recipe(DepthPro-hf): add depth-estimation recipe (Goal-L1 PASS on CPU)

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PR: apple/DepthPro-hf — depth-estimation recipe (fp32, CPU)

Iter: 6 (build closure shipped iter-3 as depth_pro-002/003; this PR adds the L1-CPU evidence on top)
Producer: main agent (2026-06-23)
Claimed tier: (Effort = L0★, Goal = L1-CPU, Outcome = L0)

Summary

This PR ships the apple/DepthPro-hf depth-estimation recipe. DepthPro is a 952M-param model with 3 independent DINOv2 backbones (patch + image + fov encoders) plus neck/fov/fusion stages — the recipe is structurally one of the largest single-graph models in the catalog. Builds cleanly via the standard L0★ template; L1-CPU PASSes via a custom Python harness that reuses the cached artifact (the winml perf path triggers a re-export per invocation, which is wasteful for a 3.6 GB model — see Diligence ladder note in §8). No source-code changes.

1. Recipe file

examples/recipes/apple_DepthPro-hf/depth-estimation_fp16_config.json

Filename fp16_* is cosmetic per _meta-014; recipe ships fp32 (102 FLOAT32 initializers, 0 FLOAT16 verified by onnx.load).

Recipe input shape: pixel_values [1, 3, 1536, 1536] float32 range [0, 1]. Outputs: predicted_depth [1, 1536, 1536] + field_of_view [1].

2. README index row

examples/recipes/README.md — row to add for apple/DepthPro-hf | depth-estimation | single (no composite) | recipe.

3. Build output directory + artifact inventory

temp/depth_pro_build/ (gitignored — referenced by path for reviewer re-execution):

File	Size	Purpose
model.onnx	small	optimized graph pointer (external-data layout)
model.onnx.data	~3.6 GB	external-data shard (FLOAT32 weights)
export.onnx + .data	~3.6 GB	pre-optimize artifact
optimized.onnx + .data	~3.6 GB	mid-pipeline artifact
analyze_result.json	mined	op histogram (Step 4)
export_htp_metadata.json	mined	module hierarchy + trace coverage (Step 4)
winml_build_config.json	mined	autoconf diff (Step 4)

External-data layout check (_meta-023): model.onnx and model.onnx.data co-located in same directory. PASS.

4. Build log

temp/depth_pro_build.log — Build complete in 758.0s (export 375s + optimize 355s). Build artifact path: temp/depth_pro_build/.

5. Appended findings

Per-model — model_knowledge/depth_pro.json

• depth_pro-001 — pre-build coverage probe (WINML-ONLY).
• depth_pro-002 — VALIDATED build closure (iter-3).
• depth_pro-003 — build-artifact mining (3-backbone architecture, 49% layout-move ops, 952M params).

Skill-meta

No new _meta-NNN findings in this PR (Lane B).

6. Optimum-coverage probe verdict

mt = "depth_pro"
# vendor: {}  (NOT registered upstream)
# after_winml: {"depth-estimation": <winml override>}
# added_by_winml: ["depth-estimation"]

Verdict: WINML-ONLY. depth_pro model_type is not registered in Optimum's TasksManager._SUPPORTED_MODEL_TYPE; winml's register_onnx_overwrite decorator at src/winml/modelkit/models/hf/depth_pro.py is what makes export work. Despite the WINML-ONLY classification, no code is needed in THIS PR (the per-arch file already exists) — the recipe is a pure consumer of the existing registration. Effort L0★ confirmed.

7. Claimed (Effort, Goal, Outcome) tier

• Effort = L0★ (recipe-only; the per-arch depth_pro.py already exists from prior iter; this PR adds only the recipe + finding append)
• Goal = L1-CPU (L0 PASS + L1-CPU PASS via custom Python harness; L2/L3 deferred — depth metrics CLI-blocked per _meta-015 analogue for depth-estimation)
• Outcome = L0 (recipe + finding append + this report)

8. Goal-ladder verdict table (per _meta-018)

Tier	Verdict	Evidence
L0	PASS	winml build → model.onnx + model.onnx.data co-located; opset 17, fp32, 2822 nodes, 19 unique op types; Build complete in 758.0s. Log: temp/depth_pro_build.log
L1-CPU	PASS	Avg 28513.7 ms / Min 27830.9 / Max 29110.3 / Std 525.9 on real-shape pixel_values [1,3,1536,1536] input; warmup 29582 ms (cold); throughput 0.035 samples/sec on CPU. Custom Python harness per _meta-017 (avoids re-export). Log: temp/depth_pro_perf_cpu.log; script: temp/depth_pro_perf.py
L1-DML/QNN/OpenVINO	HOST-BLOCKED	Per _meta-016. 49% layout-move ops (Reshape/Transpose/Slice = 1378/2822 per depth_pro-003) means QNN-NPU would likely be heavily move-bound even when available.
L2	DEFERRED-HARNESS	Honest verdict per _meta-018. PT-vs-ONNX comparison would need DepthPro pipeline reconstruction (preprocessor → 3-backbone forward → neck → fusion → head); script not written this turn.
L3	CLI-BLOCKED	winml eval task registry does not include depth-estimation (analogous to translation per _meta-015).

Short-circuit honored: no FAIL anywhere. L2/L3 deferred-or-blocked do not halt the march per _meta-018. Honest ceiling is L1-CPU PASS.

Diligence ladder (_meta-037) — invoked during L1 attempt:

1. Re-read depth_pro.json — no prior perf workaround documented for this model.
2. PR-mine — no prior DepthPro perf PRs.
3. Re-run winml config — N/A, recipe already exists.
4. --ep-options retry — N/A, CPU not failing.
5. value_range / shape pinning — recipe shape already pinned to [1,3,1536,1536].
6. Custom Python harness — ✅ this is the step that worked. winml perf triggered full re-export (~13 min per invocation since each uv run winml perf rebuilds the artifact); switched to direct onnxruntime.InferenceSession against cached temp/depth_pro_build/model.onnx. Loaded in 15.44s, ran 3 iters in 86s total.

Feature gap from step 6 trigger: winml perf should accept a pre-built artifact path (e.g. --artifact temp/depth_pro_build/model.onnx) and skip the build phase entirely. For a 3.6 GB model, the build-per-perf-invocation cost is prohibitive. Captured under depth_pro-003 feature_gaps_filed[] as a follow-up.

9. Methodology-evolution declaration (per _meta-031)

No NEW methodology friction in this PR. The custom-harness pattern is _meta-017; the winml perf re-export cost is a new observation but rolls into the existing _meta-017 gotcha rather than a fresh _meta-NNN. Triggers:

• (1) CLI surprise — none (one personal flag-name mistake --warmup-iterations vs --warmup, recovered via winml perf --help; not a doc-cited flag).
• (2) Doc-code drift — none.
• (3) Silent-failure mode — none.
• (4) New verdict shape — none.
• (5) Reviewer-found gap — pending.
• (6) Effort mis-estimate — none (L0★ predicted, L0★ delivered).
• (7) PR-mining discovery — none.

Reviewer should confirm "no methodology friction observed" per _meta-031 anti-trigger.

Reviewer hand-off package — Step 6 9-item self-check

1. Recipe file — §1 ✓
2. README row — §2 ✓ (to add in this PR)
3. Build output dir + artifact inventory — §3 ✓
4. Build log — §4 ✓
5. Appended findings — §5 ✓
6. Optimum-coverage probe verdict — §6 ✓
7. Claimed tier — §7 ✓
8. Goal-ladder verdict table — §8 ✓
9. Methodology-evolution declaration — §9 ✓