# Phase 0 EvalMDE Adaptation — Handoff

**Date:** 2026-05-14
**Status:** EvalMDE workspace bootstrapped; main eval script + sbatch still to write.

---

## Goal

Run the 7 MoGe-Phase-0 models on **Infinigen 95 scenes** under the **EvalMDE protocol**
(raw native input, no homography warp), producing **RelNormal + SAWA-H + standard metrics**.

EvalMDE and MoGe are independent workflows. EvalMDE workspace is at `/home/ywan0794/EvalMDE/`.
Model wrappers are *copied* from MoGe (single source of truth still in MoGe/baselines/),
because the wrappers' `infer(image, intrinsics)` API doesn't depend on MoGe's eval pipeline.

---

## What's done

### 1. EvalMDE env (Python 3.10) — built and verified
`evalmde` conda env has: torch 2.7.0+cu126, opencv, scipy, utils3d, pipeline, evalmde package,
bpy 4.0 (Blender python, for textureless-relighting visualization).
Sample run `python compute_metrics_example.py` outputs `sawa_h=1.268, rel_normal=0.390` ✓.

### 2. 7 baselines (model wrappers) — copied from MoGe + verified
`/home/ywan0794/EvalMDE/baselines/`:
- `depth_pro.py`   →  emits `depth_metric` (+ `intrinsics` from FOV head)
- `marigold.py`    →  emits `depth_affine_invariant`   (paper: scale_inv+shift_inv → affine)
- `lotus.py`       →  emits `disparity_affine_invariant`  when `--disparity` set
- `depthmaster.py` →  emits `depth_affine_invariant`
- `ppd.py`         →  emits `depth_affine_invariant`   (training quantile normalization)
- `da3_mono.py`    →  emits `depth_scale_invariant`
- `fe2e.py`        →  emits `depth_affine_invariant`   (Lpred clamped to [0,1])

`MGEBaselineInterface` copied to `/home/ywan0794/EvalMDE/test/baseline.py`.

### 3. EvalMDE-native dataloader skeleton — written
`/home/ywan0794/EvalMDE/scripts/dataloader.py` (`EvalMDELoaderPipeline`):
- Reads `<scene>/rgb.png` + `<scene>/gt_depth.npz` (keys: `depth (H,W)`, `intr (4,) [fx,fy,cx,cy]px`, `valid (H,W) bool`)
- Pixel intrinsics → 3×3 normalized matrix `[fx/W, fy/H, cx/W, cy/H]` (MoGe convention)
- Computes 3D pointmap from depth + native pixel intrinsics
- NaN/invalid pixels replaced with `1.0` (matches `evalmde/utils/depth.py:load_data` convention)
- Returns dict with: `image [3,H,W] float [0,1]`, `depth`, `depth_mask`, `intrinsics (3,3)`,
  `points (H,W,3)`, `is_metric=True`, `_intr_px (4,)` (for EvalMDE metrics raw npz)

### 4. Infinigen download — IN PROGRESS (background)
- Source: Princeton GDrive `1amzb6KyF2USFQ5W4CeYKFCh1F-yOQsmp`
- Target: `/home/ywan0794/EvalMDE/data/infinigen/`
- Log: `/tmp/dl_infinigen.log`
- Estimated 50-100 GB
- Check state: `du -sh /home/ywan0794/EvalMDE/data/infinigen/`

### 5. Production MoGe-protocol eval — independent track, already running
- `sbatch eval_scripts/eval_all_slurm.sh` submitted earlier (job 12110 etc.)
- 5 models pending (Marigold/Lotus/DepthMaster/PPD/FE2E), 2 already done (DA3-Mono/Depth Pro)
- Results in `/home/ywan0794/MoGe/eval_output/<model>_<TS>.json`
- **EvalMDE adaptation is a separate effort, doesn't block production MoGe eval.**

---

## TODO (was 4 items, now 2 remain)

### ✅ TODO-1: Fix baseline imports — SUPERSEDED by sys.path approach in run_inference.py
`EvalMDE/baselines/*.py` still have `from moge.test.baseline import MGEBaselineInterface`.
**Resolved via Option A**: `scripts/run_inference.py` does `sys.path.insert(0, '/home/ywan0794/MoGe')`
so baselines still resolve their interface from MoGe. No sed needed.

### ✅ TODO-2 (inference driver): `scripts/run_inference.py` — WRITTEN
`/home/ywan0794/EvalMDE/scripts/run_inference.py`:
- Click CLI with `--baseline /path/to/baselines/<m>.py --data-root <infinigen> --output-root <out> --model-name <name>`
- Passes remaining click args through to baseline's `load.main(ctx.args)`
- For each scene with `rgb.png + gt_depth.npz`: loads rgb, builds normalized 3×3 K from GT pixel intr,
  calls `baseline.infer_for_evaluation(image, K_norm)`, picks depth in priority order
  (`depth_metric > depth_scale_invariant > depth_affine_invariant > 1/disparity_affine_invariant`),
  writes `<out>/<model>/<scene>/pred_depth.npz` with EvalMDE keys `{depth, intr (4,) px, valid}`
- For pred intrinsics: uses model-predicted intr if present (Depth Pro), else GT intr

### ❗ Original TODO-2 (script/eval.py) was REWORKED into 2 stages: inference + metric.
This is cleaner: inference runs in per-model env, metric runs in evalmde env.

### TODO-3: Write `scripts/compute_metrics.py` (run in evalmde env)
Reads each model's pred_depth.npz + GT gt_depth.npz, computes EvalMDE metrics + standard MDE metrics.

Pseudocode:
```python
import sys, json, click
from pathlib import Path
import numpy as np

from evalmde.utils.depth import load_data
from evalmde.metrics.rel_normal import compute_rel_normal
from evalmde.metrics.sawa_h     import compute_sawa_h

@click.command()
@click.option('--gt-root',   required=True, type=click.Path())  # Infinigen root
@click.option('--pred-root', required=True, type=click.Path())  # output of run_inference.py
@click.option('--model-name', required=True, type=str)
@click.option('--output',    required=True, type=click.Path())
def main(gt_root, pred_root, model_name, output):
    gt_root = Path(gt_root); pred_root = Path(pred_root) / model_name
    scenes = sorted(d.name for d in pred_root.iterdir() if (d / 'pred_depth.npz').exists())

    results = []
    for scene in scenes:
        gt_d,  gt_intr,  gt_v = load_data(gt_root  / scene / 'gt_depth.npz')
        pr_d,  pr_intr,  pr_v = load_data(pred_root / scene / 'pred_depth.npz')

        # SAWA-H aligns internally (affine via least-squares). RelNormal uses surface normals
        # which are invariant to scale but NOT to shift — for affine-invariant preds, the
        # shift will skew normals at far depths. Acceptable caveat in Phase 0; document it.
        sawa  = compute_sawa_h    (pr_d, pr_intr, pr_v, gt_d, gt_intr, gt_v)
        rnorm = compute_rel_normal(pr_d, pr_intr, pr_v, gt_d, gt_intr, gt_v)

        # Standard AbsRel + δ1 after affine alignment (re-implement, ~10 lines):
        mask  = gt_v & pr_v
        gtm, prm = gt_d[mask], pr_d[mask]
        # fit y = a*x + b on (prm, gtm)
        A = np.stack([prm, np.ones_like(prm)], axis=-1)
        a, b = np.linalg.lstsq(A, gtm, rcond=None)[0]
        aligned = pr_d * a + b
        am = aligned[mask]
        abs_rel = np.mean(np.abs(am - gtm) / np.maximum(gtm, 1e-6))
        delta1  = np.mean(np.maximum(am/gtm, gtm/am) < 1.25)

        results.append({'scene': scene, 'sawa_h': float(sawa), 'rel_normal': float(rnorm),
                        'abs_rel': float(abs_rel), 'delta1': float(delta1)})

    # Per-scene + aggregate mean
    summary = {'per_scene': results,
               'mean': {k: float(np.mean([r[k] for r in results])) for k in ['sawa_h','rel_normal','abs_rel','delta1']}}
    json.dump(summary, open(output, 'w'), indent=2)


if __name__ == '__main__':
    main()
```

**Note on alignment**: `compute_sawa_h` aligns internally (via `align_depth_least_square` + `align_affine_lstsq`),
so passing RAW pred (affine-invariant) is correct. `compute_rel_normal` does NOT align — its
inputs should be in a comparable depth scale. For Phase 0 simplicity, pass raw pred; document
the affine-shift caveat in the analysis. For stricter eval, pre-affine-align before RelNormal.

### TODO-4: Scene list / config
Once Infinigen download succeeds (currently blocked, see issue below), `run_inference.py`
auto-discovers all scene dirs under `--data-root`. If a subset is wanted, write
`scenes.txt` and add filtering in run_inference.py (~3 lines).

### TODO-5: sbatch `eval_scripts/eval_evalmde_all_slurm.sh`
Same pattern as MoGe's `sanity_all_slurm.sh`: single sbatch, single H100, serial per-model.
For each of 7 models: `conda activate <env>; python scripts/run_inference.py --baseline baselines/<m>.py ...`
Then after all 7 inferences done: `conda activate evalmde; for m in ...; do python scripts/compute_metrics.py --model-name $m ...; done`

Each per-model env needs `evalmde` pip-installed so it can `from evalmde.metrics...` — actually
**no, this is wrong**: per-model envs only run inference (which needs torch + model wrapper deps,
no evalmde). Only the metric-aggregation stage runs in evalmde env. So envs need no extra install.

### TODO-3: Scene list / config
Once Infinigen download finishes, inspect actual layout:
```bash
ls /home/ywan0794/EvalMDE/data/infinigen/ | head -20
```
If scenes are `scene_001/`, `scene_002/`, ...: dataloader auto-discovers them.
If grouped under sub-folders or different naming: may need a manual `scenes.txt` split file.

### TODO-4: sbatch `EvalMDE/eval_scripts/eval_evalmde_all_slurm.sh`
Mirror MoGe's `sanity_all_slurm.sh` structure:
- Single sbatch, single H100, serial per-model
- For each model: activate model's conda env, run `python scripts/eval.py --baseline baselines/<m>.py --data-root data/infinigen --output results/<m>.json`
- After all inference done, optionally re-aggregate in evalmde env for cross-model summary

Per-model env mapping same as MoGe:
| model | env |
|---|---|
| depth_pro | depth-pro |
| marigold | marigold |
| lotus | lotus |
| depthmaster | depthmaster |
| ppd | ppd |
| da3_mono | da3 |
| fe2e | fe2e |

Plus: each env needs `evalmde` package installed (`pip install -e /home/ywan0794/EvalMDE`)
so `from evalmde.metrics.* import compute_rel_normal, compute_sawa_h` works inside model envs.

---

## Paper-canonical inference parameters (locked, confirmed against each repo)

| Model | Args | Source |
|---|---|---|
| Depth Pro | `--precision fp32` | `create_model_and_transforms()` default |
| Marigold | v1-1 + `--denoise_steps 4 --ensemble_size 1` | (user decision: balanced speed) |
| Lotus | g-v2-1-disparity + `--mode generation --disparity --timestep 999 --fp16 --seed 42` | `Lotus/eval.sh` |
| DepthMaster | `--processing_res 768` | `DepthMaster/scripts/infer.sh` |
| PPD | `--semantics_model MoGe2 --semantics_pth checkpoints/moge2.pt --model_pth checkpoints/ppd_moge.pth --sampling_steps 4` | `PPD/ppd/configs/eval.yaml` |
| DA3-Mono | `--hf_id depth-anything/DA3MONO-LARGE` | DA3 README |
| FE2E | `--prompt_type empty --single_denoise --cfg_guidance 6.0 --size_level 768` | `FE2E/README.md` eval block |

---

## Key insights to preserve

1. **EvalMDE protocol uses raw native input, no homography warp.** MoGe's eval pipeline
   does aggressive canonical-view warping (`dataloader.py:_process_instance:119-180`).
   That is MoGe-paper-specific; EvalMDE explicitly uses raw inputs (see `compute_metrics_example.py`).

2. **Output key contract** (per MGEBaselineInterface):
   - `depth_metric` → metric depth in meters (Depth Pro)
   - `depth_scale_invariant` → scale-invariant relative depth (DA3-Mono)
   - `depth_affine_invariant` → affine-invariant depth (Marigold/DepthMaster/PPD/FE2E)
   - `disparity_affine_invariant` → affine-invariant disparity (Lotus disparity ckpts)

3. **Pre-alignment for SAWA-H/RelNormal**: SAWA-H itself does affine alignment internally
   (`evalmde/metrics/sawa_h.py:compute_sawa_h` uses `align_depth_least_square` + `align_affine_lstsq`),
   so you can pass RAW pred depth to SAWA-H. RelNormal works on normals which are
   scale-invariant in the limit, but **shift in depth space WILL skew normals at far depths** —
   so for affine-invariant pred models, do an affine align before passing to `compute_rel_normal`.

4. **MoGe's eval can run in parallel with EvalMDE work.** Production `eval_all_slurm.sh`
   already running. Don't disturb.

5. **Lotus disparity ckpt inversion was numerically unstable** (1/disp blows up near
   disparity=0). For EvalMDE, only emit `disparity_affine_invariant` from Lotus, then
   convert: `aligned_disp = scale*disp + shift` (fit in disp space), `aligned_depth = 1/aligned_disp.clamp(1/gt_depth_max)`.
   Reference: `moge/test/metrics.py:202-218` disparity_affine_invariant block.

---

## Resume instructions

1. `cd /home/ywan0794/EvalMDE`
2. Check Infinigen download: `du -sh data/infinigen; tail /tmp/dl_infinigen.log`
3. Fix imports (TODO-1):
   ```bash
   sed -i 's|from moge.test.baseline|from test.baseline|g' baselines/*.py
   ```
4. Write `scripts/eval.py` (TODO-2) using the pseudocode above.
5. Test on 1 scene with depth_pro: `python scripts/eval.py --baseline baselines/depth_pro.py --data-root data/infinigen --output /tmp/test.json --repo /home/ywan0794/EvalMDE/ml-depth-pro --checkpoint /home/ywan0794/EvalMDE/ml-depth-pro/checkpoints/depth_pro.pt`
6. Inspect `/tmp/test.json`. If sane (rel_normal in [0, 1] rad, sawa_h plausible),
   proceed to write sbatch (TODO-4).

---

**End of handoff.**