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fix: --export-rvf no longer silently produces a placeholder model (#920)

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The --export-rvf handler ran *before* the --train/--pretrain handlers and
unconditionally wrote placeholder sine-wave weights, then returned. So the
documented `--train --dataset … --export-rvf <path>` workflow
(user-guide.md) short-circuited to a PLACEHOLDER model and never trained —
printing "exported successfully" for a non-functional model. Given the
project's anti-"is it fake" stance, silently emitting a fake model is the
wrong default.

Fix:
- Only emit the placeholder container-format demo when --export-rvf is used
  *standalone* (new `export_emits_placeholder_demo` guard). With
  --train/--pretrain, fall through so the real training pipeline runs and
  exports calibrated weights.
- The standalone path now prints a clear WARNING that it writes a
  container-format demo with placeholder weights — not a trained model —
  pointing to --train / a pretrained encoder (#894).
- Docs: flag --export-rvf as a placeholder demo in the flag table, and fix
  the Docker training example to use --save-rvf (consistent with the
  from-source example) instead of the placeholder --export-rvf.

3 unit tests for the guard. Full crate unit suite: 429 + 117 passed, 0 failed.
This commit is contained in:
rUv
2026-06-03 08:55:36 +02:00
committed by GitHub
parent f5d0e1e69e
commit 0cfd255730
2 changed files with 63 additions and 5 deletions
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docs/user-guide.md
+2 -2
View File
@@ -1048,7 +1048,7 @@ The Rust sensing server binary accepts the following flags:
| `--dataset` | (none) | Path to dataset directory (MM-Fi or Wi-Pose) |
| `--dataset-type` | `mmfi` | Dataset format: `mmfi` or `wipose` |
| `--epochs` | `100` | Training epochs |
| `--export-rvf` | (none) | Export RVF model container and exit |
| `--export-rvf` | (none) | Export a **placeholder** RVF container-format demo and exit — **not a trained model**. For a real model use `--train` (+ `--save-rvf`) or download a pretrained encoder. |
| `--save-rvf` | (none) | Save model state to RVF on shutdown |
| `--model` | (none) | Load a trained `.rvf` model for inference |
| `--load-rvf` | (none) | Load model config from RVF container |
@@ -1359,7 +1359,7 @@ docker run --rm \
-v $(pwd)/output:/output \
--entrypoint /app/sensing-server \
ruvnet/wifi-densepose:latest \
--train --dataset /data --epochs 100 --export-rvf /output/model.rvf
--train --dataset /data --epochs 100 --save-rvf /output/model.rvf
```
The pipeline runs 10 phases: