics +python uored_vafcls_eval.py -l models/akdcnn_uored_vafcls_segment_qat.fbz --split-mode segment --save-metrics + python update_readme.py ``` -Each key in `metrics.json` has exactly one writer, so the scripts can be run in any order without racing. +Each key in `metrics.json` has exactly one writer, so the scripts can be run in any order without racing. There is deliberately no bearing-level single-model AUROC: nothing in the README reports one, because reporting one is the mistake this example is about. **The standing rule:** any change to the architecture, the training recipe or the `ENCODE_*` constants invalidates the cross-validated table, and requires a fresh 100-fold sweep. Re-running the fixed fold is *not* a substitute — that is the whole point of this example. diff --git a/akida1/model_zoo/uored_vafcls/docs/README.md.template b/akida1/model_zoo/uored_vafcls/docs/README.md.template index f19e151..4552552 100644 --- a/akida1/model_zoo/uored_vafcls/docs/README.md.template +++ b/akida1/model_zoo/uored_vafcls/docs/README.md.template @@ -8,74 +8,45 @@ The interesting part of this example is not the model. It is the evaluation: thi ## Model Card -**Cross-validated performance** — {cv_folds} bearing-disjoint folds × {cv_seeds} seed. **This is the model's performance.** +**The same model, the same recipe, two ways of splitting the data.** The top row is a single train-and-evaluate run under a deliberately naive split; the bottom row is the mean over {cv_folds} bearing-disjoint folds × {cv_seeds} seed. Identical architecture, identical training budget — 720 training and 240 held-out windows either way — so the only thing separating the two rows is whether the split was allowed to leak. **The bottom row is the model's performance.** - - - - + + + + + + - - - - + + + + + + - - - - - - - - - - + + + + + +
StageMacro AUROC (mean ± std)Worst foldBest foldSplitFloat AUROCQAT AUROCAkida AUROCParamsActivation sparsity
Full precision{cv_float_auroc_mean} ± {cv_float_auroc_std}{cv_float_auroc_min}{cv_float_auroc_max}Naive segment-level (leaky){segment_float_auroc}{segment_qat_auroc}{segment_akida_auroc}{segment_params}{segment_sparsity}
Quantized + tuned (4-bit){cv_qat_auroc_mean} ± {cv_qat_auroc_std}——
Akida{cv_akida_auroc_mean} ± {cv_akida_auroc_std}{cv_akida_auroc_min}{cv_akida_auroc_max}Bearing-level (protocol){cv_float_auroc_mean}{cv_qat_auroc_mean}{cv_akida_auroc_mean}{params}{sparsity}
-Standard error of the mean on the Akida figure is {cv_akida_auroc_sem}. Parameters: {params}. Activation sparsity: {sparsity}. - -> **Read the table above, not the one below.** Individual folds range from {cv_akida_auroc_min} to {cv_akida_auroc_max} — a spread many times larger than the difference between any two architectures anyone would want to compare. Quoting one fold's score as a model's performance is quoting noise. See [Why cross-validation is not optional](#why-cross-validation-is-not-optional). +> **The gap between those two rows is the point of this example.** The naive row is what this dataset hands you if you split it the obvious way — by time within each recording, so the same bearing appears on both sides. It is not a result, and it is not quoted anywhere else in this README. The bearing-level row is lower because it is honest, not because the model is worse. +> +> Read the bearing-level row as a distribution, not a number. Individual folds range from {cv_akida_auroc_min} to {cv_akida_auroc_max} — a spread many times larger than the difference between any two architectures anyone would want to compare. Quoting one fold's score as a model's performance is quoting noise. See [Why cross-validation is not optional](#why-cross-validation-is-not-optional). Distribution of per-fold AUROC -**Reference model** — fold {fixed_fold} only, the weights shipped in `pretrained_models/`. - - - - - - - - - - - - - - - - - - - - - - - - -
Float AUROCQAT AUROCAkida AUROCinnerouterballcage
{float_auroc}{qat_auroc}{akida_auroc}{akida_auroc_inner}{akida_auroc_outer}{akida_auroc_ball}{akida_auroc_cage}
- -Fold {fixed_fold} is the *first* of the 100 evaluation folds. It was picked because it is first, not because of its score. It scores {cv_fixed_fold_akida_auroc} against the {cv_akida_auroc_mean} mean above. Every hardware number below comes from this one model on this one fold — **it is not the model's performance**, it is a fixed, reproducible artefact to benchmark. - **AKD1500 hardware benchmark** @@ -118,6 +89,8 @@ Fold {fixed_fold} is the *first* of the 100 evaluation folds. It was picked beca
+*Measured on the model trained on the bearing-level split at fold {fixed_fold} — the weights shipped in `pretrained_models/` — which is a fixed, reproducible artefact rather than a performance claim. The architecture, the input shape and the quantization are identical under either split, so these latency, power and mapping figures apply to both rows of the Model Card.* + Full model benchmark `Minimal` mapping uses the fewest neural processors that will hold the model; `AllNPs` spreads it over everything available, which usually trades power for latency. This model is an unusual case: it already saturates the device in `Minimal` mode, so the two mappings are nearly identical. @@ -283,16 +256,25 @@ It runs the whole chain on each of the 100 evaluation folds, appending to `docs/ `README.md` in this folder is **generated** — edit [`docs/README.md.template`](docs/README.md.template), never `README.md` directly. The performance tables are filled from `docs/metrics.json`, which is written by the `--save-metrics` flags: ```bash -python uored_vafcls_eval.py -l pretrained_models/akdcnn_uored_vafcls.h5 --save-metrics -python uored_vafcls_eval.py -l pretrained_models/akdcnn_uored_vafcls_qat.h5 --save-metrics -python uored_vafcls_eval.py -l pretrained_models/akdcnn_uored_vafcls_qat.fbz --save-metrics -python uored_vafcls_benchmark.py -l pretrained_models/akdcnn_uored_vafcls_qat.fbz --save-metrics -python uored_vafcls_cross_validate.py --save-metrics # 30-60 min +# Bearing-level split (the protocol) - the Model Card's bottom row. +# Its AUROCs come from the sweep, never from a single model. +python uored_vafcls_cross_validate.py --save-metrics # 30-60 min python uored_vafcls_cross_validate.py --first-fold 5 --last-fold 29 --seeds 3 \ - -o docs/cv_seed_std.csv --save-seed-std # the seed-noise figure + -o docs/cv_seed_std.csv --save-seed-std # the seed-noise figure +python uored_vafcls_eval.py -l pretrained_models/akdcnn_uored_vafcls.h5 --save-metrics # params +python uored_vafcls_eval.py -l pretrained_models/akdcnn_uored_vafcls_qat.fbz --save-metrics # sparsity +python uored_vafcls_benchmark.py -l pretrained_models/akdcnn_uored_vafcls_qat.fbz --save-metrics + +# Naive segment-level split (the comparator) - the Model Card's top row. +# One model, one run: the split is singular and its score is stable to ~0.001. +bash uored_vafcls_naive_split.sh +python uored_vafcls_eval.py -l models/akdcnn_uored_vafcls_segment.h5 --split-mode segment --save-metrics +python uored_vafcls_eval.py -l models/akdcnn_uored_vafcls_segment_qat.h5 --split-mode segment --save-metrics +python uored_vafcls_eval.py -l models/akdcnn_uored_vafcls_segment_qat.fbz --split-mode segment --save-metrics + python update_readme.py ``` -Each key in `metrics.json` has exactly one writer, so the scripts can be run in any order without racing. +Each key in `metrics.json` has exactly one writer, so the scripts can be run in any order without racing. There is deliberately no bearing-level single-model AUROC: nothing in the README reports one, because reporting one is the mistake this example is about. **The standing rule:** any change to the architecture, the training recipe or the `ENCODE_*` constants invalidates the cross-validated table, and requires a fresh 100-fold sweep. Re-running the fixed fold is *not* a substitute — that is the whole point of this example. diff --git a/akida1/model_zoo/uored_vafcls/docs/metrics.json b/akida1/model_zoo/uored_vafcls/docs/metrics.json index 8b7473a..d229e7c 100644 --- a/akida1/model_zoo/uored_vafcls/docs/metrics.json +++ b/akida1/model_zoo/uored_vafcls/docs/metrics.json @@ -1,28 +1,22 @@ { - "cv_folds": "TBD", - "cv_seeds": "TBD", - "cv_float_auroc_mean": "TBD", - "cv_float_auroc_std": "TBD", - "cv_float_auroc_min": "TBD", - "cv_float_auroc_max": "TBD", - "cv_qat_auroc_mean": "TBD", - "cv_qat_auroc_std": "TBD", - "cv_akida_auroc_mean": "TBD", - "cv_akida_auroc_std": "TBD", - "cv_akida_auroc_min": "TBD", - "cv_akida_auroc_max": "TBD", - "cv_akida_auroc_sem": "TBD", - "params": "TBD", - "sparsity": "TBD", - "fixed_fold": "TBD", - "float_auroc": "TBD", - "qat_auroc": "TBD", - "akida_auroc": "TBD", - "akida_auroc_inner": "TBD", - "akida_auroc_outer": "TBD", - "akida_auroc_ball": "TBD", - "akida_auroc_cage": "TBD", - "cv_fixed_fold_akida_auroc": "TBD", + "segment_float_auroc": "0.9990", + "segment_qat_auroc": "0.9958", + "segment_akida_auroc": "0.9963", + "segment_params": "335,444", + "segment_sparsity": "38.08%", + "cv_folds": "100", + "cv_seeds": "1", + "cv_float_auroc_mean": "0.9237", + "cv_qat_auroc_mean": "0.9197", + "cv_akida_auroc_mean": "0.9192", + "cv_akida_auroc_std": "0.0546", + "cv_akida_auroc_min": "0.7408", + "cv_akida_auroc_max": "0.9963", + "cv_seed_std": "0.0184", + "cv_seed_std_folds": "25", + "params": "335,444", + "sparsity": "36.52%", + "fixed_fold": "5", "minimal_nps": "TBD", "minimal_passes": "TBD", "minimal_cycles": "TBD", @@ -38,7 +32,5 @@ "allnps_total_P": "TBD", "allnps_total_E": "TBD", "allnps_dyn_P": "TBD", - "allnps_dyn_E": "TBD", - "cv_seed_std": "TBD", - "cv_seed_std_folds": "TBD" + "allnps_dyn_E": "TBD" } diff --git a/akida1/model_zoo/uored_vafcls/docs/ref_cv_auroc_distribution.png b/akida1/model_zoo/uored_vafcls/docs/ref_cv_auroc_distribution.png new file mode 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+# The segment split trains on all 60 recordings rather than 36, so it needs its +# own per-recording count to land on the same 720 windows per epoch: 60 x 12. +# Matching the budget is what makes the two modes comparable - at 20 windows per +# recording the naive split would get 1200 windows and 10 steps per epoch, and +# its higher score would confound leakage with 67% more gradient steps. The test +# side needs no such adjustment: the last 40% of a recording holds exactly 4 +# whole windows, and 60 x 4 = 240, the same as 24 held-out recordings x 10. +SEGMENT_TRAIN_WINDOWS_PER_RECORDING = 12 + # --------------------------------------------------------------------------- # uint8 input encoding # --------------------------------------------------------------------------- @@ -188,6 +241,65 @@ def fold_rows(bearing_id, fold, n_per_mode=N_BEARINGS_PER_FAULT_MODE, return np.flatnonzero(~is_test), np.flatnonzero(is_test) +# --------------------------------------------------------------------------- +# The naive comparator +# --------------------------------------------------------------------------- +# A time region is a half-open (start, stop) in samples. Every window drawn for +# a split comes from one of these, which is the whole of what separates the two +# modes: 'bearing' draws from the full recording on both sides and separates the +# rows instead, 'segment' shares every row and separates the time. +FULL_REGION = (0, SIGNAL_LENGTH) +SEGMENT_TRAIN_REGION = (0, SEGMENT_BOUNDARY) +SEGMENT_TEST_REGION = (SEGMENT_BOUNDARY, SIGNAL_LENGTH) + +SplitPlan = namedtuple('SplitPlan', ['mode', 'train_rows', 'test_rows', + 'train_region', 'test_region', + 'train_windows_per_recording']) + + +def check_split_mode(split_mode): + """Validate a split mode, returning it unchanged. + + Args: + split_mode (str): one of SPLIT_MODES. + + Returns: + str: `split_mode`. + """ + if split_mode not in SPLIT_MODES: + raise ValueError(f'split_mode must be one of {SPLIT_MODES}, got ' + f'{split_mode!r}') + return split_mode + + +def split_plan(bearing_id, fold, split_mode=DEFAULT_SPLIT_MODE): + """Resolve one split into rows, time regions and a training window budget. + + Both modes are described in the module docstring. This is the only place + that branches on the mode; everything downstream consumes the plan. + + Args: + bearing_id (np.ndarray): per-recording bearing id, shape (N_RECORDINGS,). + fold (int): fold index. Ignored by 'segment', which has one split. + split_mode (str): one of SPLIT_MODES. + + Returns: + SplitPlan: rows and time regions for each side, plus the number of + training windows to draw per recording. + """ + check_split_mode(split_mode) + + if split_mode == 'segment': + rows = np.arange(len(bearing_id)) + return SplitPlan(split_mode, rows, rows, SEGMENT_TRAIN_REGION, + SEGMENT_TEST_REGION, + SEGMENT_TRAIN_WINDOWS_PER_RECORDING) + + train_rows, test_rows = fold_rows(bearing_id, fold) + return SplitPlan(split_mode, train_rows, test_rows, FULL_REGION, + FULL_REGION, TRAIN_WINDOWS_PER_RECORDING) + + # --------------------------------------------------------------------------- # Input encoding # --------------------------------------------------------------------------- @@ -281,25 +393,58 @@ def _labels(cache, rows): return cache['labels'][rows].astype(np.float32) -def _describe(name, cache, rows): +def _describe(name, cache, rows, region=FULL_REGION): """Print what a set of recordings contains, by bearing and fault mode.""" bearings = sorted(set(cache['bearing_id'][rows].tolist())) counts = {} for fault_type in cache['fault_type'][rows]: counts[str(fault_type)] = counts.get(str(fault_type), 0) + 1 breakdown = ', '.join(f'{k} {v}' for k, v in sorted(counts.items())) + span = '' + if tuple(region) != FULL_REGION: + span = (f', t = {region[0] / SAMPLE_RATE:.1f}-' + f'{region[1] / SAMPLE_RATE:.1f} s') print(f'{name}: {len(rows)} recordings from {len(bearings)} bearings ' - f'{bearings} ({breakdown})') + f'{bearings} ({breakdown}{span})') + + +def _describe_split(cache, plan, fold): + """Print the split, and how much of it leaks.""" + if plan.mode == 'segment': + print(f'Naive segment split: every recording contributes both sides, ' + f'cut at {SEGMENT_SPLIT_FRACTION:.0%} ' + f'({SEGMENT_BOUNDARY / SAMPLE_RATE:.1f} s). There is only one ' + f'such split, so the fold index is ignored.') + else: + print(f'Fold {fold}: holding out bearings ' + f'{held_out_bearings_for_fold(fold)}') + + _describe('Train', cache, plan.train_rows, plan.train_region) + _describe('Held out', cache, plan.test_rows, plan.test_region) + + # The diagnostic that distinguishes the two modes at a glance. A protocol + # split shares nothing; the naive one shares everything. + train_bearings = set(cache['bearing_id'][plan.train_rows].tolist()) + test_bearings = set(cache['bearing_id'][plan.test_rows].tolist()) + shared = train_bearings & test_bearings + verdict = 'LEAKS' if shared else 'leakage-free' + print(f'Train and held out share {len(shared)} of ' + f'{len(train_bearings | test_bearings)} bearings -> {verdict}') # --------------------------------------------------------------------------- # Window generation # --------------------------------------------------------------------------- -def _train_dataset(cache, rows, batch_size, dtype, seed): - """Random 1 s crops with a random gain, re-drawn every epoch.""" +def _train_dataset(cache, rows, batch_size, dtype, seed, region=FULL_REGION, + windows_per_recording=TRAIN_WINDOWS_PER_RECORDING): + """Random 1 s crops with a random gain, re-drawn every epoch. + + Crops start anywhere in `region` that leaves a whole window inside it. + """ signals = cache['signals'] labels = _labels(cache, rows) - n_items = len(rows) * TRAIN_WINDOWS_PER_RECORDING + n_items = len(rows) * windows_per_recording + region_start, region_stop = region # An independent generator, not the global Keras RNG. The shell pipeline # re-seeds in a fresh process between building and training, so only an @@ -311,7 +456,7 @@ def generator(): for idx in range(n_items): i = idx % len(rows) row = rows[i] - start = rng.integers(0, SIGNAL_LENGTH - WINDOW_LENGTH) + start = rng.integers(region_start, region_stop - WINDOW_LENGTH) window = np.array(signals[row, start:start + WINDOW_LENGTH], dtype=np.float32) # Gain is applied to the float waveform @@ -334,17 +479,22 @@ def generator(): ) -def _eval_arrays(cache, rows): - """Deterministic contiguous tiling: EVAL_WINDOWS_PER_RECORDING per recording. +def _eval_arrays(cache, rows, region=FULL_REGION): + """Deterministic contiguous tiling of `region`, as many whole windows as fit. + + That is EVAL_WINDOWS_PER_RECORDING (10) over a full recording, or 4 over the + segment split's test region and 6 over its training region. Small enough to materialise (240 x 1200 x 35 uint8 is about 10 MB). """ signals = cache['signals'] labels = _labels(cache, rows) + region_start, region_stop = region + n_windows = (region_stop - region_start) // WINDOW_LENGTH windows, targets = [], [] for i, row in enumerate(rows): - for j in range(EVAL_WINDOWS_PER_RECORDING): - start = j * WINDOW_LENGTH + for j in range(n_windows): + start = region_start + j * WINDOW_LENGTH window = np.array(signals[row, start:start + WINDOW_LENGTH], dtype=np.float32) windows.append(encode_uint8(window)) @@ -352,8 +502,8 @@ def _eval_arrays(cache, rows): return frame_window(np.stack(windows)), np.stack(targets) -def _eval_dataset(cache, rows, batch_size, dtype): - x, y = _eval_arrays(cache, rows) +def _eval_dataset(cache, rows, batch_size, dtype, region=FULL_REGION): + x, y = _eval_arrays(cache, rows, region) return ( tf.data.Dataset.from_tensor_slices((x, y)) .batch(batch_size) @@ -366,8 +516,9 @@ def _eval_dataset(cache, rows, batch_size, dtype): # Public API # --------------------------------------------------------------------------- def get_data(data_path=DEFAULT_DATA_PATH, input_shape=INPUT_SHAPE, - batch_size=BATCH_SIZE, dtype=tf.uint8, fold=FIXED_FOLD, seed=0): - """Load the training and held-out datasets for one bearing-disjoint fold. + batch_size=BATCH_SIZE, dtype=tf.uint8, fold=FIXED_FOLD, seed=0, + split_mode=DEFAULT_SPLIT_MODE): + """Load the training and held-out datasets for one fold. There is deliberately no validation split. With 36 training recordings, carving one out would cost about a sixth of the data and would itself have to @@ -385,8 +536,11 @@ def get_data(data_path=DEFAULT_DATA_PATH, input_shape=INPUT_SHAPE, dtype (tf.dtypes.DType, optional): input data type. Defaults to tf.uint8. fold (int): bearing-disjoint fold index. Folds 0-4 are the tuning budget; 5-104 are the evaluation folds. Defaults to FIXED_FOLD. + Ignored when `split_mode` is 'segment', which has one split. seed (int): seed for the crop positions, the gains and the shuffle order. The split itself does not depend on it. + split_mode (str): 'bearing' for the protocol, 'segment' for the naive + comparator. See the module docstring. Defaults to 'bearing'. Returns: tf.data.Dataset, tf.data.Dataset: training dataset, held-out dataset. @@ -394,14 +548,14 @@ def get_data(data_path=DEFAULT_DATA_PATH, input_shape=INPUT_SHAPE, _check_input_shape(input_shape) cache = _load_cache(data_path) - train_rows, test_rows = fold_rows(cache['bearing_id'], fold) - print(f'Fold {fold}: holding out bearings ' - f'{held_out_bearings_for_fold(fold)}') - _describe('Train', cache, train_rows) - _describe('Held out', cache, test_rows) + plan = split_plan(cache['bearing_id'], fold, split_mode) + _describe_split(cache, plan, fold) - return (_train_dataset(cache, train_rows, batch_size, dtype, seed), - _eval_dataset(cache, test_rows, batch_size, dtype)) + return (_train_dataset(cache, plan.train_rows, batch_size, dtype, seed, + plan.train_region, + plan.train_windows_per_recording), + _eval_dataset(cache, plan.test_rows, batch_size, dtype, + plan.test_region)) # def get_test_data(data_path=DEFAULT_DATA_PATH, input_shape=INPUT_SHAPE, @@ -432,27 +586,31 @@ def get_data(data_path=DEFAULT_DATA_PATH, input_shape=INPUT_SHAPE, def get_samples(data_path=DEFAULT_DATA_PATH, input_shape=INPUT_SHAPE, - num_samples=1024, fold=FIXED_FOLD, seed=0): + num_samples=1024, fold=FIXED_FOLD, seed=0, + split_mode=DEFAULT_SPLIT_MODE): """Load a block of training windows as a plain uint8 numpy array. Used for activation-sparsity measurement and hardware benchmarking, both of which need uint8 numpy input rather than a dataset. The draw is over the - fold's *training* recordings: Akida timings depend on input activity, so the + fold's *training* data: Akida timings depend on input activity, so the windows have to be real, but to avoid data leakage at quantization, they must - not come from held-out data. + not come from held-out data. Under 'segment' that means the training side of + the time cut, since every recording appears on both sides. - Up to EVAL_WINDOWS_PER_RECORDING per training recording (360 for a standard - fold) are the deterministic tiled windows; beyond that the remainder are - seeded random crops. + The deterministic tiled windows of the training region come first - 360 of + them either way, 36 recordings x 10 under 'bearing' and 60 x 6 under + 'segment'; beyond that the remainder are seeded random crops. Args: data_path (str): directory holding the .npz cache. input_shape (tuple): model input shape; must be INPUT_SHAPE. num_samples (int): number of windows to return. Defaults to 1024. fold (int): bearing-disjoint fold index. Defaults to FIXED_FOLD. + Ignored when `split_mode` is 'segment'. seed (int): seed for the draw order and for any random crops. Akida timings and sparsity depend on input activity, so varying this varies the measurement. + split_mode (str): 'bearing' or 'segment'. Defaults to 'bearing'. Returns: np.ndarray: shape (num_samples,) + INPUT_SHAPE, dtype uint8. @@ -461,10 +619,12 @@ def get_samples(data_path=DEFAULT_DATA_PATH, input_shape=INPUT_SHAPE, cache = _load_cache(data_path) signals = cache['signals'] - train_rows, _ = fold_rows(cache['bearing_id'], fold) + plan = split_plan(cache['bearing_id'], fold, split_mode) + train_rows = plan.train_rows + region_start, region_stop = plan.train_region rng = np.random.default_rng(seed) - tiled, _ = _eval_arrays(cache, train_rows) + tiled, _ = _eval_arrays(cache, train_rows, plan.train_region) idx = rng.permutation(len(tiled))[:num_samples] samples = tiled[idx] @@ -473,7 +633,7 @@ def get_samples(data_path=DEFAULT_DATA_PATH, input_shape=INPUT_SHAPE, extra = np.empty((shortfall, WINDOW_LENGTH), dtype=np.uint8) for k in range(shortfall): row = train_rows[rng.integers(0, len(train_rows))] - start = rng.integers(0, SIGNAL_LENGTH - WINDOW_LENGTH) + start = rng.integers(region_start, region_stop - WINDOW_LENGTH) window = np.array(signals[row, start:start + WINDOW_LENGTH], dtype=np.float32) extra[k] = encode_uint8(window) @@ -590,6 +750,11 @@ def prepare_raw(raw_dir, data_path=DEFAULT_DATA_PATH): help='Bearing-disjoint fold to summarise') parser.add_argument('--seed', type=int, default=0, help='Seed for the crop positions, gains and shuffle') + parser.add_argument('--split-mode', choices=SPLIT_MODES, + default=DEFAULT_SPLIT_MODE, + help="'bearing' is the leakage-free protocol; " + "'segment' is the naive time-wise split within " + 'each recording, for comparison only') parser.add_argument('--prepare-raw', metavar='RAW_DIR', default=None, help='Rebuild the .npz cache from the raw Mendeley CSV ' 'tree instead of downloading it') @@ -600,10 +765,12 @@ def prepare_raw(raw_dir, data_path=DEFAULT_DATA_PATH): print(f'Wrote {path} ({os.path.getsize(path) / 1e6:.0f} MB)') _load_cache.cache_clear() - if not (args.check_splits or args.report_encoding): - train_ds, test_ds = get_data(args.data, fold=args.fold, seed=args.seed) - samples = get_samples(args.data, num_samples=100, fold=args.fold, - seed=args.seed) - print(f'\nBatches: train {len(train_ds)}, held out {len(test_ds)}') - print(f'Samples for benchmarking: {samples.shape} {samples.dtype} ' - f'range [{samples.min()}, {samples.max()}]') + train_ds, test_ds = get_data(args.data, fold=args.fold, seed=args.seed, + split_mode=args.split_mode) + samples = get_samples(args.data, num_samples=100, fold=args.fold, + seed=args.seed, split_mode=args.split_mode) + print(f'\nBatches: train {len(train_ds)}, held out {len(test_ds)}') + print(f'Windows: train {len(train_ds) * BATCH_SIZE}, ' + f'held out {sum(1 for _ in test_ds.unbatch())}') + print(f'Samples for benchmarking: {samples.shape} {samples.dtype} ' + f'range [{samples.min()}, {samples.max()}]') diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_eval.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_eval.py index 5ae60c9..8526018 100644 --- a/akida1/model_zoo/uored_vafcls/uored_vafcls_eval.py +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_eval.py @@ -32,8 +32,8 @@ from akida_models.sparsity import compute_sparsity from brainchip_utils.hardware_utils import get_akida_device from brainchip_utils.plot_utils import pretty_print_sparsity -from uored_vafcls_data import (FIXED_FOLD, LABEL_COLUMNS, BATCH_SIZE, - get_samples, get_data) +from uored_vafcls_data import (DEFAULT_SPLIT_MODE, FIXED_FOLD, LABEL_COLUMNS, + BATCH_SIZE, SPLIT_MODES, get_samples, get_data) tf.config.experimental.enable_op_determinism() @@ -126,9 +126,15 @@ def evaluate_akida_model(akida_model, dataset): help='Bearing-disjoint fold to evaluate on') parser.add_argument('--seed', type=int, default=0, help='Random seed for the sparsity sample draw') + parser.add_argument('--split-mode', choices=SPLIT_MODES, + default=DEFAULT_SPLIT_MODE, + help="'bearing' is the leakage-free protocol; " + "'segment' is the naive time-wise split within " + 'each recording, for comparison only') parser.add_argument('--save-metrics', action='store_true', help='Write AUROC (and param count for .h5) to ' - 'metrics.json. Only valid on the fixed fold') + 'metrics.json. Only valid on the fixed fold of ' + 'the bearing-disjoint split') args = parser.parse_args() # --------------------------------------------------------------------------- @@ -149,7 +155,7 @@ def evaluate_akida_model(akida_model, dataset): # Data loading # --------------------------------------------------------------------------- _, test_ds = get_data(args.data, imsize, batch_size=args.batch_size, - fold=args.fold) + fold=args.fold, split_mode=args.split_mode) # --------------------------------------------------------------------------- # Evaluation @@ -160,7 +166,10 @@ def evaluate_akida_model(akida_model, dataset): logits, labels = predict_keras_model(model, test_ds) scores = auroc_scores(logits, labels) - report(scores, f'Fold {args.fold} held-out') + if args.split_mode == 'segment': + report(scores, 'Naive segment split (LEAKY, for comparison only)') + else: + report(scores, f'Fold {args.fold} held-out') # --------------------------------------------------------------------------- # Activation sparsity @@ -169,7 +178,8 @@ def evaluate_akida_model(akida_model, dataset): if isakida: samples = get_samples(args.data, imsize, num_samples=NUM_SPARSITY_SAMPLES, - fold=args.fold, seed=args.seed) + fold=args.fold, seed=args.seed, + split_mode=args.split_mode) sparsity_dict = compute_sparsity(model, samples=samples) pretty_print_sparsity(sparsity_dict) sparsity = float(np.mean(list(sparsity_dict.values()))) @@ -185,29 +195,58 @@ def evaluate_akida_model(akida_model, dataset): # This should only be used for code maintenance, when the model or training # pipeline is updated and a new trained model integrated. # - # Guarded on the fold: the README's reference table is specifically the - # fixed fold, and a stray --fold would otherwise silently replace it - # with a number from a different held-out set. - if args.fold != FIXED_FOLD: - raise SystemExit( - f'--save-metrics writes the README reference table, which is ' - f'fold {FIXED_FOLD}; refusing to write results from fold ' - f'{args.fold}.') - + # What this script may publish depends on the split, and the two cases + # are deliberately not symmetric. + # + # Under the naive split it writes the Model Card's top row outright: one + # train-and-evaluate run is the whole of that row, because the segment + # split is singular and its score is stable to about 0.001. + # + # Under the protocol it writes no AUROC at all. The bottom row is the + # 100-fold mean from uored_vafcls_cross_validate.py, and a single-fold + # number presented beside it is exactly the error this example exists to + # argue against. All that is taken from here is what a single model can + # honestly supply: the parameter count and the measured sparsity. metrics_path = pathlib.Path(__file__).parent / 'docs' / 'metrics.json' metrics = json.loads(metrics_path.read_text()) if metrics_path.exists() else {} auroc_str = f'{scores["macro"]:.4f}' - metrics['fixed_fold'] = str(args.fold) - if isakida: - metrics['akida_auroc'] = auroc_str - metrics['sparsity'] = f'{sparsity * 100:.2f}%' - # The per-label breakdown is only reported for the deployed model. - for name in LABEL_COLUMNS: - metrics[f'akida_auroc_{name}'] = f'{scores[name]:.4f}' - elif 'qat' in pathlib.Path(args.loadmodel).stem: - metrics['qat_auroc'] = auroc_str + written = [] + + if args.split_mode == 'segment': + if isakida: + metrics['segment_akida_auroc'] = auroc_str + metrics['segment_sparsity'] = f'{sparsity * 100:.2f}%' + written = ['segment_akida_auroc', 'segment_sparsity'] + elif 'qat' in pathlib.Path(args.loadmodel).stem: + metrics['segment_qat_auroc'] = auroc_str + written = ['segment_qat_auroc'] + else: + metrics['segment_float_auroc'] = auroc_str + metrics['segment_params'] = f'{model.count_params():,}' + written = ['segment_float_auroc', 'segment_params'] else: - metrics['float_auroc'] = auroc_str - metrics['params'] = f'{model.count_params():,}' + # Guarded on the fold: the hardware table and the sparsity figure + # are specifically the fixed fold, and a stray --fold would + # otherwise silently replace them with a different model's. The + # guard is meaningless under 'segment', which has only one split. + if args.fold != FIXED_FOLD: + raise SystemExit( + f'--save-metrics writes README figures measured on fold ' + f'{FIXED_FOLD}; refusing to write results from fold ' + f'{args.fold}.') + + metrics['fixed_fold'] = str(args.fold) + written = ['fixed_fold'] + if isakida: + metrics['sparsity'] = f'{sparsity * 100:.2f}%' + written.append('sparsity') + elif 'qat' not in pathlib.Path(args.loadmodel).stem: + metrics['params'] = f'{model.count_params():,}' + written.append('params') + metrics_path.write_text(json.dumps(metrics, indent=4) + '\n') - print(f'Metrics saved to {metrics_path}') + print(f'Metrics saved to {metrics_path}: {", ".join(written)}') + if args.split_mode != 'segment' and written == ['fixed_fold']: + print('Note: nothing else to write. The README reports no ' + 'single-model AUROC for the bearing-disjoint split - that ' + 'row is the cross-validated mean.') diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_naive_split.sh b/akida1/model_zoo/uored_vafcls/uored_vafcls_naive_split.sh new file mode 100644 index 0000000..fe3c213 --- /dev/null +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_naive_split.sh @@ -0,0 +1,66 @@ +#!/usr/bin/env bash +# Copyright 2025 Brainchip Holdings Ltd. Apache 2.0 License +# +# The naive comparator: the same pipeline as uored_vafcls_train.sh, run under a +# deliberately leaky split. Build, train, quantize, tune, convert, and evaluate +# at each stage. +# +# Usage: bash uored_vafcls_naive_split.sh [DATADIR] [SEED] +# +# THE NUMBER THIS PRINTS IS NOT A RESULT. It is inflated by construction and +# exists only to be compared against uored_vafcls_train.sh, which runs the same +# model and the same recipe under the bearing-disjoint protocol. +# +# The naive split divides each recording by time - the first 60% trains, the +# last 40% tests - so all 20 bearings appear on both sides, and windows seconds +# apart from the same bearing, the same fault and the same run are split across +# train and test. Both splits draw the same 720 training and 240 test windows, +# so the difference in score is attributable to the leakage and nothing else. +# See the DATA SPLIT PROTOCOL notes in uored_vafcls_data.py. +# +# There is no FOLD argument: unlike the protocol, this split is singular, so +# there is nothing to cross-validate. Its score is stable to about 0.001 where +# the bearing-disjoint folds span 0.04-0.05. Re-run with a different SEED to +# confirm that. +# +# Models are written with a _segment suffix so this never overwrites the +# protocol-trained checkpoints that the README tables describe. + +DATADIR="${1:-}" +DATA_ARG=${DATADIR:+-d "$DATADIR"} +SEED="${2:-${SEED:-0}}" +COMMON="--split-mode segment --seed $SEED $DATA_ARG" + +echo "UORED-VAFCLS NAIVE pipeline: seed ${SEED}, segment split (leaky by design)" + +# 1 - Build the untrained model +python uored_vafcls_model.py -s models/akdcnn_uored_vafcls_segment_untrained.h5 \ + --seed "$SEED" + +# 2 - Full-precision training +python uored_vafcls_train.py -l models/akdcnn_uored_vafcls_segment_untrained.h5 \ + -s models/akdcnn_uored_vafcls_segment.h5 -e 30 -lr 2e-4 -b 120 $COMMON + +# 3 - Evaluate the float model +python uored_vafcls_eval.py -l models/akdcnn_uored_vafcls_segment.h5 $COMMON + +# 4 - Post-training quantization: 4-bit weights and activations, 8-bit input +cnn2snn quantize -m models/akdcnn_uored_vafcls_segment.h5 -i 8 -w 4 -a 4 + +# 5 - Quantization-aware tuning +python uored_vafcls_train.py \ + -l models/akdcnn_uored_vafcls_segment_iq8_wq4_aq4.h5 \ + -s models/akdcnn_uored_vafcls_segment_qat.h5 -e 10 -lr 1e-5 -b 120 $COMMON + +# 6 - Evaluate the tuned quantized model +python uored_vafcls_eval.py -l models/akdcnn_uored_vafcls_segment_qat.h5 $COMMON + +# 7 - Convert to Akida +cnn2snn convert -m models/akdcnn_uored_vafcls_segment_qat.h5 + +# 8 - Evaluate on Akida +python uored_vafcls_eval.py -l models/akdcnn_uored_vafcls_segment_qat.fbz $COMMON + +# No hardware benchmark: latency and sparsity are properties of the graph and +# the input statistics, not of how the data was split. For those numbers run +# uored_vafcls_benchmark.py against the protocol-trained model. diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_train.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_train.py index facb11f..0a51e5b 100644 --- a/akida1/model_zoo/uored_vafcls/uored_vafcls_train.py +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_train.py @@ -34,7 +34,8 @@ from tf_keras.optimizers.schedules import CosineDecay from tf_keras.utils import set_random_seed -from uored_vafcls_data import get_data +from uored_vafcls_data import (DEFAULT_SPLIT_MODE, FIXED_FOLD, SPLIT_MODES, + get_data) # Must be called before any TF ops to make GPU ops deterministic. tf.config.experimental.enable_op_determinism() @@ -101,10 +102,15 @@ def train_uored_vafcls(model, train_ds, epochs, learning_rate, help='Peak learning rate for the cosine schedule') parser.add_argument('-reg', '--regularization', type=float, default=None, help='Activity Regularization to increase sparsity') - parser.add_argument('--fold', type=int, default=5, + parser.add_argument('--fold', type=int, default=FIXED_FOLD, help='Bearing-disjoint fold index.') parser.add_argument('--seed', type=int, default=0, help='Random seed for reproducibility') + parser.add_argument('--split-mode', choices=SPLIT_MODES, + default=DEFAULT_SPLIT_MODE, + help="'bearing' is the leakage-free protocol; " + "'segment' is the naive time-wise split within " + 'each recording, for comparison only') args = parser.parse_args() # Model @@ -113,7 +119,7 @@ def train_uored_vafcls(model, train_ds, epochs, learning_rate, # Dataset train_ds, test_ds = get_data(args.data, model.input_shape[1:], args.batch_size, fold=args.fold, - seed=args.seed) + seed=args.seed, split_mode=args.split_mode) # Run Training train_uored_vafcls(model=model, train_ds=train_ds, diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_train.sh b/akida1/model_zoo/uored_vafcls/uored_vafcls_train.sh index 682c92e..a939405 100644 --- a/akida1/model_zoo/uored_vafcls/uored_vafcls_train.sh +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_train.sh @@ -42,7 +42,7 @@ cnn2snn quantize -m models/akdcnn_uored_vafcls.h5 -i 8 -w 4 -a 4 # 5 - Quantization-aware tuning. Larger than this repository's usual 2 epochs: # at 6 steps per epoch, 2 epochs is 12 optimizer steps and recovers nothing. python uored_vafcls_train.py -l models/akdcnn_uored_vafcls_iq8_wq4_aq4.h5 \ - -s models/akdcnn_uored_vafcls_qat.h5 -e 10 -lr 1e-5 -b 120 $COMMON + -s models/akdcnn_uored_vafcls_qat.h5 -e 10 -lr 5e-5 -b 120 $COMMON # 6 - Evaluate the tuned quantized model python uored_vafcls_eval.py -l models/akdcnn_uored_vafcls_qat.h5 $COMMON From bf152b07dd4c064f5f18b5770974829ebad9242f Mon Sep 17 00:00:00 2001 From: Douglas McLelland Date: Thu, 17 Sep 2026 10:54:34 +0200 Subject: [PATCH 05/15] Pretrained single-fold models --- .../uored_vafcls/pretrained_models/akdcnn_uored_vafcls.h5 | 3 +++ .../uored_vafcls/pretrained_models/akdcnn_uored_vafcls_qat.fbz | 3 +++ .../uored_vafcls/pretrained_models/akdcnn_uored_vafcls_qat.h5 | 3 +++ 3 files changed, 9 insertions(+) create mode 100644 akida1/model_zoo/uored_vafcls/pretrained_models/akdcnn_uored_vafcls.h5 create mode 100644 akida1/model_zoo/uored_vafcls/pretrained_models/akdcnn_uored_vafcls_qat.fbz create mode 100644 akida1/model_zoo/uored_vafcls/pretrained_models/akdcnn_uored_vafcls_qat.h5 diff --git a/akida1/model_zoo/uored_vafcls/pretrained_models/akdcnn_uored_vafcls.h5 b/akida1/model_zoo/uored_vafcls/pretrained_models/akdcnn_uored_vafcls.h5 new file mode 100644 index 0000000..22d2e35 --- /dev/null +++ b/akida1/model_zoo/uored_vafcls/pretrained_models/akdcnn_uored_vafcls.h5 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:58b53b23c7c0b8f3da8b1b99769856e89e2865ea7a734e40362721826fbfae70 +size 1434032 diff --git a/akida1/model_zoo/uored_vafcls/pretrained_models/akdcnn_uored_vafcls_qat.fbz b/akida1/model_zoo/uored_vafcls/pretrained_models/akdcnn_uored_vafcls_qat.fbz new file mode 100644 index 0000000..7aac9d5 --- /dev/null +++ b/akida1/model_zoo/uored_vafcls/pretrained_models/akdcnn_uored_vafcls_qat.fbz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:99c41669d0e12409c553dcd8125cc79d38ce66bde3b274498d44dfdd87295e0a +size 402863 diff --git a/akida1/model_zoo/uored_vafcls/pretrained_models/akdcnn_uored_vafcls_qat.h5 b/akida1/model_zoo/uored_vafcls/pretrained_models/akdcnn_uored_vafcls_qat.h5 new file mode 100644 index 0000000..413cb77 --- /dev/null +++ b/akida1/model_zoo/uored_vafcls/pretrained_models/akdcnn_uored_vafcls_qat.h5 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:61b7ea5d4a770f16379149f2095c7abee4e22c1439388800fa387cf114ddf3a9 +size 1386544 From 4f4d8f7b63bf27b75fe6385d0447e92347d67dd0 Mon Sep 17 00:00:00 2001 From: Douglas McLelland Date: Thu, 17 Sep 2026 22:46:26 +0200 Subject: [PATCH 06/15] model fixed for akida --- akida1/model_zoo/uored_vafcls/README.md | 14 +++++----- .../model_zoo/uored_vafcls/docs/metrics.json | 16 ++++++------ .../uored_vafcls/uored_vafcls_data.py | 2 +- .../uored_vafcls/uored_vafcls_model.py | 26 +++++++++++++++---- 4 files changed, 37 insertions(+), 21 deletions(-) diff --git a/akida1/model_zoo/uored_vafcls/README.md b/akida1/model_zoo/uored_vafcls/README.md index ce6ed8a..77ee394 100644 --- a/akida1/model_zoo/uored_vafcls/README.md +++ b/akida1/model_zoo/uored_vafcls/README.md @@ -32,18 +32,18 @@ The interesting part of this example is not the model. It is the evaluation: thi Bearing-level (protocol) - 0.9237 - 0.9197 - 0.9192 + 0.8950 + 0.8939 + 0.8939 335,444 - 36.52% + 37.81% > **The gap between those two rows is the point of this example.** The naive row is what this dataset hands you if you split it the obvious way — by time within each recording, so the same bearing appears on both sides. It is not a result, and it is not quoted anywhere else in this README. The bearing-level row is lower because it is honest, not because the model is worse. > -> Read the bearing-level row as a distribution, not a number. Individual folds range from 0.7408 to 0.9963 — a spread many times larger than the difference between any two architectures anyone would want to compare. Quoting one fold's score as a model's performance is quoting noise. See [Why cross-validation is not optional](#why-cross-validation-is-not-optional). +> Read the bearing-level row as a distribution, not a number. Individual folds range from 0.6989 to 0.9892 — a spread many times larger than the difference between any two architectures anyone would want to compare. Quoting one fold's score as a model's performance is quoting noise. See [Why cross-validation is not optional](#why-cross-validation-is-not-optional). Distribution of per-fold AUROC @@ -103,8 +103,8 @@ The model maps entirely to hardware — 1 HRC input convolution, 41 CNP1 convolu This dataset has 60 recordings from **20 physical bearings**. A fold trains on 12 bearings and is tested on the 8 held out. That is a small enough test set that the choice of which bearings are held out matters more than almost anything about the model: -- The **across-fold** standard deviation is 0.0546, and folds range from 0.7408 to 0.9963 with the architecture, the recipe and the seed all held fixed. -- The **within-fold** standard deviation across random seeds is 0.0184 (measured over 25 folds at 3 seeds each) — comparable to the entire across-fold spread. +- The **across-fold** standard deviation is 0.0666, and folds range from 0.6989 to 0.9892 with the architecture, the recipe and the seed all held fixed. +- The **within-fold** standard deviation across random seeds is 0.0186 (measured over 25 folds at 3 seeds each) — comparable to the entire across-fold spread. So a single-fold, single-seed AUROC cannot distinguish two architectures on this dataset. For scale: the WDCNN network this model descends from and this model itself differ by about 0.03 in their 100-fold means, which is *less* than one fold's seed-to-seed noise. Any comparison made on one fold is a coin toss dressed up as a result. diff --git a/akida1/model_zoo/uored_vafcls/docs/metrics.json b/akida1/model_zoo/uored_vafcls/docs/metrics.json index d229e7c..78bafa0 100644 --- a/akida1/model_zoo/uored_vafcls/docs/metrics.json +++ b/akida1/model_zoo/uored_vafcls/docs/metrics.json @@ -6,16 +6,16 @@ "segment_sparsity": "38.08%", "cv_folds": "100", "cv_seeds": "1", - "cv_float_auroc_mean": "0.9237", - "cv_qat_auroc_mean": "0.9197", - "cv_akida_auroc_mean": "0.9192", - "cv_akida_auroc_std": "0.0546", - "cv_akida_auroc_min": "0.7408", - "cv_akida_auroc_max": "0.9963", - "cv_seed_std": "0.0184", + "cv_float_auroc_mean": "0.8950", + "cv_qat_auroc_mean": "0.8939", + "cv_akida_auroc_mean": "0.8939", + "cv_akida_auroc_std": "0.0666", + "cv_akida_auroc_min": "0.6989", + "cv_akida_auroc_max": "0.9892", + "cv_seed_std": "0.0186", "cv_seed_std_folds": "25", "params": "335,444", - "sparsity": "36.52%", + "sparsity": "37.81%", "fixed_fold": "5", "minimal_nps": "TBD", "minimal_passes": "TBD", diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_data.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_data.py index 91fd646..fb4daae 100644 --- a/akida1/model_zoo/uored_vafcls/uored_vafcls_data.py +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_data.py @@ -153,7 +153,7 @@ # Windowing # --------------------------------------------------------------------------- WINDOW_LENGTH = 42_000 # 1.0 s -FRAME_WIDTH = 35 # samples per frame; 42000 / 35 = 1200 frames +FRAME_WIDTH = 140 # samples per frame; 42000 / 35 = 1200 frames INPUT_SHAPE = (WINDOW_LENGTH // FRAME_WIDTH, FRAME_WIDTH, 1) # (1200, 35, 1) TRAIN_WINDOWS_PER_RECORDING = 20 # 36 recordings x 20 = 720 windows per epoch diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_model.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_model.py index f54380b..6ef852e 100644 --- a/akida1/model_zoo/uored_vafcls/uored_vafcls_model.py +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_model.py @@ -42,11 +42,15 @@ from tf_keras import Model from tf_keras.layers import (BatchNormalization, Conv2D, Dense, Input, - MaxPooling2D, ReLU, Rescaling, GlobalAveragePooling2D) + MaxPooling2D, ReLU, Rescaling, GlobalAveragePooling2D, Flatten) from tf_keras.utils import set_random_seed +import akida +from cnn2snn import quantize, convert -def build_uored_vafcls_model(input_shape=(1200, 35, 1), fault_classes=4, seed=0): +from uored_vafcls_data import INPUT_SHAPE + +def build_uored_vafcls_model(input_shape=INPUT_SHAPE, fault_classes=4, seed=0): """Build the untrained akdcnn model. Args: @@ -97,8 +101,13 @@ def _conv_block(x, filters, name, kernel=(3, 3), padding='same', pool=True, x = Rescaling(1.0 / 128.0, -1.0, name='rescaling')(inputs) # Stem: a dense convolution with no padding - x = _conv_block(x, STEM_FILTERS, 'stem', kernel=STEM_KERNEL_SIZE, - padding='valid', pool=True, pool_padding='valid') + x = Conv2D(STEM_FILTERS, STEM_KERNEL_SIZE, padding='valid', use_bias=False, + name='stem_conv')(x) + x = BatchNormalization(momentum=BN_MOMENTUM, epsilon=BN_EPSILON, + name='stem_bn')(x) + x = ReLU(max_value=6.0, name='stem_relu')(x) + x = MaxPooling2D((2, 2), strides=(2, 2), padding='valid', + name='stem_pool')(x) # Blocks for i, (filters, pool) in enumerate(zip(BLOCK_FILTERS[:-1], BLOCK_POOLING[:-1]), @@ -108,7 +117,7 @@ def _conv_block(x, filters, name, kernel=(3, 3), padding='same', pool=True, # Final block with Global Average Pooling # Note that for Akida 1, it needs to be placed before the neighbouring ReLU x = Conv2D(128, 3, padding='same', use_bias=False, - name='block6_conv')(x) + name='block6_conv')(x) x = BatchNormalization(momentum=BN_MOMENTUM, epsilon=BN_EPSILON, name='block6_bn')(x) x = GlobalAveragePooling2D(name='gap')(x) @@ -138,3 +147,10 @@ def _conv_block(x, filters, name, kernel=(3, 3), padding='same', pool=True, model.summary() model.save(args.savepath, include_optimizer=False) print(f'Model saved to {args.savepath}') + + qmodel = quantize(model, weight_quantization=4, activ_quantization=4, input_weight_quantization=8) + ak_model = convert(qmodel) + device = akida.AKD1500() + + ak_model.map(device, mode=akida.MapMode.Minimal, hw_only=True) + ak_model.summary() From c9d0b3163457b63f6420a88430998cb7d9d3c77d Mon Sep 17 00:00:00 2001 From: Douglas McLelland Date: Fri, 18 Sep 2026 00:25:31 +0200 Subject: [PATCH 07/15] Pretrained models updated --- .../uored_vafcls/pretrained_models/akdcnn_uored_vafcls.h5 | 2 +- .../uored_vafcls/pretrained_models/akdcnn_uored_vafcls_qat.fbz | 2 +- .../uored_vafcls/pretrained_models/akdcnn_uored_vafcls_qat.h5 | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/akida1/model_zoo/uored_vafcls/pretrained_models/akdcnn_uored_vafcls.h5 b/akida1/model_zoo/uored_vafcls/pretrained_models/akdcnn_uored_vafcls.h5 index 22d2e35..8230da4 100644 --- a/akida1/model_zoo/uored_vafcls/pretrained_models/akdcnn_uored_vafcls.h5 +++ b/akida1/model_zoo/uored_vafcls/pretrained_models/akdcnn_uored_vafcls.h5 @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:58b53b23c7c0b8f3da8b1b99769856e89e2865ea7a734e40362721826fbfae70 +oid sha256:2bad90b1198e665d7089697bbd78af760eb3bf2b346ed2681c561636dbf76b64 size 1434032 diff --git a/akida1/model_zoo/uored_vafcls/pretrained_models/akdcnn_uored_vafcls_qat.fbz b/akida1/model_zoo/uored_vafcls/pretrained_models/akdcnn_uored_vafcls_qat.fbz index 7aac9d5..69e2df3 100644 --- a/akida1/model_zoo/uored_vafcls/pretrained_models/akdcnn_uored_vafcls_qat.fbz +++ b/akida1/model_zoo/uored_vafcls/pretrained_models/akdcnn_uored_vafcls_qat.fbz @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:99c41669d0e12409c553dcd8125cc79d38ce66bde3b274498d44dfdd87295e0a +oid sha256:bd7e0dd12a1cba7770eb2427462de7f3f776564fb18509d8daabe11f39ff7076 size 402863 diff --git a/akida1/model_zoo/uored_vafcls/pretrained_models/akdcnn_uored_vafcls_qat.h5 b/akida1/model_zoo/uored_vafcls/pretrained_models/akdcnn_uored_vafcls_qat.h5 index 413cb77..c06585e 100644 --- a/akida1/model_zoo/uored_vafcls/pretrained_models/akdcnn_uored_vafcls_qat.h5 +++ b/akida1/model_zoo/uored_vafcls/pretrained_models/akdcnn_uored_vafcls_qat.h5 @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:61b7ea5d4a770f16379149f2095c7abee4e22c1439388800fa387cf114ddf3a9 +oid sha256:200840acad818dc81668f34d657f0b2fe418aa1b14ce5b4ac00ce86d83e41b07 size 1386544 From 2717d47e404edd555172acc0c8101a79dae748ce Mon Sep 17 00:00:00 2001 From: dmclelland Date: Fri, 18 Sep 2026 00:31:44 +0200 Subject: [PATCH 08/15] HW benchmark run --- akida1/model_zoo/uored_vafcls/README.md | 32 +++++++++--------- .../model_zoo/uored_vafcls/docs/metrics.json | 32 +++++++++--------- .../docs/ref_benchmark_results_full.png | Bin 0 -> 263887 bytes .../docs/ref_benchmark_results_layers.png | Bin 0 -> 102189 bytes 4 files changed, 32 insertions(+), 32 deletions(-) create mode 100644 akida1/model_zoo/uored_vafcls/docs/ref_benchmark_results_full.png create mode 100644 akida1/model_zoo/uored_vafcls/docs/ref_benchmark_results_layers.png diff --git a/akida1/model_zoo/uored_vafcls/README.md b/akida1/model_zoo/uored_vafcls/README.md index 77ee394..da4c259 100644 --- a/akida1/model_zoo/uored_vafcls/README.md +++ b/akida1/model_zoo/uored_vafcls/README.md @@ -66,25 +66,25 @@ The interesting part of this example is not the model. It is the evaluation: thi Minimal - TBD - TBD - TBD - TBD - TBD - TBD - TBD - TBD + 17 + 1 + 3947210 + 9.868 + 178.5 + 1.784 + 66.6 + 0.666 AllNPs - TBD - TBD - TBD - TBD - TBD - TBD - TBD - TBD + 27 + 1 + 2932072 + 7.330 + 201.4 + 1.502 + 88.8 + 0.662 diff --git a/akida1/model_zoo/uored_vafcls/docs/metrics.json b/akida1/model_zoo/uored_vafcls/docs/metrics.json index 78bafa0..e0f3fe1 100644 --- a/akida1/model_zoo/uored_vafcls/docs/metrics.json +++ b/akida1/model_zoo/uored_vafcls/docs/metrics.json @@ -17,20 +17,20 @@ "params": "335,444", "sparsity": "37.81%", "fixed_fold": "5", - "minimal_nps": "TBD", - "minimal_passes": "TBD", - "minimal_cycles": "TBD", - "minimal_latency_ms": "TBD", - "minimal_total_P": "TBD", - "minimal_total_E": "TBD", - "minimal_dyn_P": "TBD", - "minimal_dyn_E": "TBD", - "allnps_nps": "TBD", - "allnps_passes": "TBD", - "allnps_cycles": "TBD", - "allnps_latency_ms": "TBD", - "allnps_total_P": "TBD", - "allnps_total_E": "TBD", - "allnps_dyn_P": "TBD", - "allnps_dyn_E": "TBD" + "minimal_nps": "17", + "minimal_passes": "1", + "minimal_cycles": "3947210", + "minimal_latency_ms": "9.868", + "minimal_total_P": "178.5", + "minimal_total_E": "1.784", + "minimal_dyn_P": "66.6", + "minimal_dyn_E": "0.666", + "allnps_nps": "27", + "allnps_passes": "1", + "allnps_cycles": "2932072", + "allnps_latency_ms": "7.330", + "allnps_total_P": "201.4", + "allnps_total_E": "1.502", + "allnps_dyn_P": "88.8", + "allnps_dyn_E": "0.662" } diff --git a/akida1/model_zoo/uored_vafcls/docs/ref_benchmark_results_full.png b/akida1/model_zoo/uored_vafcls/docs/ref_benchmark_results_full.png new file mode 100644 index 0000000000000000000000000000000000000000..eab05d6a5a0e710d8509198bdb74271f27312809 GIT binary patch literal 263887 zcmc$`cRbgB|3CU}At?!BrAIr zIro>Y&-FdO^E>DKb#AwF-9Fdnx(aXa_j5cR_w{)BDO|aT@Ihum|Vxb+b+c@<6j{6CON|cOTycT%*b9nfQtnAC?xABawiT8^P zQqt&^lq6o~Y)TGWUbV~HUei-n#?16%cwSVr_UHJxiOIHtjEs|_)eM{1?(IBJ@xT5{ 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G^Zx=dI%(+u literal 0 HcmV?d00001 From 02e7f5658711822907f31bd4df0a9c055e5a398c Mon Sep 17 00:00:00 2001 From: Douglas McLelland Date: Fri, 18 Sep 2026 09:35:36 +0200 Subject: [PATCH 09/15] Naive split results updated for final model --- akida1/model_zoo/uored_vafcls/README.md | 6 +++--- akida1/model_zoo/uored_vafcls/docs/metrics.json | 8 ++++---- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/akida1/model_zoo/uored_vafcls/README.md b/akida1/model_zoo/uored_vafcls/README.md index da4c259..7fbe6f3 100644 --- a/akida1/model_zoo/uored_vafcls/README.md +++ b/akida1/model_zoo/uored_vafcls/README.md @@ -24,11 +24,11 @@ The interesting part of this example is not the model. It is the evaluation: thi Naive segment-level (leaky) - 0.9990 - 0.9958 + 0.9983 0.9963 + 0.9973 335,444 - 38.08% + 39.06% Bearing-level (protocol) diff --git a/akida1/model_zoo/uored_vafcls/docs/metrics.json b/akida1/model_zoo/uored_vafcls/docs/metrics.json index e0f3fe1..c103e27 100644 --- a/akida1/model_zoo/uored_vafcls/docs/metrics.json +++ b/akida1/model_zoo/uored_vafcls/docs/metrics.json @@ -1,9 +1,9 @@ { - "segment_float_auroc": "0.9990", - "segment_qat_auroc": "0.9958", - "segment_akida_auroc": "0.9963", + "segment_float_auroc": "0.9983", + "segment_qat_auroc": "0.9963", + "segment_akida_auroc": "0.9973", "segment_params": "335,444", - "segment_sparsity": "38.08%", + "segment_sparsity": "39.06%", "cv_folds": "100", "cv_seeds": "1", "cv_float_auroc_mean": "0.8950", From b02ff788a5b1fb8995370677352c8c112fa3ce1d Mon Sep 17 00:00:00 2001 From: Douglas McLelland Date: Fri, 18 Sep 2026 15:27:22 +0200 Subject: [PATCH 10/15] Readme finalized, all text tidied --- akida1/model_zoo/uored_vafcls/README.md | 180 ++++++++++-------- .../uored_vafcls/docs/README.md.template | 180 ++++++++++-------- .../uored_vafcls/docs/cv_results.csv | 101 ++++++++++ .../uored_vafcls/docs/cv_seed_std.csv | 76 ++++++++ .../docs/ref_cv_auroc_distribution.png | Bin 43156 -> 44870 bytes .../pretrained_models/akdcnn_uored_vafcls.h5 | 2 +- .../akdcnn_uored_vafcls_qat.fbz | 2 +- .../akdcnn_uored_vafcls_qat.h5 | 2 +- .../uored_vafcls/uored_vafcls_benchmark.py | 4 + .../uored_vafcls_cross_validate.py | 14 +- .../uored_vafcls/uored_vafcls_data.py | 43 +---- .../uored_vafcls/uored_vafcls_model.py | 37 ++-- .../uored_vafcls/uored_vafcls_naive_split.sh | 2 +- .../uored_vafcls/uored_vafcls_train.sh | 13 +- 14 files changed, 417 insertions(+), 239 deletions(-) create mode 100644 akida1/model_zoo/uored_vafcls/docs/cv_results.csv create mode 100644 akida1/model_zoo/uored_vafcls/docs/cv_seed_std.csv diff --git a/akida1/model_zoo/uored_vafcls/README.md b/akida1/model_zoo/uored_vafcls/README.md index 7fbe6f3..506b0b1 100644 --- a/akida1/model_zoo/uored_vafcls/README.md +++ b/akida1/model_zoo/uored_vafcls/README.md @@ -1,14 +1,16 @@ BrainChip Dev Hub -# Bearing Fault Diagnosis (UORED-VAFCLS) +# Bearing Fault Diagnosis from Vibration data (UORED-VAFCLS) Multi-label detection of rolling-element bearing faults — inner race, outer race, ball and cage — from one second of raw 42 kHz accelerometer data. The four faults are independent labels rather than classes, and a healthy bearing is the all-zero vector, not a fifth class. The reported metric is therefore macro AUROC, which needs no decision threshold. -The interesting part of this example is not the model. It is the evaluation: this dataset makes it extremely easy to report a number that is far better than the model deserves, in at least two different ways, and most of what follows is about not doing that. +This example tries to convey two different points: +- how to prepare and evaluate a model for Akida 1, for single-channel time-series data +- the particularities of this and related datasets that mean that special care needs to be taken in preparing the training and evaluation splits, and that comparison across published results is extremely difficult ## Model Card -**The same model, the same recipe, two ways of splitting the data.** The top row is a single train-and-evaluate run under a deliberately naive split; the bottom row is the mean over 100 bearing-disjoint folds × 1 seed. Identical architecture, identical training budget — 720 training and 240 held-out windows either way — so the only thing separating the two rows is whether the split was allowed to leak. **The bottom row is the model's performance.** +**The table reports results for a single model architecture with only the method used to split the training and evaluation data changed.** See below for full details. @@ -23,7 +25,7 @@ The interesting part of this example is not the model. It is the evaluation: thi - + @@ -31,7 +33,7 @@ The interesting part of this example is not the model. It is the evaluation: thi - + @@ -41,12 +43,6 @@ The interesting part of this example is not the model. It is the evaluation: thi
    Naive segment-level (leaky)Segment-level 0.9983 0.9963 0.997339.06%
    Bearing-level (protocol)Bearing-level 0.8950 0.8939 0.8939
    -> **The gap between those two rows is the point of this example.** The naive row is what this dataset hands you if you split it the obvious way — by time within each recording, so the same bearing appears on both sides. It is not a result, and it is not quoted anywhere else in this README. The bearing-level row is lower because it is honest, not because the model is worse. -> -> Read the bearing-level row as a distribution, not a number. Individual folds range from 0.6989 to 0.9892 — a spread many times larger than the difference between any two architectures anyone would want to compare. Quoting one fold's score as a model's performance is quoting noise. See [Why cross-validation is not optional](#why-cross-validation-is-not-optional). - -Distribution of per-fold AUROC - **AKD1500 hardware benchmark** @@ -89,97 +85,103 @@ The interesting part of this example is not the model. It is the evaluation: thi
    -*Measured on the model trained on the bearing-level split at fold 5 — the weights shipped in `pretrained_models/` — which is a fixed, reproducible artefact rather than a performance claim. The architecture, the input shape and the quantization are identical under either split, so these latency, power and mapping figures apply to both rows of the Model Card.* +Measured on the model provided in the `pretrained_models/` folder, trained on the bearing-level split at fold 5. Full model benchmark -`Minimal` mapping uses the fewest neural processors that will hold the model; `AllNPs` spreads it over everything available, which usually trades power for latency. This model is an unusual case: it already saturates the device in `Minimal` mode, so the two mappings are nearly identical. +`Minimal` mapping uses the fewest neural processors (NPs) that will hold the model; `AllNPs` spreads it over the available +NPs without increasing the number of passes: in this case, because the model almost fills the device anyway, there is minimal +difference between these modes. -The model maps entirely to hardware — 1 HRC input convolution, 41 CNP1 convolutional processors, one FNP2 and one FNP3 for the two fully-connected layers, in a single sequence over two passes. One caveat worth knowing before reading the per-layer plot: the `fc` layer's 2432 × 100 weight matrix does not fit in on-chip memory and needs 184 kB of external memory, so unlike most examples here there is real DMA traffic on every inference, and `fc` dominates the latency breakdown. +The model maps entirely to hardware in a single sequence, single pass. Per-layer benchmark -## Why cross-validation is not optional - -This dataset has 60 recordings from **20 physical bearings**. A fold trains on 12 bearings and is tested on the 8 held out. That is a small enough test set that the choice of which bearings are held out matters more than almost anything about the model: - -- The **across-fold** standard deviation is 0.0666, and folds range from 0.6989 to 0.9892 with the architecture, the recipe and the seed all held fixed. -- The **within-fold** standard deviation across random seeds is 0.0186 (measured over 25 folds at 3 seeds each) — comparable to the entire across-fold spread. - -So a single-fold, single-seed AUROC cannot distinguish two architectures on this dataset. For scale: the WDCNN network this model descends from and this model itself differ by about 0.03 in their 100-fold means, which is *less* than one fold's seed-to-seed noise. Any comparison made on one fold is a coin toss dressed up as a result. - -The practical rules this example follows: - -1. **The reported number is the mean over all 100 evaluation folds**, produced by `uored_vafcls_cross_validate.py`. The per-fold results are committed in [`docs/cv_results.csv`](docs/cv_results.csv) so the mean is auditable without re-running anything. -2. **Folds 0–4 are the tuning budget.** Every hyperparameter — epochs, learning rate, the QAT schedule, the input encoding constants — was chosen there. Folds 5–104 are never tuned on. -3. **Single-fold artefacts are labelled as such.** The pretrained weights and every hardware measurement come from fold 5, and are presented as a fixed reference point, not a performance claim. - ## Requirements -See the [requirements section](../../../README.md#requirements) of the top-level README for the environment used throughout this repository. - -This example additionally uses `scikit-learn` (for `roc_auc_score`, the reported metric), `pooch` (dataset download) and `pandas` (results bookkeeping; also `pyarrow` if you cross-check the split against the published fixtures). +See the [requirements section](../../../README.md#requirements) of the top-level README for the environment used +throughout this repository. ## Dataset -**UORED-VAFCLS** — the University of Ottawa Rolling-element Dataset, Vibration and Acoustic Faults under Constant Load and Speed. 60 accelerometer recordings, each 10 s at 42 kHz, from 20 bearings. Every bearing contributes three recordings: one healthy, plus two severities of a single fault mode. +**UORED-VAFCLS** — the University of Ottawa Rolling-element Dataset, Vibration and Acoustic Faults under Constant Load and Speed. + +This dataset comprises 60 accelerometer recordings, each 10 s at 42 kHz, from 20 bearings. Every bearing contributes three recordings: +one healthy, plus two severities of a single fault mode. | | | |---|---| | Recordings | 60 (20 bearings × 3) | | Length | 10 s at 42 kHz (420,000 samples) | -| Model input | 1 s window, framed to 1200 × 35 | +| Model input | 1 s window | | Labels | 4 independent: inner, outer, ball, cage (healthy = all zero) | | Windows per fold | 720 training (random crops), 240 held-out (tiled) | +Sample recordings from the UORED-VAFCLS dataset + Downloaded from [Mendeley Data, doi:10.17632/y2px5tg92h.5](https://data.mendeley.com/datasets/y2px5tg92h/5), licensed **CC BY 4.0** (https://creativecommons.org/licenses/by/4.0/): > Sehri, Maryam; Dumond, Patrick (2023), *"University of Ottawa Rolling-element Dataset – Vibration and Acoustic Faults under Constant Load and Speed conditions (UORED-VAFCLS)"*, Mendeley Data, V5, doi: 10.17632/y2px5tg92h.5 -*Changes made, as the licence requires us to state:* the `Accelerometer` column of the raw CSVs is repacked unmodified into a single float32 `.npz`; the pipeline in this example then removes a per-recording DC offset and rescales each recording to an 8-bit range (see [Input encoding](#input-encoding)). - -The evaluation protocol, and the leakage argument behind it, follow: - -> J. P. Vieira, V. A. Bauler, R. K. Rosa, D. Silva, *"Towards a more realistic evaluation of machine learning models for bearing fault diagnosis"*, Mechanical Systems and Signal Processing 258:114640, 2026. doi:10.1016/j.ymssp.2026.114640 ([arXiv:2509.22267](https://arxiv.org/abs/2509.22267), [code](https://github.com/gama-ufsc/bearing-data-leakage)) - -That work reports 0.9100 ± 0.0553 macro AUROC for a WDCNN on this protocol; a local reimplementation scored 0.9034 ± 0.0528 over the same 100 folds. Those figures are quoted for context only — they were measured on a different input pipeline, and are not comparable line-for-line with the Model Card (see [Input encoding](#input-encoding)). - -## Leakage and the bearing-level split +*Changes made, as the licence requires us to state:* for convenience, we make the data required in this example available +as a single file for download from the BrainChip servers, that is, the `Accelerometer` column of the raw CSVs repacked +unmodified into a single float32 `.npz`. -**In this dataset, fault mode is a property of the bearing.** Bearings 1–5 have inner-race faults, 6–10 outer-race, 11–15 ball, 16–20 cage. No bearing ever exhibits two modes. The consequence is severe: under any split that is not bearing-disjoint, a model that simply re-identifies *which bearing it is listening to* scores near-perfectly without learning anything about faults at all. Near-duplicate slices of the same 10 s recording on both sides of the split are enough to make that trivial. +## The Problem of Realistic Evaluation of Bearing Fault Diagnosis Models -The split here partitions on `bearing_id` **before any windowing**, holding out 2 bearings from each of the 4 fault modes — 8 of 20 bearings, and all three of each held-out bearing's recordings together. Enumerating every such choice gives C(5,2)⁴ = 10,000 folds, shuffled once with a fixed seed and indexed by fold number, so fold *N* here is fold *N* in the reference implementation. +This example draws very directly from the following article, which itself builds on a number of related publications that establish a +widespread methodological problem in the evaluation of machine learning approaches to the key datasets in the diagnosis of bearing faults +from vibration data (UORED-VAFCLS used here, but even more strikingly in the key datasets in the domain, CWRU and Paderborn): -Verify it yourself, over all 105 tuning and evaluation folds: - -```bash -python uored_vafcls_data.py --check-splits -``` - -**There is a second, subtler leak in the raw signals.** Amplitude is partly a recording fingerprint rather than a fault signature. All ten inner-race recordings sit at a mean of 46.6–55.6 while every outer-race and ball recording sits at 0.9–3.1. Used alone, with no model at all, the per-recording *mean* scores 0.776 AUROC on the inner-race label and 0.744 on cage; the per-recording *standard deviation* scores 0.798 on inner-race. A pipeline that feeds unnormalised signals lets the network read a large part of the answer off the DC offset. The input encoding below removes it — which lowers the reported score, and should. - -## Input encoding - -Akida takes 8-bit inputs. That is not merely an implementation detail here, because per-recording signal standard deviation spans **69×** across this dataset (1.87 to 129.4) and 8 bits is 256 levels. A single global scale wide enough for the loudest recording puts the quietest one below a single quantisation level. Per-recording scaling is *forced* by the hardware; it is not a modelling choice. - -So each window is encoded from its own recording's statistics — the centre and the 99.9th percentile of absolute deviation, both computed once over the whole 10 s trace: - -| Headroom | uint8 levels per signal σ (min / median) | clipped at gain 1.0 | at 1.7 | at 2.4 | -|---|---|---|---|---| -| 1.0 | 13.4 / 30.3 | 0.0997% | 4.31% | 11.65% | -| **2.0** (used) | **6.7 / 15.1** | **0.0003%** | **0.026%** | **0.56%** | -| 3.0 | 4.5 / 10.1 | ~0% | 0.001% | 0.016% | - -Regenerate that table for your own cache with `python uored_vafcls_data.py --report-encoding`. +> J. P. Vieira, V. A. Bauler, R. K. Rosa, D. Silva, *"Towards a more realistic evaluation of machine learning models for bearing fault diagnosis"*, Mechanical Systems and Signal Processing 258:114640, 2026. doi:10.1016/j.ymssp.2026.114640 ([arXiv:2509.22267](https://arxiv.org/abs/2509.22267), [code](https://github.com/gama-ufsc/bearing-data-leakage)) -Two details that are easy to get wrong: +The abstract from that article sets the context clearly: *"While recent advances in machine learning (ML), particularly +deep learning, have shown strong performance in controlled settings, many studies fail to +generalize to real-world applications due to methodological flaws, most notably data leakage. +This paper investigates the issue of data leakage in vibration-based bearing fault diagnosis and +its impact on model evaluation. We demonstrate that common dataset partitioning strategies, +such as segment-wise and condition-wise splits, introduce spurious correlations that inflate +performance metrics."* + +The risk is specific and easy to walk into. These datasets are built from a small number of physical bearings, each recorded +over a short, uninterrupted run under a single load and speed, and the fault mode is a property of the bearing rather than of +the signal: here, bearings 1–5 carry inner-race faults, 6–10 outer, 11–15 ball, 16–20 cage. Any split that lets windows from +one recording — or from one bearing — fall on both sides of the train/test boundary therefore hands the model a shortcut. It +can re-identify the recording from its noise floor, its mounting resonances or its running speed and read the label straight +off that identity, without learning anything transferable about bearing faults at all. Segment-wise splits (cutting each +recording in time) and condition-wise splits (holding out load or speed settings while keeping the same bearings) both leak in +exactly this way, and both are common in the published literature. The consequence is that near-perfect headline accuracies — +99%+ is routine on CWRU — measure the ease of recognising a recording, not the ability to diagnose an unseen bearing, and they +collapse as soon as the model meets hardware it has not been recorded on. It also makes cross-paper comparison largely +meaningless: two results on the same dataset are usually not measuring the same thing. + +The reference citation above sets out a rigorous protocol for each of the datasets explored. The UORED-VAFCLS dataset used here +has 60 recordings from **20 physical bearings**. A fold trains on 12 bearings and is tested on the 8 held out, the +key point being that an individual physical bearing never appears in both train and test data (even data from separate recordings +is disallowed, e.g. if the 'healthy' recording for bearing 0 is put in the train data, then the 'fault' recordings for that bearing +must not be in the test split). You can consult the details of the data split preparation in the relevant script, `uored_vafcls_data.py`. + +That rigorous data split has knock-on consequences: the data splits are small enough that the choice of which bearings are held out +matters more than almost anything about the model: + +- The **across-fold** standard deviation is 0.0666, and folds range from 0.6989 to 0.9892 + with the architecture, the recipe and the seed all held fixed. See the plot of below showing performance across folds. +- The **within-fold** standard deviation across random seeds is 0.0186 (measured over 25 folds at 3 + seeds each). -- **The statistics are per recording, never per window.** A window-dependent scale — min-max normalisation, say — is scale-invariant, which would silently turn the training pipeline's random gain augmentation into a no-op. -- **The gain is applied before encoding**, to the float waveform, so it survives the rescale. The zero point sits at mid-scale, so the ~8% of gains that come out negative are a genuine polarity inversion rather than a clamp. +Distribution of per-fold AUROC -The inverse lives *inside* the model, as a `Rescaling` layer, so full-precision training, quantization-aware tuning and Akida inference all see identical values; `cnn2snn` folds it into the first convolution at conversion. +So a single-fold, single-seed AUROC cannot distinguish two architectures on this dataset. It is absolutely necessary to run multi-fold +cross-validation to have anything approaching an accurate evaluation. For the bearing-wise split, this example follows the protocol +set out by the reference paper above: +1. **The reported number is the mean over all 100 evaluation folds**, produced by `uored_vafcls_cross_validate.py`. The per-fold results are committed in [`docs/cv_results.csv`](docs/cv_results.csv) so the mean is auditable without re-running anything. +2. **Folds 0–4 are the tuning budget.** Every hyperparameter — epochs, learning rate, the QAT schedule, the input encoding constants — was chosen there. Folds 5–104 are never tuned on. -**This is a deviation from the source pipeline, and it costs accuracy.** The reference implementation applied no normalisation at all and scored 0.9317 ± 0.0436 over these same 100 folds. That number is *not* in the Model Card and could not honestly be put there: it was measured on raw float inputs that still contained the amplitude confound described above. The figures in the Model Card were all re-measured with this encoding. What mitigates the change is that the protocol's own augmentation is a random per-window rescale with σ = 0.7 — the model is *explicitly* trained to be amplitude-invariant, so a fixed per-recording rescale falls inside the augmentation's own hypothesis class. +### The Comparison: Segment-level Split +To demonstrate the importance of this rigorous leakage-free split (and, admittedly, to show that the model developed here for +Akida is just as good as other published models, that the issue is on the data side, not the model) we present results for +a segment-level split (i.e. every 10 second recording split to 6 seconds training, 4 seconds test data). Sure enough, the +model achieves **>99.5% AUROC**, and that without any further tuning for that version of the task. ## Dataset setup @@ -189,40 +191,47 @@ The prepared cache downloads automatically on first use, to `--data` (default `. python uored_vafcls_data.py -d ./data/uored_vafcls ``` -If you already hold the raw dataset, rebuild the cache from it instead. Download `1_CSV_Raw_Data_Files (.csv)` from the Mendeley record and point at the directory containing the five `1_Healthy` … `5_Cage_Faults` subfolders: +If you prefer a different data location to the default (e.g. because your system has a dedicated data drive), you may find it +easy to set up a symbolic link from the default location. That way you can avoid passing the data path argument to all of the +scripts: ```bash -python uored_vafcls_data.py --prepare-raw /path/to/1_CSV_Raw_Data_Files +ln -s /path/to/shared/uored_vafcls ./data/uored_vafcls ``` -To share one copy between checkouts, symlink it: +If you already hold the raw dataset, rebuild the cache from it instead. Download `1_CSV_Raw_Data_Files (.csv)` from the +Mendeley record and point at the directory containing the five `1_Healthy` … `5_Cage_Faults` subfolders: ```bash -ln -s /path/to/shared/uored_vafcls ./data/uored_vafcls +python uored_vafcls_data.py --prepare-raw /path/to/1_CSV_Raw_Data_Files ``` + ## Pipeline | Stage | Description | |---|---| | Full-precision training | 30 epochs, Adam, batch 120 (6 steps/epoch), cosine decay with 5% warmup to a peak LR of 2e-4 | | Post-training quantization | `cnn2snn quantize` reduces to 4-bit weights and activations (8-bit input) | -| Quantization-aware tuning | 30 epochs at a peak LR of 2e-4, recovering part of the quantization loss | +| Quantization-aware tuning | 10 epochs at a peak LR of 5e-5, recovering almost all of the quantization loss | | Conversion to Akida | `cnn2snn convert` produces the `.fbz` model that runs on hardware | -Two notes on the recipe, both settled on folds 0–4: - -- **The learning-rate schedule spans the real step count.** The reference implementation hard-coded it and undershot by a sixth, leaving the last few epochs at a floor of zero — effectively training for fewer epochs than it appeared to. Here `total_steps` is derived from the dataset, so it cannot drift if the batch size or window count changes. -- **The QAT budget is larger than this repository's usual 2 epochs.** At 6 steps per epoch, 2 epochs is 12 optimizer steps, which recovers nothing. Even so, 4-bit quantization costs real accuracy on this architecture — compare the float and Akida rows of the Model Card. That cost is a property worth reporting, not a bug to tune away. - ## Reference Models The trained models in `pretrained_models/` are stored with Git LFS. See the [trained models section](../../../README.md#trained-models) of the top-level README if they arrive as text pointer files rather than real weights. +These models are necessarily trained on a specific fold. For that, we've selected fold 42, because performance is close to the 100-fold cross-validation value +(obviously, that in itself doesn't make the single-fold accuracy value any more meaningful; rather we hope to avoid any misunderstandings about performance +if a reader is not following the detail of this example). In any case, the pretrained model is only used for benchmarking on hardware which, since the architecture +is constant, should be more or less constant across folds (will vary only to the extent that learned sparsity within the model varies across training runs). + ## Usage ### Notebook +> ⚠️ **Work in progress — the notebooks are not included in this release yet.** The section below describes what they will +> cover; until they land, use the scripts described under [Script](#script). The links will not resolve. + [`uored_vafcls_notebook_training.ipynb`](uored_vafcls_notebook_training.ipynb) walks through the whole pipeline on a single fold: the dataset and its two leakage traps, how a 1 s waveform becomes a framed uint8 tensor, training, quantization, tuning and conversion. It also loads `docs/cv_results.csv` to show the fold distribution without needing a two-hour run. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Brainchip-Inc/brainchip_devhub/blob/main/akida1/model_zoo/uored_vafcls/uored_vafcls_notebook_training.ipynb) @@ -249,11 +258,16 @@ bash uored_vafcls_train.sh [DATADIR] [FOLD] [SEED] python uored_vafcls_cross_validate.py --save-metrics ``` -It runs the whole chain on each of the 100 evaluation folds, appending to `docs/cv_results.csv` after every fold, and takes roughly 30–60 minutes. It resumes by default, so an interrupted run picks up where it stopped. Add `--skip-akida` for a roughly 4× faster float-only sweep when comparing recipes, and `--first-fold`/`--last-fold` to split the work across machines. +It runs the whole chain on each of the 100 evaluation folds, appending to `docs/cv_results.csv` after every fold, and takes roughly 30–60 minutes. +It resumes by default, so an interrupted run picks up where it stopped. Add `--skip-akida` for a roughly 4× faster float-only sweep when comparing +recipes, and `--first-fold`/`--last-fold` to split the work across machines, or to limit to the first 5 folds (0-4) if working on tuning the model +or pipeline (remember, folds 5-104 are for the final evaluation only). ## Contributing and Maintenance -`README.md` in this folder is **generated** — edit [`docs/README.md.template`](docs/README.md.template), never `README.md` directly. The performance tables are filled from `docs/metrics.json`, which is written by the `--save-metrics` flags: +`README.md` in this folder is **generated** — edit [`docs/README.md.template`](docs/README.md.template), never `README.md` directly. The performance +tables are filled from `docs/metrics.json`, which is written by the `--save-metrics` flags (if updating results following changes to the model or +pipeline, remember to delete the `.csv` files first, or use the `--no-resume` argument on the cross-validation runs): ```bash # Bearing-level split (the protocol) - the Model Card's bottom row. diff --git a/akida1/model_zoo/uored_vafcls/docs/README.md.template b/akida1/model_zoo/uored_vafcls/docs/README.md.template index 4552552..26a46b4 100644 --- a/akida1/model_zoo/uored_vafcls/docs/README.md.template +++ b/akida1/model_zoo/uored_vafcls/docs/README.md.template @@ -1,14 +1,16 @@ BrainChip Dev Hub -# Bearing Fault Diagnosis (UORED-VAFCLS) +# Bearing Fault Diagnosis from Vibration data (UORED-VAFCLS) Multi-label detection of rolling-element bearing faults — inner race, outer race, ball and cage — from one second of raw 42 kHz accelerometer data. The four faults are independent labels rather than classes, and a healthy bearing is the all-zero vector, not a fifth class. The reported metric is therefore macro AUROC, which needs no decision threshold. -The interesting part of this example is not the model. It is the evaluation: this dataset makes it extremely easy to report a number that is far better than the model deserves, in at least two different ways, and most of what follows is about not doing that. +This example tries to convey two different points: +- how to prepare and evaluate a model for Akida 1, for single-channel time-series data +- the particularities of this and related datasets that mean that special care needs to be taken in preparing the training and evaluation splits, and that comparison across published results is extremely difficult ## Model Card -**The same model, the same recipe, two ways of splitting the data.** The top row is a single train-and-evaluate run under a deliberately naive split; the bottom row is the mean over {cv_folds} bearing-disjoint folds × {cv_seeds} seed. Identical architecture, identical training budget — 720 training and 240 held-out windows either way — so the only thing separating the two rows is whether the split was allowed to leak. **The bottom row is the model's performance.** +**The table reports results for a single model architecture with only the method used to split the training and evaluation data changed.** See below for full details. @@ -23,7 +25,7 @@ The interesting part of this example is not the model. It is the evaluation: thi - + @@ -31,7 +33,7 @@ The interesting part of this example is not the model. It is the evaluation: thi - + @@ -41,12 +43,6 @@ The interesting part of this example is not the model. It is the evaluation: thi
    Naive segment-level (leaky)Segment-level {segment_float_auroc} {segment_qat_auroc} {segment_akida_auroc}{segment_sparsity}
    Bearing-level (protocol)Bearing-level {cv_float_auroc_mean} {cv_qat_auroc_mean} {cv_akida_auroc_mean}
    -> **The gap between those two rows is the point of this example.** The naive row is what this dataset hands you if you split it the obvious way — by time within each recording, so the same bearing appears on both sides. It is not a result, and it is not quoted anywhere else in this README. The bearing-level row is lower because it is honest, not because the model is worse. -> -> Read the bearing-level row as a distribution, not a number. Individual folds range from {cv_akida_auroc_min} to {cv_akida_auroc_max} — a spread many times larger than the difference between any two architectures anyone would want to compare. Quoting one fold's score as a model's performance is quoting noise. See [Why cross-validation is not optional](#why-cross-validation-is-not-optional). - -Distribution of per-fold AUROC - **AKD1500 hardware benchmark** @@ -89,97 +85,103 @@ The interesting part of this example is not the model. It is the evaluation: thi
    -*Measured on the model trained on the bearing-level split at fold {fixed_fold} — the weights shipped in `pretrained_models/` — which is a fixed, reproducible artefact rather than a performance claim. The architecture, the input shape and the quantization are identical under either split, so these latency, power and mapping figures apply to both rows of the Model Card.* +Measured on the model provided in the `pretrained_models/` folder, trained on the bearing-level split at fold {fixed_fold}. Full model benchmark -`Minimal` mapping uses the fewest neural processors that will hold the model; `AllNPs` spreads it over everything available, which usually trades power for latency. This model is an unusual case: it already saturates the device in `Minimal` mode, so the two mappings are nearly identical. +`Minimal` mapping uses the fewest neural processors (NPs) that will hold the model; `AllNPs` spreads it over the available +NPs without increasing the number of passes: in this case, because the model almost fills the device anyway, there is minimal +difference between these modes. -The model maps entirely to hardware — 1 HRC input convolution, 41 CNP1 convolutional processors, one FNP2 and one FNP3 for the two fully-connected layers, in a single sequence over two passes. One caveat worth knowing before reading the per-layer plot: the `fc` layer's 2432 × 100 weight matrix does not fit in on-chip memory and needs 184 kB of external memory, so unlike most examples here there is real DMA traffic on every inference, and `fc` dominates the latency breakdown. +The model maps entirely to hardware in a single sequence, single pass. Per-layer benchmark -## Why cross-validation is not optional - -This dataset has 60 recordings from **20 physical bearings**. A fold trains on 12 bearings and is tested on the 8 held out. That is a small enough test set that the choice of which bearings are held out matters more than almost anything about the model: - -- The **across-fold** standard deviation is {cv_akida_auroc_std}, and folds range from {cv_akida_auroc_min} to {cv_akida_auroc_max} with the architecture, the recipe and the seed all held fixed. -- The **within-fold** standard deviation across random seeds is {cv_seed_std} (measured over {cv_seed_std_folds} folds at 3 seeds each) — comparable to the entire across-fold spread. - -So a single-fold, single-seed AUROC cannot distinguish two architectures on this dataset. For scale: the WDCNN network this model descends from and this model itself differ by about 0.03 in their 100-fold means, which is *less* than one fold's seed-to-seed noise. Any comparison made on one fold is a coin toss dressed up as a result. - -The practical rules this example follows: - -1. **The reported number is the mean over all {cv_folds} evaluation folds**, produced by `uored_vafcls_cross_validate.py`. The per-fold results are committed in [`docs/cv_results.csv`](docs/cv_results.csv) so the mean is auditable without re-running anything. -2. **Folds 0–4 are the tuning budget.** Every hyperparameter — epochs, learning rate, the QAT schedule, the input encoding constants — was chosen there. Folds 5–104 are never tuned on. -3. **Single-fold artefacts are labelled as such.** The pretrained weights and every hardware measurement come from fold {fixed_fold}, and are presented as a fixed reference point, not a performance claim. - ## Requirements -See the [requirements section](../../../README.md#requirements) of the top-level README for the environment used throughout this repository. - -This example additionally uses `scikit-learn` (for `roc_auc_score`, the reported metric), `pooch` (dataset download) and `pandas` (results bookkeeping; also `pyarrow` if you cross-check the split against the published fixtures). +See the [requirements section](../../../README.md#requirements) of the top-level README for the environment used +throughout this repository. ## Dataset -**UORED-VAFCLS** — the University of Ottawa Rolling-element Dataset, Vibration and Acoustic Faults under Constant Load and Speed. 60 accelerometer recordings, each 10 s at 42 kHz, from 20 bearings. Every bearing contributes three recordings: one healthy, plus two severities of a single fault mode. +**UORED-VAFCLS** — the University of Ottawa Rolling-element Dataset, Vibration and Acoustic Faults under Constant Load and Speed. + +This dataset comprises 60 accelerometer recordings, each 10 s at 42 kHz, from 20 bearings. Every bearing contributes three recordings: +one healthy, plus two severities of a single fault mode. | | | |---|---| | Recordings | 60 (20 bearings × 3) | | Length | 10 s at 42 kHz (420,000 samples) | -| Model input | 1 s window, framed to 1200 × 35 | +| Model input | 1 s window | | Labels | 4 independent: inner, outer, ball, cage (healthy = all zero) | | Windows per fold | 720 training (random crops), 240 held-out (tiled) | +Sample recordings from the UORED-VAFCLS dataset + Downloaded from [Mendeley Data, doi:10.17632/y2px5tg92h.5](https://data.mendeley.com/datasets/y2px5tg92h/5), licensed **CC BY 4.0** (https://creativecommons.org/licenses/by/4.0/): > Sehri, Maryam; Dumond, Patrick (2023), *"University of Ottawa Rolling-element Dataset – Vibration and Acoustic Faults under Constant Load and Speed conditions (UORED-VAFCLS)"*, Mendeley Data, V5, doi: 10.17632/y2px5tg92h.5 -*Changes made, as the licence requires us to state:* the `Accelerometer` column of the raw CSVs is repacked unmodified into a single float32 `.npz`; the pipeline in this example then removes a per-recording DC offset and rescales each recording to an 8-bit range (see [Input encoding](#input-encoding)). - -The evaluation protocol, and the leakage argument behind it, follow: - -> J. P. Vieira, V. A. Bauler, R. K. Rosa, D. Silva, *"Towards a more realistic evaluation of machine learning models for bearing fault diagnosis"*, Mechanical Systems and Signal Processing 258:114640, 2026. doi:10.1016/j.ymssp.2026.114640 ([arXiv:2509.22267](https://arxiv.org/abs/2509.22267), [code](https://github.com/gama-ufsc/bearing-data-leakage)) - -That work reports 0.9100 ± 0.0553 macro AUROC for a WDCNN on this protocol; a local reimplementation scored 0.9034 ± 0.0528 over the same 100 folds. Those figures are quoted for context only — they were measured on a different input pipeline, and are not comparable line-for-line with the Model Card (see [Input encoding](#input-encoding)). - -## Leakage and the bearing-level split +*Changes made, as the licence requires us to state:* for convenience, we make the data required in this example available +as a single file for download from the BrainChip servers, that is, the `Accelerometer` column of the raw CSVs repacked +unmodified into a single float32 `.npz`. -**In this dataset, fault mode is a property of the bearing.** Bearings 1–5 have inner-race faults, 6–10 outer-race, 11–15 ball, 16–20 cage. No bearing ever exhibits two modes. The consequence is severe: under any split that is not bearing-disjoint, a model that simply re-identifies *which bearing it is listening to* scores near-perfectly without learning anything about faults at all. Near-duplicate slices of the same 10 s recording on both sides of the split are enough to make that trivial. +## The Problem of Realistic Evaluation of Bearing Fault Diagnosis Models -The split here partitions on `bearing_id` **before any windowing**, holding out 2 bearings from each of the 4 fault modes — 8 of 20 bearings, and all three of each held-out bearing's recordings together. Enumerating every such choice gives C(5,2)⁴ = 10,000 folds, shuffled once with a fixed seed and indexed by fold number, so fold *N* here is fold *N* in the reference implementation. +This example draws very directly from the following article, which itself builds on a number of related publications that establish a +widespread methodological problem in the evaluation of machine learning approaches to the key datasets in the diagnosis of bearing faults +from vibration data (UORED-VAFCLS used here, but even more strikingly in the key datasets in the domain, CWRU and Paderborn): -Verify it yourself, over all 105 tuning and evaluation folds: - -```bash -python uored_vafcls_data.py --check-splits -``` - -**There is a second, subtler leak in the raw signals.** Amplitude is partly a recording fingerprint rather than a fault signature. All ten inner-race recordings sit at a mean of 46.6–55.6 while every outer-race and ball recording sits at 0.9–3.1. Used alone, with no model at all, the per-recording *mean* scores 0.776 AUROC on the inner-race label and 0.744 on cage; the per-recording *standard deviation* scores 0.798 on inner-race. A pipeline that feeds unnormalised signals lets the network read a large part of the answer off the DC offset. The input encoding below removes it — which lowers the reported score, and should. - -## Input encoding - -Akida takes 8-bit inputs. That is not merely an implementation detail here, because per-recording signal standard deviation spans **69×** across this dataset (1.87 to 129.4) and 8 bits is 256 levels. A single global scale wide enough for the loudest recording puts the quietest one below a single quantisation level. Per-recording scaling is *forced* by the hardware; it is not a modelling choice. - -So each window is encoded from its own recording's statistics — the centre and the 99.9th percentile of absolute deviation, both computed once over the whole 10 s trace: - -| Headroom | uint8 levels per signal σ (min / median) | clipped at gain 1.0 | at 1.7 | at 2.4 | -|---|---|---|---|---| -| 1.0 | 13.4 / 30.3 | 0.0997% | 4.31% | 11.65% | -| **2.0** (used) | **6.7 / 15.1** | **0.0003%** | **0.026%** | **0.56%** | -| 3.0 | 4.5 / 10.1 | ~0% | 0.001% | 0.016% | - -Regenerate that table for your own cache with `python uored_vafcls_data.py --report-encoding`. +> J. P. Vieira, V. A. Bauler, R. K. Rosa, D. Silva, *"Towards a more realistic evaluation of machine learning models for bearing fault diagnosis"*, Mechanical Systems and Signal Processing 258:114640, 2026. doi:10.1016/j.ymssp.2026.114640 ([arXiv:2509.22267](https://arxiv.org/abs/2509.22267), [code](https://github.com/gama-ufsc/bearing-data-leakage)) -Two details that are easy to get wrong: +The abstract from that article sets the context clearly: *"While recent advances in machine learning (ML), particularly +deep learning, have shown strong performance in controlled settings, many studies fail to +generalize to real-world applications due to methodological flaws, most notably data leakage. +This paper investigates the issue of data leakage in vibration-based bearing fault diagnosis and +its impact on model evaluation. We demonstrate that common dataset partitioning strategies, +such as segment-wise and condition-wise splits, introduce spurious correlations that inflate +performance metrics."* + +The risk is specific and easy to walk into. These datasets are built from a small number of physical bearings, each recorded +over a short, uninterrupted run under a single load and speed, and the fault mode is a property of the bearing rather than of +the signal: here, bearings 1–5 carry inner-race faults, 6–10 outer, 11–15 ball, 16–20 cage. Any split that lets windows from +one recording — or from one bearing — fall on both sides of the train/test boundary therefore hands the model a shortcut. It +can re-identify the recording from its noise floor, its mounting resonances or its running speed and read the label straight +off that identity, without learning anything transferable about bearing faults at all. Segment-wise splits (cutting each +recording in time) and condition-wise splits (holding out load or speed settings while keeping the same bearings) both leak in +exactly this way, and both are common in the published literature. The consequence is that near-perfect headline accuracies — +99%+ is routine on CWRU — measure the ease of recognising a recording, not the ability to diagnose an unseen bearing, and they +collapse as soon as the model meets hardware it has not been recorded on. It also makes cross-paper comparison largely +meaningless: two results on the same dataset are usually not measuring the same thing. + +The reference citation above sets out a rigorous protocol for each of the datasets explored. The UORED-VAFCLS dataset used here +has 60 recordings from **20 physical bearings**. A fold trains on 12 bearings and is tested on the 8 held out, the +key point being that an individual physical bearing never appears in both train and test data (even data from separate recordings +is disallowed, e.g. if the 'healthy' recording for bearing 0 is put in the train data, then the 'fault' recordings for that bearing +must not be in the test split). You can consult the details of the data split preparation in the relevant script, `uored_vafcls_data.py`. + +That rigorous data split has knock-on consequences: the data splits are small enough that the choice of which bearings are held out +matters more than almost anything about the model: + +- The **across-fold** standard deviation is {cv_akida_auroc_std}, and folds range from {cv_akida_auroc_min} to {cv_akida_auroc_max} + with the architecture, the recipe and the seed all held fixed. See the plot of below showing performance across folds. +- The **within-fold** standard deviation across random seeds is {cv_seed_std} (measured over {cv_seed_std_folds} folds at 3 + seeds each). -- **The statistics are per recording, never per window.** A window-dependent scale — min-max normalisation, say — is scale-invariant, which would silently turn the training pipeline's random gain augmentation into a no-op. -- **The gain is applied before encoding**, to the float waveform, so it survives the rescale. The zero point sits at mid-scale, so the ~8% of gains that come out negative are a genuine polarity inversion rather than a clamp. +Distribution of per-fold AUROC -The inverse lives *inside* the model, as a `Rescaling` layer, so full-precision training, quantization-aware tuning and Akida inference all see identical values; `cnn2snn` folds it into the first convolution at conversion. +So a single-fold, single-seed AUROC cannot distinguish two architectures on this dataset. It is absolutely necessary to run multi-fold +cross-validation to have anything approaching an accurate evaluation. For the bearing-wise split, this example follows the protocol +set out by the reference paper above: +1. **The reported number is the mean over all {cv_folds} evaluation folds**, produced by `uored_vafcls_cross_validate.py`. The per-fold results are committed in [`docs/cv_results.csv`](docs/cv_results.csv) so the mean is auditable without re-running anything. +2. **Folds 0–4 are the tuning budget.** Every hyperparameter — epochs, learning rate, the QAT schedule, the input encoding constants — was chosen there. Folds 5–104 are never tuned on. -**This is a deviation from the source pipeline, and it costs accuracy.** The reference implementation applied no normalisation at all and scored 0.9317 ± 0.0436 over these same 100 folds. That number is *not* in the Model Card and could not honestly be put there: it was measured on raw float inputs that still contained the amplitude confound described above. The figures in the Model Card were all re-measured with this encoding. What mitigates the change is that the protocol's own augmentation is a random per-window rescale with σ = 0.7 — the model is *explicitly* trained to be amplitude-invariant, so a fixed per-recording rescale falls inside the augmentation's own hypothesis class. +### The Comparison: Segment-level Split +To demonstrate the importance of this rigorous leakage-free split (and, admittedly, to show that the model developed here for +Akida is just as good as other published models, that the issue is on the data side, not the model) we present results for +a segment-level split (i.e. every 10 second recording split to 6 seconds training, 4 seconds test data). Sure enough, the +model achieves **>99.5% AUROC**, and that without any further tuning for that version of the task. ## Dataset setup @@ -189,40 +191,47 @@ The prepared cache downloads automatically on first use, to `--data` (default `. python uored_vafcls_data.py -d ./data/uored_vafcls ``` -If you already hold the raw dataset, rebuild the cache from it instead. Download `1_CSV_Raw_Data_Files (.csv)` from the Mendeley record and point at the directory containing the five `1_Healthy` … `5_Cage_Faults` subfolders: +If you prefer a different data location to the default (e.g. because your system has a dedicated data drive), you may find it +easy to set up a symbolic link from the default location. That way you can avoid passing the data path argument to all of the +scripts: ```bash -python uored_vafcls_data.py --prepare-raw /path/to/1_CSV_Raw_Data_Files +ln -s /path/to/shared/uored_vafcls ./data/uored_vafcls ``` -To share one copy between checkouts, symlink it: +If you already hold the raw dataset, rebuild the cache from it instead. Download `1_CSV_Raw_Data_Files (.csv)` from the +Mendeley record and point at the directory containing the five `1_Healthy` … `5_Cage_Faults` subfolders: ```bash -ln -s /path/to/shared/uored_vafcls ./data/uored_vafcls +python uored_vafcls_data.py --prepare-raw /path/to/1_CSV_Raw_Data_Files ``` + ## Pipeline | Stage | Description | |---|---| | Full-precision training | 30 epochs, Adam, batch 120 (6 steps/epoch), cosine decay with 5% warmup to a peak LR of 2e-4 | | Post-training quantization | `cnn2snn quantize` reduces to 4-bit weights and activations (8-bit input) | -| Quantization-aware tuning | 30 epochs at a peak LR of 2e-4, recovering part of the quantization loss | +| Quantization-aware tuning | 10 epochs at a peak LR of 5e-5, recovering almost all of the quantization loss | | Conversion to Akida | `cnn2snn convert` produces the `.fbz` model that runs on hardware | -Two notes on the recipe, both settled on folds 0–4: - -- **The learning-rate schedule spans the real step count.** The reference implementation hard-coded it and undershot by a sixth, leaving the last few epochs at a floor of zero — effectively training for fewer epochs than it appeared to. Here `total_steps` is derived from the dataset, so it cannot drift if the batch size or window count changes. -- **The QAT budget is larger than this repository's usual 2 epochs.** At 6 steps per epoch, 2 epochs is 12 optimizer steps, which recovers nothing. Even so, 4-bit quantization costs real accuracy on this architecture — compare the float and Akida rows of the Model Card. That cost is a property worth reporting, not a bug to tune away. - ## Reference Models The trained models in `pretrained_models/` are stored with Git LFS. See the [trained models section](../../../README.md#trained-models) of the top-level README if they arrive as text pointer files rather than real weights. +These models are necessarily trained on a specific fold. For that, we've selected fold 42, because performance is close to the 100-fold cross-validation value +(obviously, that in itself doesn't make the single-fold accuracy value any more meaningful; rather we hope to avoid any misunderstandings about performance +if a reader is not following the detail of this example). In any case, the pretrained model is only used for benchmarking on hardware which, since the architecture +is constant, should be more or less constant across folds (will vary only to the extent that learned sparsity within the model varies across training runs). + ## Usage ### Notebook +> ⚠️ **Work in progress — the notebooks are not included in this release yet.** The section below describes what they will +> cover; until they land, use the scripts described under [Script](#script). The links will not resolve. + [`uored_vafcls_notebook_training.ipynb`](uored_vafcls_notebook_training.ipynb) walks through the whole pipeline on a single fold: the dataset and its two leakage traps, how a 1 s waveform becomes a framed uint8 tensor, training, quantization, tuning and conversion. It also loads `docs/cv_results.csv` to show the fold distribution without needing a two-hour run. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Brainchip-Inc/brainchip_devhub/blob/main/akida1/model_zoo/uored_vafcls/uored_vafcls_notebook_training.ipynb) @@ -249,11 +258,16 @@ bash uored_vafcls_train.sh [DATADIR] [FOLD] [SEED] python uored_vafcls_cross_validate.py --save-metrics ``` -It runs the whole chain on each of the 100 evaluation folds, appending to `docs/cv_results.csv` after every fold, and takes roughly 30–60 minutes. It resumes by default, so an interrupted run picks up where it stopped. Add `--skip-akida` for a roughly 4× faster float-only sweep when comparing recipes, and `--first-fold`/`--last-fold` to split the work across machines. +It runs the whole chain on each of the 100 evaluation folds, appending to `docs/cv_results.csv` after every fold, and takes roughly 30–60 minutes. +It resumes by default, so an interrupted run picks up where it stopped. Add `--skip-akida` for a roughly 4× faster float-only sweep when comparing +recipes, and `--first-fold`/`--last-fold` to split the work across machines, or to limit to the first 5 folds (0-4) if working on tuning the model +or pipeline (remember, folds 5-104 are for the final evaluation only). ## Contributing and Maintenance -`README.md` in this folder is **generated** — edit [`docs/README.md.template`](docs/README.md.template), never `README.md` directly. The performance tables are filled from `docs/metrics.json`, which is written by the `--save-metrics` flags: +`README.md` in this folder is **generated** — edit [`docs/README.md.template`](docs/README.md.template), never `README.md` directly. The performance +tables are filled from `docs/metrics.json`, which is written by the `--save-metrics` flags (if updating results following changes to the model or +pipeline, remember to delete the `.csv` files first, or use the `--no-resume` argument on the cross-validation runs): ```bash # Bearing-level split (the protocol) - the Model Card's bottom row. diff --git a/akida1/model_zoo/uored_vafcls/docs/cv_results.csv b/akida1/model_zoo/uored_vafcls/docs/cv_results.csv new file mode 100644 index 0000000..fb095a6 --- /dev/null +++ b/akida1/model_zoo/uored_vafcls/docs/cv_results.csv @@ -0,0 +1,101 @@ +fold,seed,float_auroc,qat_auroc,akida_auroc,float_auroc_inner,float_auroc_outer,float_auroc_ball,float_auroc_cage,akida_auroc_inner,akida_auroc_outer,akida_auroc_ball,akida_auroc_cage,params,n_train_windows,n_test_windows,seconds +5,0,0.979125,0.967891,0.966000,0.999625,0.970875,0.957875,0.988125,0.996313,0.933688,0.935250,0.998750,335444,720,240,23.8 +6,0,0.932438,0.926438,0.926406,0.988750,1.000000,0.995750,0.745250,0.986250,0.999875,0.992750,0.726750,335444,720,240,20.9 +7,0,0.850781,0.859094,0.858578,0.988750,0.894500,0.793875,0.726000,0.989375,0.864688,0.831750,0.748500,335444,720,240,20.4 +8,0,0.882875,0.894594,0.897125,0.914250,0.989500,0.984625,0.643125,0.947187,0.895500,0.988750,0.757062,335444,720,240,20.7 +9,0,0.932313,0.959172,0.960812,0.972250,0.960750,0.997375,0.798875,0.984750,0.951875,0.996125,0.910500,335444,720,240,21.0 +10,0,0.862469,0.881625,0.882266,0.943125,0.897750,0.965750,0.643250,0.949250,0.911375,0.952813,0.715625,335444,720,240,20.1 +11,0,0.843313,0.794547,0.793922,0.920625,0.951500,0.986250,0.514875,0.908563,0.914438,0.981875,0.370812,335444,720,240,20.6 +12,0,0.880750,0.918297,0.919672,0.949000,1.000000,0.918875,0.655125,0.955375,0.998125,0.961688,0.763500,335444,720,240,20.2 +13,0,0.885594,0.867422,0.868688,0.945125,0.957250,0.972375,0.667625,0.935125,0.943375,0.960812,0.635437,335444,720,240,20.2 +14,0,0.934594,0.931766,0.935609,0.993250,0.934125,0.951000,0.860000,0.988125,0.925812,0.948875,0.879625,335444,720,240,20.6 +15,0,0.811937,0.774172,0.773297,0.910250,1.000000,0.925875,0.411625,0.904563,1.000000,0.914500,0.274125,335444,720,240,20.2 +16,0,0.841656,0.829406,0.828625,0.926375,0.994625,0.985000,0.460625,0.931812,0.988000,0.982625,0.412062,335444,720,240,20.2 +17,0,0.877969,0.866922,0.865719,0.927625,0.982750,0.969375,0.632125,0.937125,0.944750,0.991875,0.589125,335444,720,240,20.6 +18,0,0.849938,0.813516,0.819438,0.928250,1.000000,0.999250,0.472250,0.937937,0.998500,0.985750,0.355563,335444,720,240,20.2 +19,0,0.913594,0.888219,0.896406,0.990875,0.995375,0.933125,0.735000,0.990438,0.995500,0.889437,0.710250,335444,720,240,20.1 +20,0,0.955844,0.973375,0.976031,0.999375,0.933750,0.958125,0.932125,0.999250,0.947063,0.971625,0.986187,335444,720,240,20.7 +21,0,0.963094,0.951156,0.950563,0.995750,0.995125,0.985500,0.876000,0.994000,0.992563,0.991000,0.824688,335444,720,240,20.3 +22,0,0.887844,0.919125,0.919766,0.990500,0.901250,0.881000,0.778625,0.990250,0.952750,0.871125,0.864938,335444,720,240,20.8 +23,0,0.876187,0.904187,0.899844,0.939625,1.000000,0.978125,0.587000,0.940562,0.998750,0.985313,0.674750,335444,720,240,20.2 +24,0,0.938312,0.936922,0.938719,0.986875,1.000000,0.973625,0.792750,0.986000,0.999875,0.985750,0.783250,335444,720,240,20.7 +25,0,0.792750,0.800828,0.798906,0.954250,0.938500,0.902875,0.375375,0.975000,0.956688,0.932000,0.331937,335444,720,240,20.2 +26,0,0.970656,0.981297,0.984141,0.999875,1.000000,0.994375,0.888375,0.999125,0.989500,0.984500,0.963437,335444,720,240,20.2 +27,0,0.895281,0.910016,0.908906,0.988875,1.000000,0.964375,0.627875,0.989187,0.996875,0.963000,0.686562,335444,720,240,20.1 +28,0,0.954750,0.933781,0.926016,0.994000,0.997375,0.978875,0.848750,0.994000,0.998500,0.933062,0.778500,335444,720,240,20.9 +29,0,0.948406,0.935656,0.939234,1.000000,0.844875,0.951250,0.997500,0.998125,0.816688,0.945750,0.996375,335444,720,240,20.2 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zn5}VgI1U#e0ffLZbeYwnn~FfHP}5+HH$kv4g+phk(lFvUc}p^sfuJ|vzI{U$yA?ll z52}BB`nzfaYu2r+g!_v(@?YZqPw+Gkj~N6zN0SsQG@ztQUE=I;#}f*EwnZezH zsJrjR52Xl@qw&OX&D+ Date: Fri, 18 Sep 2026 15:40:06 +0200 Subject: [PATCH 11/15] Benchmarks on definitive model. HwPr added to plot (not table) --- akida1/model_zoo/uored_vafcls/README.md | 31 +++++++++-------- .../uored_vafcls/docs/README.md.template | 7 ++-- .../model_zoo/uored_vafcls/docs/metrics.json | 32 +++++++++++------- .../docs/ref_benchmark_results_full.png | Bin 263887 -> 390978 bytes .../docs/ref_benchmark_results_layers.png | Bin 102189 -> 102398 bytes .../uored_vafcls/uored_vafcls_benchmark.py | 2 +- 6 files changed, 43 insertions(+), 29 deletions(-) diff --git a/akida1/model_zoo/uored_vafcls/README.md b/akida1/model_zoo/uored_vafcls/README.md index 506b0b1..ba65a74 100644 --- a/akida1/model_zoo/uored_vafcls/README.md +++ b/akida1/model_zoo/uored_vafcls/README.md @@ -64,23 +64,23 @@ This example tries to convey two different points: Minimal 17 1 - 3947210 - 9.868 - 178.5 - 1.784 - 66.6 - 0.666 + 3930879 + 9.827 + 179.2 + 1.782 + 67.5 + 0.671 AllNPs 27 1 - 2932072 - 7.330 - 201.4 - 1.502 - 88.8 - 0.662 + 2932933 + 7.332 + 201.9 + 1.506 + 89.0 + 0.664 @@ -90,8 +90,11 @@ Measured on the model provided in the `pretrained_models/` folder, trained on th Full model benchmark `Minimal` mapping uses the fewest neural processors (NPs) that will hold the model; `AllNPs` spreads it over the available -NPs without increasing the number of passes: in this case, because the model almost fills the device anyway, there is minimal -difference between these modes. +NPs without increasing the number of passes: in this case, because the model almost fills the device anyway, there is only +a slight difference between these modes. 'HwPr' mode tries to accelerate the model even further, by splitting the model +over more passes (effectively increasing the number of NPs available per layer). That strategy is effective for a +majority of models, but not in this case: that's because the model is small, and fits in a single pass by default; any +gain per-layer is outweighed by the need to reload model weights on every inference. The model maps entirely to hardware in a single sequence, single pass. diff --git a/akida1/model_zoo/uored_vafcls/docs/README.md.template b/akida1/model_zoo/uored_vafcls/docs/README.md.template index 26a46b4..fa35904 100644 --- a/akida1/model_zoo/uored_vafcls/docs/README.md.template +++ b/akida1/model_zoo/uored_vafcls/docs/README.md.template @@ -90,8 +90,11 @@ Measured on the model provided in the `pretrained_models/` folder, trained on th Full model benchmark `Minimal` mapping uses the fewest neural processors (NPs) that will hold the model; `AllNPs` spreads it over the available -NPs without increasing the number of passes: in this case, because the model almost fills the device anyway, there is minimal -difference between these modes. +NPs without increasing the number of passes: in this case, because the model almost fills the device anyway, there is only +a slight difference between these modes. 'HwPr' mode tries to accelerate the model even further, by splitting the model +over more passes (effectively increasing the number of NPs available per layer). That strategy is effective for a +majority of models, but not in this case: that's because the model is small, and fits in a single pass by default; any +gain per-layer is outweighed by the need to reload model weights on every inference. The model maps entirely to hardware in a single sequence, single pass. diff --git a/akida1/model_zoo/uored_vafcls/docs/metrics.json b/akida1/model_zoo/uored_vafcls/docs/metrics.json index c103e27..0b0fc16 100644 --- a/akida1/model_zoo/uored_vafcls/docs/metrics.json +++ b/akida1/model_zoo/uored_vafcls/docs/metrics.json @@ -19,18 +19,26 @@ "fixed_fold": "5", "minimal_nps": "17", "minimal_passes": "1", - "minimal_cycles": "3947210", - "minimal_latency_ms": "9.868", - "minimal_total_P": "178.5", - "minimal_total_E": "1.784", - "minimal_dyn_P": "66.6", - "minimal_dyn_E": "0.666", + "minimal_cycles": "3930879", + "minimal_latency_ms": "9.827", + "minimal_total_P": "179.2", + "minimal_total_E": "1.782", + "minimal_dyn_P": "67.5", + "minimal_dyn_E": "0.671", "allnps_nps": "27", "allnps_passes": "1", - "allnps_cycles": "2932072", - "allnps_latency_ms": "7.330", - "allnps_total_P": "201.4", - "allnps_total_E": "1.502", - "allnps_dyn_P": "88.8", - "allnps_dyn_E": "0.662" + "allnps_cycles": "2932933", + "allnps_latency_ms": "7.332", + "allnps_total_P": "201.9", + "allnps_total_E": "1.506", + "allnps_dyn_P": "89.0", + "allnps_dyn_E": "0.664", + "hwpr_nps": "64", + "hwpr_passes": "3", + "hwpr_cycles": "3063152", + "hwpr_latency_ms": "7.658", + "hwpr_total_P": "216.2", + "hwpr_total_E": "1.791", + "hwpr_dyn_P": "103.1", + "hwpr_dyn_E": "0.854" } diff --git a/akida1/model_zoo/uored_vafcls/docs/ref_benchmark_results_full.png b/akida1/model_zoo/uored_vafcls/docs/ref_benchmark_results_full.png index eab05d6a5a0e710d8509198bdb74271f27312809..4717b1bd92377cf08a05dffba5b09b0fae020786 100644 GIT binary patch literal 390978 zcmcG$c{G-78$EoNq^PJ&p-7`!nUW+!hEgJ#hs;t@GDPMfB_UJ@B|>D%P^e^xOd$$I zDzi$;JdfW#d!OfdpYONc@1NgVpS4;|_i$b3c^=2HkG=PC`JXthxSF1Wo}*jpJ4DN{B4BmmWd4Cc?t%j{cXsc}%JQ!N&J`kb@q69<>RP7@w^sP@cwhhD zKgskW@0^zW@Bh1gy_%l$IMdqy`J)zIer>(N|Nham$nISBKi{=>t(M+-aoPX6K>Rq#^ zhhd$$hxLw3w$xS5F$=1vPj8=_pI^IfozK*i^Qkju0!M%S{ykjIX8hy4VP?(zGit+x zWT)_U-v9i>rv|Lv@y{9nz*DJxS5ALSG~pWJwD{Y=rK 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G^Zx=dI%(+u diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_benchmark.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_benchmark.py index e0f0f67..bdefa61 100644 --- a/akida1/model_zoo/uored_vafcls/uored_vafcls_benchmark.py +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_benchmark.py @@ -114,7 +114,7 @@ # ------------------------------------------------------------------------- # Full-model benchmark (latency + optional power) # ------------------------------------------------------------------------- - map_modes = ['Minimal', 'AllNps'] + map_modes = ['Minimal', 'AllNps', 'HwPr'] POWER_REPEATS = 10 full_results = dict() for mm in map_modes: From b580f06de3c9b90a093cdc7bd74646161833af7e Mon Sep 17 00:00:00 2001 From: dmclellandbc <68535149+dmclellandbc@users.noreply.github.com> Date: Tue, 22 Sep 2026 12:29:43 +0200 Subject: [PATCH 12/15] Apply batched suggestions from code review Docstring suggestions, except README Co-authored-by: sbruers-brainchip --- .../uored_vafcls/uored_vafcls_benchmark.py | 2 +- akida1/model_zoo/uored_vafcls/uored_vafcls_eval.py | 12 ++++++------ akida1/model_zoo/uored_vafcls/uored_vafcls_model.py | 2 +- .../uored_vafcls/uored_vafcls_naive_split.sh | 13 +++++++------ akida1/model_zoo/uored_vafcls/uored_vafcls_train.py | 5 ++--- akida1/model_zoo/uored_vafcls/uored_vafcls_train.sh | 2 +- 6 files changed, 18 insertions(+), 18 deletions(-) diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_benchmark.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_benchmark.py index bdefa61..ae30449 100644 --- a/akida1/model_zoo/uored_vafcls/uored_vafcls_benchmark.py +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_benchmark.py @@ -1,5 +1,5 @@ #!/usr/bin/env python -# Copyright 2025 Brainchip Holdings Ltd. Apache 2.0 License +# Copyright 2026 Brainchip Holdings Ltd. Apache 2.0 License """ UORED-VAFCLS per-layer hardware benchmark. diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_eval.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_eval.py index 8526018..27e9636 100644 --- a/akida1/model_zoo/uored_vafcls/uored_vafcls_eval.py +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_eval.py @@ -1,13 +1,13 @@ #!/usr/bin/env python -# Copyright 2025 Brainchip Holdings Ltd. Apache 2.0 License +# Copyright 2026 Brainchip Holdings Ltd. Apache 2.0 License """ UORED-VAFCLS evaluation for tf_keras or akida models. -The reported metric is macro AUROC over the four fault labels, not accuracy. -Healthy bearings are encoded as the all-zero label vector rather than a fifth -class, so there is no argmax prediction to be right or wrong about: any accuracy -or F1 figure would depend on a decision threshold nobody has chosen. AUROC is -threshold-free and is what the protocol this example follows reports. +The reported metric is macro AUROC (Area Under the Receiver Operating Characteristic Curve) +over the four fault labels. AUROC is threshold free and measures how well the positive and negative +class can be separated by the model. +Note, healthy bearings are encoded here as the all-zero label vector rather than a fifth +class. Scores from a single fold are of limited use on their own - the spread across folds is far wider than the difference between models worth comparing. Use diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_model.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_model.py index 80d80cf..0e6d32c 100644 --- a/akida1/model_zoo/uored_vafcls/uored_vafcls_model.py +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_model.py @@ -1,5 +1,5 @@ #!/usr/bin/env python -# Copyright 2025 Brainchip Holdings Ltd. Apache 2.0 License +# Copyright 2026 Brainchip Holdings Ltd. Apache 2.0 License """ UORED-VAFCLS bearing fault model (akdcnn). diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_naive_split.sh b/akida1/model_zoo/uored_vafcls/uored_vafcls_naive_split.sh index 1e0e366..5b66251 100644 --- a/akida1/model_zoo/uored_vafcls/uored_vafcls_naive_split.sh +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_naive_split.sh @@ -1,9 +1,11 @@ #!/usr/bin/env bash -# Copyright 2025 Brainchip Holdings Ltd. Apache 2.0 License +# Copyright 2026 Brainchip Holdings Ltd. Apache 2.0 License # # The naive comparator: the same pipeline as uored_vafcls_train.sh, run under a # deliberately leaky split. Build, train, quantize, tune, convert, and evaluate -# at each stage. +# This builds the same pipeline as uored_vafcls_train.sh (Build, train, quantize, tune, convert, and evaluate +), run under a +# deliberately leaky data split (see uored_vafcls_data.py docstring for more info). # # Usage: bash uored_vafcls_naive_split.sh [DATADIR] [SEED] # @@ -11,10 +13,9 @@ # exists only to be compared against uored_vafcls_train.sh, which runs the same # model and the same recipe under the bearing-disjoint protocol. # -# The naive split divides each recording by time - the first 60% trains, the -# last 40% tests - so all 20 bearings appear on both sides, and windows seconds -# apart from the same bearing, the same fault and the same run are split across -# train and test. Both splits draw the same total number of training and test windows, +# This naive split approach divides each recording by time: the first 60% is used for training and the +# last 40% is used for testing. This means that all 20 bearings appear in both train and test, leading to data leakage. +# Both splits draw the same total number of training and test windows, # so the difference in score is attributable to the leakage and nothing else. # See the DATA SPLIT PROTOCOL notes in uored_vafcls_data.py. # diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_train.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_train.py index 0a51e5b..df9f5e5 100644 --- a/akida1/model_zoo/uored_vafcls/uored_vafcls_train.py +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_train.py @@ -1,10 +1,9 @@ #!/usr/bin/env python -# Copyright 2025 Brainchip Holdings Ltd. Apache 2.0 License +# Copyright 2026 Brainchip Holdings Ltd. Apache 2.0 License """ UORED-VAFCLS bearing fault training. -The loss is binary cross-entropy over four independent fault labels, not a -softmax over classes: a machine can in principle show more than one fault, and +The loss is binary cross-entropy over four independent fault labels, as a machine can in principle show more than one fault (although it is not the case with the present dataset). Thus a healthy bearing is the all-zero label vector rather than a fifth class. The model emits raw logits, so `from_logits=True` throughout. diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_train.sh b/akida1/model_zoo/uored_vafcls/uored_vafcls_train.sh index 1f750d2..0153ee4 100644 --- a/akida1/model_zoo/uored_vafcls/uored_vafcls_train.sh +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_train.sh @@ -1,5 +1,5 @@ #!/usr/bin/env bash -# Copyright 2025 Brainchip Holdings Ltd. Apache 2.0 License +# Copyright 2026 Brainchip Holdings Ltd. Apache 2.0 License # # Full UORED-VAFCLS pipeline on ONE bearing-disjoint fold: build, train, # quantize, tune, convert, evaluate at each stage, and benchmark on hardware. From 10c3a1a0c5dc28b7ce982200fee998296438c8eb Mon Sep 17 00:00:00 2001 From: dmclelland Date: Tue, 22 Sep 2026 13:56:26 +0200 Subject: [PATCH 13/15] Remaining review comments, except benchmark table --- akida1/model_zoo/uored_vafcls/README.md | 87 +++++++++++-------- .../uored_vafcls/docs/README.md.template | 83 +++++++++++------- .../model_zoo/uored_vafcls/docs/metrics.json | 2 +- .../uored_vafcls/uored_vafcls_benchmark.py | 8 +- .../uored_vafcls_cross_validate.py | 17 ++-- .../uored_vafcls/uored_vafcls_data.py | 74 ++++++---------- .../uored_vafcls/uored_vafcls_eval.py | 2 +- .../uored_vafcls/uored_vafcls_model.py | 8 +- 8 files changed, 145 insertions(+), 136 deletions(-) diff --git a/akida1/model_zoo/uored_vafcls/README.md b/akida1/model_zoo/uored_vafcls/README.md index ba65a74..eda729e 100644 --- a/akida1/model_zoo/uored_vafcls/README.md +++ b/akida1/model_zoo/uored_vafcls/README.md @@ -10,7 +10,7 @@ This example tries to convey two different points: ## Model Card -**The table reports results for a single model architecture with only the method used to split the training and evaluation data changed.** See below for full details. +The table reports results for a **single model architecture**. Only the method used to **split the training and evaluation data** is changed. See below for full details. @@ -85,20 +85,17 @@ This example tries to convey two different points:
      -Measured on the model provided in the `pretrained_models/` folder, trained on the bearing-level split at fold 5. +Measured on the model provided in the `pretrained_models/` folder, trained on the bearing-level split at fold 42. -Full model benchmark +Full model benchmark -`Minimal` mapping uses the fewest neural processors (NPs) that will hold the model; `AllNPs` spreads it over the available -NPs without increasing the number of passes: in this case, because the model almost fills the device anyway, there is only -a slight difference between these modes. 'HwPr' mode tries to accelerate the model even further, by splitting the model -over more passes (effectively increasing the number of NPs available per layer). That strategy is effective for a -majority of models, but not in this case: that's because the model is small, and fits in a single pass by default; any -gain per-layer is outweighed by the need to reload model weights on every inference. - -The model maps entirely to hardware in a single sequence, single pass. - -Per-layer benchmark +The model is small enough to map entirely to hardware in a single sequence, single pass. The actual mapping depends on the +requested mapping mode, as follows. `Minimal` mapping uses the fewest neural processors (NPs) that will hold the model; +`AllNPs` spreads it over the available NPs without increasing the number of passes: in this case, because the model almost +fills the device anyway, there is only a slight difference between these modes. `HwPr` mode tries to accelerate the model +even further, by splitting the model over more passes (effectively increasing the number of NPs available per layer). That +strategy is effective for a majority of models, but not in this case: that's because the model is small, and fits in a single +pass by default; any gain per-layer is outweighed by the need to reload model weights on every inference. ## Requirements @@ -138,13 +135,14 @@ from vibration data (UORED-VAFCLS used here, but even more strikingly in the key > J. P. Vieira, V. A. Bauler, R. K. Rosa, D. Silva, *"Towards a more realistic evaluation of machine learning models for bearing fault diagnosis"*, Mechanical Systems and Signal Processing 258:114640, 2026. doi:10.1016/j.ymssp.2026.114640 ([arXiv:2509.22267](https://arxiv.org/abs/2509.22267), [code](https://github.com/gama-ufsc/bearing-data-leakage)) -The abstract from that article sets the context clearly: *"While recent advances in machine learning (ML), particularly -deep learning, have shown strong performance in controlled settings, many studies fail to -generalize to real-world applications due to methodological flaws, most notably data leakage. -This paper investigates the issue of data leakage in vibration-based bearing fault diagnosis and -its impact on model evaluation. We demonstrate that common dataset partitioning strategies, -such as segment-wise and condition-wise splits, introduce spurious correlations that inflate -performance metrics."* +The abstract from that article sets the context clearly: +> *"While recent advances in machine learning (ML), particularly + deep learning, have shown strong performance in controlled settings, many studies fail to + generalize to real-world applications due to methodological flaws, most notably data leakage. + This paper investigates the issue of data leakage in vibration-based bearing fault diagnosis and + its impact on model evaluation. We demonstrate that common dataset partitioning strategies, + such as segment-wise and condition-wise splits, introduce spurious correlations that inflate + performance metrics."* The risk is specific and easy to walk into. These datasets are built from a small number of physical bearings, each recorded over a short, uninterrupted run under a single load and speed, and the fault mode is a property of the bearing rather than of @@ -165,26 +163,43 @@ is disallowed, e.g. if the 'healthy' recording for bearing 0 is put in the train must not be in the test split). You can consult the details of the data split preparation in the relevant script, `uored_vafcls_data.py`. That rigorous data split has knock-on consequences: the data splits are small enough that the choice of which bearings are held out -matters more than almost anything about the model: +matters more than almost anything about the model, as can be seen from the plot of the distribution of performance across folds +below: - The **across-fold** standard deviation is 0.0666, and folds range from 0.6989 to 0.9892 - with the architecture, the recipe and the seed all held fixed. See the plot of below showing performance across folds. + with the architecture, the recipe and the seed all held fixed. - The **within-fold** standard deviation across random seeds is 0.0186 (measured over 25 folds at 3 seeds each). Distribution of per-fold AUROC +*(Note: Fold 42 is flagged because the model trained on this fold is the one set aside in the `pretrained_models` folder.)* + So a single-fold, single-seed AUROC cannot distinguish two architectures on this dataset. It is absolutely necessary to run multi-fold cross-validation to have anything approaching an accurate evaluation. For the bearing-wise split, this example follows the protocol set out by the reference paper above: 1. **The reported number is the mean over all 100 evaluation folds**, produced by `uored_vafcls_cross_validate.py`. The per-fold results are committed in [`docs/cv_results.csv`](docs/cv_results.csv) so the mean is auditable without re-running anything. -2. **Folds 0–4 are the tuning budget.** Every hyperparameter — epochs, learning rate, the QAT schedule, the input encoding constants — was chosen there. Folds 5–104 are never tuned on. +2. **Folds 0–4** are used for tuning hyperparameters, such as the number of epochs, learning rate, the QAT schedule and the input encoding constants. The remaining **folds (5–104)** are reserved as the test split, and should never be used for tuning. + +### Results for the Segment-level Split -### The Comparison: Segment-level Split -To demonstrate the importance of this rigorous leakage-free split (and, admittedly, to show that the model developed here for -Akida is just as good as other published models, that the issue is on the data side, not the model) we present results for -a segment-level split (i.e. every 10 second recording split to 6 seconds training, 4 seconds test data). Sure enough, the -model achieves **>99.5% AUROC**, and that without any further tuning for that version of the task. +Read on its own, results for the bearing-disjoint score invite the wrong conclusion: that +the model is weak. It is not - the evaluation is honest, and honest numbers on +this dataset are lower than the ones usually published. To make that visible, +we also show results for a segment-level data split (each recording contributes different +time segments to both the train and test splits). Published results very commonly use +this kind of data splitting. + +Both modes yield identical budgets - 720 training windows (6 steps per epoch) +and 240 test windows - so a score difference between them is attributable to the +split and nothing else. Sure enough, the model achieves **>99.5% AUROC**, and without +any further tuning for this version of the task. + +Unlike the bearing-level split, the segment-level split needs no cross-validation. It has no folds +to average over (there is one time cut, the same for every run) and its score +is stable to about 0.001 where the bearing-disjoint folds span 0.04-0.05. One run +is sufficient. You can re-run uored_vafcls_naive_split.sh with a different seed to +confirm that for yourself. ## Dataset setup @@ -251,11 +266,11 @@ Run the full pipeline on one fold — build, train, quantize, tune, convert, eva bash uored_vafcls_train.sh [DATADIR] [FOLD] [SEED] ``` -`FOLD` defaults to 5 and `SEED` to 0. It takes about a minute on a single GPU, plus benchmarking. The nine steps are the standard Akida 1 sequence; see the script for the exact commands. +`FOLD` defaults to 42 and `SEED` to 0. It takes about a minute on a single GPU, plus benchmarking. The nine steps are the standard Akida 1 sequence; see the script for the exact commands. ### Cross-validation -**This is the command that produces the headline number.** +**Use this command to reproduce the reported cross-validation performance reported above.** ```bash python uored_vafcls_cross_validate.py --save-metrics @@ -268,12 +283,14 @@ or pipeline (remember, folds 5-104 are for the final evaluation only). ## Contributing and Maintenance -`README.md` in this folder is **generated** — edit [`docs/README.md.template`](docs/README.md.template), never `README.md` directly. The performance -tables are filled from `docs/metrics.json`, which is written by the `--save-metrics` flags (if updating results following changes to the model or -pipeline, remember to delete the `.csv` files first, or use the `--no-resume` argument on the cross-validation runs): +`README.md` in this folder is **generated** — edit [`docs/README.md.template`](docs/README.md.template), never `README.md` directly. + +The performance tables are filled from `docs/metrics.json`, which is written by the `--save-metrics` flags (if updating results +following changes to the model or pipeline, remember to delete the `.csv` files first, or use the `--no-resume` argument on the +cross-validation runs): ```bash -# Bearing-level split (the protocol) - the Model Card's bottom row. +# Bearing-level split (Model Card's bottom row). # Its AUROCs come from the sweep, never from a single model. python uored_vafcls_cross_validate.py --save-metrics # 30-60 min python uored_vafcls_cross_validate.py --first-fold 5 --last-fold 29 --seeds 3 \ @@ -282,7 +299,7 @@ python uored_vafcls_eval.py -l pretrained_models/akdcnn_uored_vafcls.h5 --save-m python uored_vafcls_eval.py -l pretrained_models/akdcnn_uored_vafcls_qat.fbz --save-metrics # sparsity python uored_vafcls_benchmark.py -l pretrained_models/akdcnn_uored_vafcls_qat.fbz --save-metrics -# Naive segment-level split (the comparator) - the Model Card's top row. +# Naive segment-level split (Model Card's top row). # One model, one run: the split is singular and its score is stable to ~0.001. bash uored_vafcls_naive_split.sh python uored_vafcls_eval.py -l models/akdcnn_uored_vafcls_segment.h5 --split-mode segment --save-metrics diff --git a/akida1/model_zoo/uored_vafcls/docs/README.md.template b/akida1/model_zoo/uored_vafcls/docs/README.md.template index fa35904..135b6fd 100644 --- a/akida1/model_zoo/uored_vafcls/docs/README.md.template +++ b/akida1/model_zoo/uored_vafcls/docs/README.md.template @@ -10,7 +10,7 @@ This example tries to convey two different points: ## Model Card -**The table reports results for a single model architecture with only the method used to split the training and evaluation data changed.** See below for full details. +The table reports results for a **single model architecture**. Only the method used to **split the training and evaluation data** is changed. See below for full details. @@ -87,18 +87,15 @@ This example tries to convey two different points: Measured on the model provided in the `pretrained_models/` folder, trained on the bearing-level split at fold {fixed_fold}. -Full model benchmark +Full model benchmark -`Minimal` mapping uses the fewest neural processors (NPs) that will hold the model; `AllNPs` spreads it over the available -NPs without increasing the number of passes: in this case, because the model almost fills the device anyway, there is only -a slight difference between these modes. 'HwPr' mode tries to accelerate the model even further, by splitting the model -over more passes (effectively increasing the number of NPs available per layer). That strategy is effective for a -majority of models, but not in this case: that's because the model is small, and fits in a single pass by default; any -gain per-layer is outweighed by the need to reload model weights on every inference. - -The model maps entirely to hardware in a single sequence, single pass. - -Per-layer benchmark +The model is small enough to map entirely to hardware in a single sequence, single pass. The actual mapping depends on the +requested mapping mode, as follows. `Minimal` mapping uses the fewest neural processors (NPs) that will hold the model; +`AllNPs` spreads it over the available NPs without increasing the number of passes: in this case, because the model almost +fills the device anyway, there is only a slight difference between these modes. `HwPr` mode tries to accelerate the model +even further, by splitting the model over more passes (effectively increasing the number of NPs available per layer). That +strategy is effective for a majority of models, but not in this case: that's because the model is small, and fits in a single +pass by default; any gain per-layer is outweighed by the need to reload model weights on every inference. ## Requirements @@ -138,13 +135,14 @@ from vibration data (UORED-VAFCLS used here, but even more strikingly in the key > J. P. Vieira, V. A. Bauler, R. K. Rosa, D. Silva, *"Towards a more realistic evaluation of machine learning models for bearing fault diagnosis"*, Mechanical Systems and Signal Processing 258:114640, 2026. doi:10.1016/j.ymssp.2026.114640 ([arXiv:2509.22267](https://arxiv.org/abs/2509.22267), [code](https://github.com/gama-ufsc/bearing-data-leakage)) -The abstract from that article sets the context clearly: *"While recent advances in machine learning (ML), particularly -deep learning, have shown strong performance in controlled settings, many studies fail to -generalize to real-world applications due to methodological flaws, most notably data leakage. -This paper investigates the issue of data leakage in vibration-based bearing fault diagnosis and -its impact on model evaluation. We demonstrate that common dataset partitioning strategies, -such as segment-wise and condition-wise splits, introduce spurious correlations that inflate -performance metrics."* +The abstract from that article sets the context clearly: +> *"While recent advances in machine learning (ML), particularly + deep learning, have shown strong performance in controlled settings, many studies fail to + generalize to real-world applications due to methodological flaws, most notably data leakage. + This paper investigates the issue of data leakage in vibration-based bearing fault diagnosis and + its impact on model evaluation. We demonstrate that common dataset partitioning strategies, + such as segment-wise and condition-wise splits, introduce spurious correlations that inflate + performance metrics."* The risk is specific and easy to walk into. These datasets are built from a small number of physical bearings, each recorded over a short, uninterrupted run under a single load and speed, and the fault mode is a property of the bearing rather than of @@ -165,26 +163,43 @@ is disallowed, e.g. if the 'healthy' recording for bearing 0 is put in the train must not be in the test split). You can consult the details of the data split preparation in the relevant script, `uored_vafcls_data.py`. That rigorous data split has knock-on consequences: the data splits are small enough that the choice of which bearings are held out -matters more than almost anything about the model: +matters more than almost anything about the model, as can be seen from the plot of the distribution of performance across folds +below: - The **across-fold** standard deviation is {cv_akida_auroc_std}, and folds range from {cv_akida_auroc_min} to {cv_akida_auroc_max} - with the architecture, the recipe and the seed all held fixed. See the plot of below showing performance across folds. + with the architecture, the recipe and the seed all held fixed. - The **within-fold** standard deviation across random seeds is {cv_seed_std} (measured over {cv_seed_std_folds} folds at 3 seeds each). Distribution of per-fold AUROC +*(Note: Fold 42 is flagged because the model trained on this fold is the one set aside in the `pretrained_models` folder.)* + So a single-fold, single-seed AUROC cannot distinguish two architectures on this dataset. It is absolutely necessary to run multi-fold cross-validation to have anything approaching an accurate evaluation. For the bearing-wise split, this example follows the protocol set out by the reference paper above: 1. **The reported number is the mean over all {cv_folds} evaluation folds**, produced by `uored_vafcls_cross_validate.py`. The per-fold results are committed in [`docs/cv_results.csv`](docs/cv_results.csv) so the mean is auditable without re-running anything. -2. **Folds 0–4 are the tuning budget.** Every hyperparameter — epochs, learning rate, the QAT schedule, the input encoding constants — was chosen there. Folds 5–104 are never tuned on. +2. **Folds 0–4** are used for tuning hyperparameters, such as the number of epochs, learning rate, the QAT schedule and the input encoding constants. The remaining **folds (5–104)** are reserved as the test split, and should never be used for tuning. + +### Results for the Segment-level Split -### The Comparison: Segment-level Split -To demonstrate the importance of this rigorous leakage-free split (and, admittedly, to show that the model developed here for -Akida is just as good as other published models, that the issue is on the data side, not the model) we present results for -a segment-level split (i.e. every 10 second recording split to 6 seconds training, 4 seconds test data). Sure enough, the -model achieves **>99.5% AUROC**, and that without any further tuning for that version of the task. +Read on its own, results for the bearing-disjoint score invite the wrong conclusion: that +the model is weak. It is not - the evaluation is honest, and honest numbers on +this dataset are lower than the ones usually published. To make that visible, +we also show results for a segment-level data split (each recording contributes different +time segments to both the train and test splits). Published results very commonly use +this kind of data splitting. + +Both modes yield identical budgets - 720 training windows (6 steps per epoch) +and 240 test windows - so a score difference between them is attributable to the +split and nothing else. Sure enough, the model achieves **>99.5% AUROC**, and without +any further tuning for this version of the task. + +Unlike the bearing-level split, the segment-level split needs no cross-validation. It has no folds +to average over (there is one time cut, the same for every run) and its score +is stable to about 0.001 where the bearing-disjoint folds span 0.04-0.05. One run +is sufficient. You can re-run uored_vafcls_naive_split.sh with a different seed to +confirm that for yourself. ## Dataset setup @@ -255,7 +270,7 @@ bash uored_vafcls_train.sh [DATADIR] [FOLD] [SEED] ### Cross-validation -**This is the command that produces the headline number.** +**Use this command to reproduce the reported cross-validation performance reported above.** ```bash python uored_vafcls_cross_validate.py --save-metrics @@ -268,12 +283,14 @@ or pipeline (remember, folds 5-104 are for the final evaluation only). ## Contributing and Maintenance -`README.md` in this folder is **generated** — edit [`docs/README.md.template`](docs/README.md.template), never `README.md` directly. The performance -tables are filled from `docs/metrics.json`, which is written by the `--save-metrics` flags (if updating results following changes to the model or -pipeline, remember to delete the `.csv` files first, or use the `--no-resume` argument on the cross-validation runs): +`README.md` in this folder is **generated** — edit [`docs/README.md.template`](docs/README.md.template), never `README.md` directly. + +The performance tables are filled from `docs/metrics.json`, which is written by the `--save-metrics` flags (if updating results +following changes to the model or pipeline, remember to delete the `.csv` files first, or use the `--no-resume` argument on the +cross-validation runs): ```bash -# Bearing-level split (the protocol) - the Model Card's bottom row. +# Bearing-level split (Model Card's bottom row). # Its AUROCs come from the sweep, never from a single model. python uored_vafcls_cross_validate.py --save-metrics # 30-60 min python uored_vafcls_cross_validate.py --first-fold 5 --last-fold 29 --seeds 3 \ @@ -282,7 +299,7 @@ python uored_vafcls_eval.py -l pretrained_models/akdcnn_uored_vafcls.h5 --save-m python uored_vafcls_eval.py -l pretrained_models/akdcnn_uored_vafcls_qat.fbz --save-metrics # sparsity python uored_vafcls_benchmark.py -l pretrained_models/akdcnn_uored_vafcls_qat.fbz --save-metrics -# Naive segment-level split (the comparator) - the Model Card's top row. +# Naive segment-level split (Model Card's top row). # One model, one run: the split is singular and its score is stable to ~0.001. bash uored_vafcls_naive_split.sh python uored_vafcls_eval.py -l models/akdcnn_uored_vafcls_segment.h5 --split-mode segment --save-metrics diff --git a/akida1/model_zoo/uored_vafcls/docs/metrics.json b/akida1/model_zoo/uored_vafcls/docs/metrics.json index 0b0fc16..25b7e04 100644 --- a/akida1/model_zoo/uored_vafcls/docs/metrics.json +++ b/akida1/model_zoo/uored_vafcls/docs/metrics.json @@ -16,7 +16,7 @@ "cv_seed_std_folds": "25", "params": "335,444", "sparsity": "37.81%", - "fixed_fold": "5", + "fixed_fold": "42", "minimal_nps": "17", "minimal_passes": "1", "minimal_cycles": "3930879", diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_benchmark.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_benchmark.py index ae30449..a5b36ce 100644 --- a/akida1/model_zoo/uored_vafcls/uored_vafcls_benchmark.py +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_benchmark.py @@ -172,14 +172,14 @@ if args.save_metrics: - # The is used to update the stored metrics that are used to generate the + # The save-metrics argument used to update the stored metrics that are used to generate the # performance tables in the README of this folder. # This should only be used for code maintenance, when the model or training # pipeline is updated and a new trained model integrated. # - # Note: 'sparsity' is deliberately NOT written here - uored_vafcls_eval.py - # owns that key, so that the accuracy table can be regenerated without - # hardware and the two scripts never race to set it. + # Note: 'sparsity' is deliberately NOT written here, but is created by + # uored_vafcls_eval.py. This is so that the accuracy table can be regenerated without + # hardware. metrics_path = pathlib.Path(__file__).parent / 'docs' / 'metrics.json' metrics = json.loads(metrics_path.read_text()) if metrics_path.exists() else {} for mm, res in full_results.items(): diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_cross_validate.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_cross_validate.py index a65d5ac..6fc94c8 100644 --- a/akida1/model_zoo/uored_vafcls/uored_vafcls_cross_validate.py +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_cross_validate.py @@ -1,5 +1,5 @@ #!/usr/bin/env python -# Copyright 2025 Brainchip Holdings Ltd. Apache 2.0 License +# Copyright 2026 Brainchip Holdings Ltd. Apache 2.0 License """ UORED-VAFCLS cross-validation sweep. @@ -10,13 +10,13 @@ This is the script that produces the headline number for this example, and the reason it exists is that a single fold does not support a conclusion. There are only 20 bearings; a fold trains on 12 of them and is tested on 8. The AUROC -spread across folds is 0.04-0.05, and individual folds range from about 0.70 to +spread across folds is 0.04-0.07, and individual folds range from about 0.70 to about 0.99 with the model and the seed held fixed. Two architectures differing by a couple of AUROC points cannot be distinguished by one fold, and reporting one fold's score as a model's performance is reporting noise. -Folds 0-4 are the tuning budget - the only folds a hyperparameter may be chosen -on. Folds 5-104 are the 100 evaluation folds, and the mean over them is the +Folds 0-4 are the tuning budget, used to do hyperparameter tuning. +Folds 5-104 are the 100 evaluation folds, and the mean over them is the number to quote. Results accumulate in docs/cv_results.csv, one row per (fold, seed), flushed @@ -25,7 +25,7 @@ Runtime is roughly 30-60 s per fold, so about 1-2 hours for the default 100-fold full chain. --skip-akida drops the quantize/convert/Akida stages and is -about four times faster; use it for recipe sweeps. +about four times faster; use it for recipe sweeps on the first 5 folds. Example ------- @@ -99,7 +99,6 @@ def run_fold(fold, seed=0, data_path='./data/uored_vafcls', epochs=30, started = time.time() model = build_uored_vafcls_model(seed=seed) - # model.summary() train_ds, test_ds = get_data(data_path, model.input_shape[1:], batch_size, fold=fold, seed=seed) @@ -391,7 +390,11 @@ def plot_distribution(rows, fixed_fold, savepath): if args.save_metrics: import json - + # The save-metrics argument used to update the stored metrics that are used to generate the + # performance tables in the README of this folder. + # This should only be used for code maintenance, when the model or training + # pipeline is updated and a new trained model integrated. + # # The published aggregate must describe the whole evaluation range, or # the README would quote a mean over whichever folds happened to run. have = {int(row['fold']) for row in rows} diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_data.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_data.py index 6b4c2e0..fc80032 100644 --- a/akida1/model_zoo/uored_vafcls/uored_vafcls_data.py +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_data.py @@ -1,5 +1,5 @@ #!/usr/bin/env python -# Copyright 2025 Brainchip Holdings Ltd. Apache 2.0 License +# Copyright 2026 Brainchip Holdings Ltd. Apache 2.0 License """ UORED-VAFCLS bearing fault data pipeline. @@ -10,9 +10,9 @@ -> random gain (training only) -> rescale to uint8-range using a single per-dataset scale factor - -> reshape to (1200, 35, 1) + -> reshape to (300, 140, 1) -DATA SPLIT PROTOCOL +BEARING LEVEL DATA SPLIT PROTOCOL The dataset split implemented here is derived from the method proposed by Vieira et al (2026), "Towards a more realistic evaluation of machine learning @@ -27,42 +27,27 @@ plus two severities of a single fault mode - and holding a bearing out removes all three. -Folds are enumerated, not tabulated: every way of holding out 2 bearings from +Folds are enumerated: every way of holding out 2 bearings from each of the 4 fault modes gives C(5,2)^4 = 10,000 folds, shuffled once with a fixed seed and indexed by fold number. Folds 0-4 are the tuning budget; folds -5-104 are the 100 evaluation folds. FIXED_FOLD (5) is the one the pretrained +5-104 are the 100 evaluation folds. FIXED_FOLD (42) is the one the pretrained models and the hardware benchmark use. -A NAIVE COMPARATOR - -Read on its own, the bearing-disjoint score invites the wrong conclusion: that -the model is weak. It is not - the evaluation is honest, and honest numbers on -this dataset are lower than the ones usually published. To make that visible, -`split_mode='segment'` implements the naive split the protocol exists to rule -out, so the two can be run side by side and the *gap* reported. +SEGMENT LEVEL DATA SPLIT The segment split divides each recording by time - the first 60% of every 10 s recording trains, the last 40% tests - and keeps all 60 recordings on both sides. Windows minutes apart from the same bearing, the same fault and the same -run therefore land on both sides. It is the most severe form of leakage in -Vieira et al's taxonomy, and the most commonly committed. - -Both modes yield identical budgets - 720 training windows (6 steps per epoch) -and 240 test windows - so a score difference between them is attributable to the -split and nothing else. 'segment' is for comparison only: it must never be the -source of a published metric, and uored_vafcls_eval.py --save-metrics refuses it. - -Unlike the protocol, the naive split needs no cross-validation. It has no folds -to average over - there is one time cut, the same for every run - and its score -is stable to about 0.001 where the bearing-disjoint folds span 0.04-0.05. One run -is the whole story; re-run uored_vafcls_naive_split.sh with a different seed to -confirm that for yourself. uored_vafcls_cross_validate.py is therefore reserved -for the protocol and has no notion of split mode. - -Labels are multi-label, not one-of-N: [inner, outer, ball, cage], with healthy -encoded as the all-zero vector rather than a fifth class. The reported metric is -macro AUROC (see uored_vafcls_eval.py) - there is no argmax prediction here, so -an accuracy figure would depend on an arbitrary threshold. +run therefore land on both sides of the train/test split. It is the most severe +form of leakage in Vieira et al's taxonomy, and the most commonly committed. + +CLASS LABELS + +Labels are potentially multi-label, [inner, outer, ball, cage], not one-of-N: +a bearing could in principal suffer from multiple faults (although that case +is not represented in any of the relevant datasets). Note that 'healthy' is +not labelled as a separate class, but is encoded as the all-zero vector. The +reported metric is macro AUROC (see uored_vafcls_eval.py). DATA PREPARATION @@ -91,12 +76,14 @@ """ import functools +import glob import os import random from collections import namedtuple from itertools import combinations, product import numpy as np +import pandas as pd import tensorflow as tf # --------------------------------------------------------------------------- @@ -151,12 +138,12 @@ # Windowing # --------------------------------------------------------------------------- WINDOW_LENGTH = 42_000 # 1.0 s -FRAME_WIDTH = 140 # samples per frame; 42000 / 35 = 1200 frames -INPUT_SHAPE = (WINDOW_LENGTH // FRAME_WIDTH, FRAME_WIDTH, 1) # (1200, 35, 1) +FRAME_WIDTH = 140 # samples per frame; 42000 / 140 = 300 frames +INPUT_SHAPE = (WINDOW_LENGTH // FRAME_WIDTH, FRAME_WIDTH, 1) # (300, 140, 1) TRAIN_WINDOWS_PER_RECORDING = 20 # 36 recordings x 20 = 720 windows per epoch EVAL_WINDOWS_PER_RECORDING = 10 # 24 recordings x 10 = 240 windows, tiled -GAIN_STD = 0.7 # random scalar gain, N(1, GAIN_STD) +GAIN_STD = 0.7 # Random augmentation, a scalar gain applied to the signal, N(1, GAIN_STD) BATCH_SIZE = 120 # exact divisor of 720 -> 6 steps per epoch # The segment split trains on all 60 recordings rather than 36, so it needs its @@ -240,11 +227,11 @@ def fold_rows(bearing_id, fold, n_per_mode=N_BEARINGS_PER_FAULT_MODE, # --------------------------------------------------------------------------- -# The naive comparator +# The Segment-Level Split Comparator # --------------------------------------------------------------------------- # A time region is a half-open (start, stop) in samples. Every window drawn for # a split comes from one of these, which is the whole of what separates the two -# modes: 'bearing' draws from the full recording on both sides and separates the +# modes: 'bearing' draws from the full recording and separates the # rows instead, 'segment' shares every row and separates the time. FULL_REGION = (0, SIGNAL_LENGTH) SEGMENT_TRAIN_REGION = (0, SEGMENT_BOUNDARY) @@ -273,9 +260,6 @@ def check_split_mode(split_mode): def split_plan(bearing_id, fold, split_mode=DEFAULT_SPLIT_MODE): """Resolve one split into rows, time regions and a training window budget. - Both modes are described in the module docstring. This is the only place - that branches on the mode; everything downstream consumes the plan. - Args: bearing_id (np.ndarray): per-recording bearing id, shape (N_RECORDINGS,). fold (int): fold index. Ignored by 'segment', which has one split. @@ -420,8 +404,6 @@ def _describe_split(cache, plan, fold): _describe('Train', cache, plan.train_rows, plan.train_region) _describe('Held out', cache, plan.test_rows, plan.test_region) - # The diagnostic that distinguishes the two modes at a glance. A protocol - # split shares nothing; the naive one shares everything. train_bearings = set(cache['bearing_id'][plan.train_rows].tolist()) test_bearings = set(cache['bearing_id'][plan.test_rows].tolist()) shared = train_bearings & test_bearings @@ -457,9 +439,8 @@ def generator(): start = rng.integers(region_start, region_stop - WINDOW_LENGTH) window = np.array(signals[row, start:start + WINDOW_LENGTH], dtype=np.float32) - # Gain is applied to the float waveform + # Random Augmentation: Gain is applied to the float waveform window *= np.float32(rng.normal(1.0, GAIN_STD)) - # window = encode_uint8(window, center[row], scale[row]) window = encode_uint8(window) yield frame_window(window), labels[i] @@ -467,7 +448,7 @@ def generator(): tf.TensorSpec(shape=(NUM_LABELS,), dtype=tf.float32)) return ( tf.data.Dataset.from_generator(generator, output_signature=signature) - # Declared so that len(train_ds) works, which is how the training script + # Cardinality is declared so that len(train_ds) works, which is how the training script # derives steps_per_epoch for the LR schedule. .apply(tf.data.experimental.assert_cardinality(n_items)) .shuffle(n_items, seed=seed, reshuffle_each_iteration=True) @@ -483,7 +464,7 @@ def _eval_arrays(cache, rows, region=FULL_REGION): That is EVAL_WINDOWS_PER_RECORDING (10) over a full recording, or 4 over the segment split's test region and 6 over its training region. - Small enough to materialise (240 x 1200 x 35 uint8 is about 10 MB). + Small enough to materialise (240 x 300 x 140 uint8 is about 10 MB). """ signals = cache['signals'] labels = _labels(cache, rows) @@ -650,9 +631,6 @@ def prepare_raw(raw_dir, data_path=DEFAULT_DATA_PATH): Returns: str: path to the written cache. """ - import glob - - import pandas as pd signals = np.zeros((N_RECORDINGS, SIGNAL_LENGTH), dtype=np.float32) records = [] diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_eval.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_eval.py index 27e9636..d197edf 100644 --- a/akida1/model_zoo/uored_vafcls/uored_vafcls_eval.py +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_eval.py @@ -190,7 +190,7 @@ def evaluate_akida_model(akida_model, dataset): # Persist metrics # --------------------------------------------------------------------------- if args.save_metrics: - # The is used to update the stored metrics that are used to generate the + # The save-metrics argument is used to update the stored metrics that are used to generate the # performance tables in the README of this folder. # This should only be used for code maintenance, when the model or training # pipeline is updated and a new trained model integrated. diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_model.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_model.py index 0e6d32c..65e2df4 100644 --- a/akida1/model_zoo/uored_vafcls/uored_vafcls_model.py +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_model.py @@ -105,13 +105,7 @@ def _conv_block(x, filters, name, kernel=(3, 3), padding='same', pool=True, x = Rescaling(1.0 / 128.0, -1.0, name='rescaling')(inputs) # Stem: a dense convolution with no padding - x = Conv2D(STEM_FILTERS, STEM_KERNEL_SIZE, padding='valid', use_bias=False, - name='stem_conv')(x) - x = BatchNormalization(momentum=BN_MOMENTUM, epsilon=BN_EPSILON, - name='stem_bn')(x) - x = ReLU(max_value=6.0, name='stem_relu')(x) - x = MaxPooling2D((2, 2), strides=(2, 2), padding='valid', - name='stem_pool')(x) + x = _conv_block(x, STEM_FILTERS, name='stem', kernel=STEM_KERNEL_SIZE, padding='valid', pool=True, pool_padding='valid') # Blocks for i, (filters, pool) in enumerate(zip(BLOCK_FILTERS[:-1], BLOCK_POOLING[:-1]), From d5285fc469d8180935eacb0e5656980fe7a26bd6 Mon Sep 17 00:00:00 2001 From: dmclelland Date: Tue, 22 Sep 2026 14:27:43 +0200 Subject: [PATCH 14/15] HwPr row added to Model Card table --- akida1/model_zoo/uored_vafcls/README.md | 11 +++++++++++ akida1/model_zoo/uored_vafcls/docs/README.md.template | 11 +++++++++++ 2 files changed, 22 insertions(+) diff --git a/akida1/model_zoo/uored_vafcls/README.md b/akida1/model_zoo/uored_vafcls/README.md index eda729e..bba36d0 100644 --- a/akida1/model_zoo/uored_vafcls/README.md +++ b/akida1/model_zoo/uored_vafcls/README.md @@ -82,6 +82,17 @@ The table reports results for a **single model architecture**. Only the method u + + + + + + + + + + +
      89.0 0.664
      HwPr64330631527.658216.21.791103.10.854
      diff --git a/akida1/model_zoo/uored_vafcls/docs/README.md.template b/akida1/model_zoo/uored_vafcls/docs/README.md.template index 135b6fd..cc9c625 100644 --- a/akida1/model_zoo/uored_vafcls/docs/README.md.template +++ b/akida1/model_zoo/uored_vafcls/docs/README.md.template @@ -82,6 +82,17 @@ The table reports results for a **single model architecture**. Only the method u {allnps_dyn_P} {allnps_dyn_E} + + HwPr + {hwpr_nps} + {hwpr_passes} + {hwpr_cycles} + {hwpr_latency_ms} + {hwpr_total_P} + {hwpr_total_E} + {hwpr_dyn_P} + {hwpr_dyn_E} + From ae8c91e45763bac7693005f8c3b4fcf906c41dd4 Mon Sep 17 00:00:00 2001 From: dmclelland Date: Tue, 22 Sep 2026 16:00:20 +0200 Subject: [PATCH 15/15] Data file uploaded, sha256 check added --- akida1/model_zoo/uored_vafcls/README.md | 6 ++++-- akida1/model_zoo/uored_vafcls/docs/README.md.template | 6 ++++-- akida1/model_zoo/uored_vafcls/uored_vafcls_data.py | 6 ++---- 3 files changed, 10 insertions(+), 8 deletions(-) diff --git a/akida1/model_zoo/uored_vafcls/README.md b/akida1/model_zoo/uored_vafcls/README.md index bba36d0..421bd50 100644 --- a/akida1/model_zoo/uored_vafcls/README.md +++ b/akida1/model_zoo/uored_vafcls/README.md @@ -135,8 +135,10 @@ Downloaded from [Mendeley Data, doi:10.17632/y2px5tg92h.5](https://data.mendeley > Sehri, Maryam; Dumond, Patrick (2023), *"University of Ottawa Rolling-element Dataset – Vibration and Acoustic Faults under Constant Load and Speed conditions (UORED-VAFCLS)"*, Mendeley Data, V5, doi: 10.17632/y2px5tg92h.5 *Changes made, as the licence requires us to state:* for convenience, we make the data required in this example available -as a single file for download from the BrainChip servers, that is, the `Accelerometer` column of the raw CSVs repacked -unmodified into a single float32 `.npz`. +as a single file for download from the BrainChip servers. No sample values are altered: the `Accelerometer` column of each +raw CSV is repacked verbatim as float32 into a single `.npz`, along with the first row of the `Speed` column as a +per-recording nominal RPM, and per-recording metadata (bearing id, fault type, severity, waveform id and the multi-label +targets) derived from the original filenames and folder structure. ## The Problem of Realistic Evaluation of Bearing Fault Diagnosis Models diff --git a/akida1/model_zoo/uored_vafcls/docs/README.md.template b/akida1/model_zoo/uored_vafcls/docs/README.md.template index cc9c625..be226d4 100644 --- a/akida1/model_zoo/uored_vafcls/docs/README.md.template +++ b/akida1/model_zoo/uored_vafcls/docs/README.md.template @@ -135,8 +135,10 @@ Downloaded from [Mendeley Data, doi:10.17632/y2px5tg92h.5](https://data.mendeley > Sehri, Maryam; Dumond, Patrick (2023), *"University of Ottawa Rolling-element Dataset – Vibration and Acoustic Faults under Constant Load and Speed conditions (UORED-VAFCLS)"*, Mendeley Data, V5, doi: 10.17632/y2px5tg92h.5 *Changes made, as the licence requires us to state:* for convenience, we make the data required in this example available -as a single file for download from the BrainChip servers, that is, the `Accelerometer` column of the raw CSVs repacked -unmodified into a single float32 `.npz`. +as a single file for download from the BrainChip servers. No sample values are altered: the `Accelerometer` column of each +raw CSV is repacked verbatim as float32 into a single `.npz`, along with the first row of the `Speed` column as a +per-recording nominal RPM, and per-recording metadata (bearing id, fault type, severity, waveform id and the multi-label +targets) derived from the original filenames and folder structure. ## The Problem of Realistic Evaluation of Bearing Fault Diagnosis Models diff --git a/akida1/model_zoo/uored_vafcls/uored_vafcls_data.py b/akida1/model_zoo/uored_vafcls/uored_vafcls_data.py index fc80032..9546a3e 100644 --- a/akida1/model_zoo/uored_vafcls/uored_vafcls_data.py +++ b/akida1/model_zoo/uored_vafcls/uored_vafcls_data.py @@ -97,7 +97,8 @@ # not fetchable as a single file; --prepare-raw rebuilds this .npz from them. MIRROR_URL = ('https://data.brainchip.com/dataset-mirror/uored_vafcls/' + CACHE_NAME) -MIRROR_HASH = None # TODO: sha256 once the mirror is uploaded +MIRROR_HASH = ('sha256:4ba82c0c36a38fb2afc1428c46a84264093da8c6084d4e471db' + 'f00c9679c9c98') SAMPLE_RATE = 42_000 # Hz SIGNAL_LENGTH = 420_000 # 10 s per recording @@ -354,9 +355,6 @@ def _download_cache(data_path): """Fetch the prepared .npz from the BrainChip dataset mirror.""" import pooch - if MIRROR_HASH is None: - print('Note: MIRROR_HASH is not set yet, so the download cannot be ' - 'checksum-verified.') os.makedirs(data_path, exist_ok=True) print(f'Downloading the prepared UORED-VAFCLS cache to {data_path} ...') pooch.retrieve(url=MIRROR_URL, known_hash=MIRROR_HASH, path=data_path,