Merge pull request #6 from cmoyates/feat/engine-integration-surface
feat: add CorridorKeyMLXEngine integration surface
This commit is contained in:
Cristopher Yates
GitHub
commit
6e427fde99
69
README.md
69
README.md
@ -199,7 +199,76 @@ result = tiled_inference(model, x, tile_size=512, overlap=64)
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uv run python scripts/compare_reference.py
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```
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## Using as a CorridorKey backend
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This repo can be consumed as a drop-in MLX backend by the main CorridorKey app.
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### Install (editable, from sibling checkout)
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```bash
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# from the main CorridorKey repo directory
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uv pip install -e ../corridorkey-mlx
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```
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### Engine API
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```python
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from corridorkey_mlx import CorridorKeyMLXEngine
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engine = CorridorKeyMLXEngine(
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checkpoint_path="/abs/path/to/corridorkey_mlx.safetensors",
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img_size=2048, # production (512 for dev)
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use_refiner=True,
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compile=True, # faster after first call
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)
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result = engine.process_frame(rgb_uint8, mask_uint8)
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# result["alpha"] — (H, W) uint8 alpha matte
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# result["fg"] — (H, W, 3) uint8 foreground
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# result["comp"] — (H, W, 3) uint8 fg composited over black
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# result["processed"] — (H, W, 3) uint8 (placeholder, same as fg)
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```
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### Expected inputs
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- **image**: numpy uint8 `(H, W, 3)` RGB. sRGB color space (standard).
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- **mask**: numpy uint8 `(H, W)` or `(H, W, 1)` grayscale alpha hint.
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- **checkpoint**: `.safetensors` format, converted from PyTorch via `scripts/convert_weights.py`.
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Inputs are resized internally to `img_size` for inference, then outputs are
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resized back to the original input resolution.
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### Smoke test
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```bash
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uv run python scripts/smoke_engine.py \
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--image input.png --hint hint.png \
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--checkpoint checkpoints/corridorkey_mlx.safetensors \
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--img-size 512
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```
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### Standalone scripts vs engine usage
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| | Standalone (`scripts/infer.py`) | Engine (`CorridorKeyMLXEngine`) |
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|---|---|---|
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| Input | file paths | numpy arrays |
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| Output | saved PNGs | in-memory dict |
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| Returns | `alpha`, `foreground` | `alpha`, `fg`, `comp`, `processed` |
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| Default img_size | 512 | 2048 |
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| Use case | one-off CLI inference | app backend integration |
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### Stubs (not yet implemented)
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- `despill_strength` — accepted but ignored (warns once)
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- `auto_despeckle` / `despeckle_size` — accepted but ignored (warns once)
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- `input_is_linear` — accepted but no-op (model expects sRGB)
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### Python version
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Requires Python >=3.11. Compatible with the main CorridorKey repo's 3.11 target.
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## Current Status
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Phases 1–6 complete. Full model assembly with end-to-end parity verified.
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Optimization, benchmarking, and tiled inference available.
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Engine integration surface available for backend consumption.
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192
docs/plans/2026-03-01-feat-engine-integration-surface-plan.md
Normal file
192
docs/plans/2026-03-01-feat-engine-integration-surface-plan.md
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@ -0,0 +1,192 @@
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---
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title: "feat: Add CorridorKeyMLXEngine integration surface"
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type: feat
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date: 2026-03-01
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---
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# feat: Add CorridorKeyMLXEngine integration surface
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## Overview
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Expose a stable `CorridorKeyMLXEngine` class so the main CorridorKey repo can consume this package as a drop-in MLX backend. Currently only flat functions exist (`load_model`, `infer`, `infer_and_save`) — none accept in-memory arrays, and there is no engine lifecycle or `process_frame(...)` contract.
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## Problem Statement
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The main CorridorKey repo expects a backend engine with:
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- Constructor: `checkpoint_path`, `device`, `img_size`, `use_refiner`
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- Method: `process_frame(image, mask_linear, ...)` returning dict with `alpha`, `fg`, `comp`, `processed`
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This repo currently:
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- Has no engine class — only loose functions in `pipeline.py`
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- `infer()` accepts file paths, not numpy/PIL arrays
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- Returns only `alpha` + `foreground`, missing `comp` and `processed`
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- Has no `refiner_scale`, despill, despeckle, or linear/sRGB handling
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- `DEFAULT_CHECKPOINT` is a relative path (breaks as installed library)
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- `requires-python >= 3.12` but main repo targets 3.11
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## Proposed Solution
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Thin adapter class in `src/corridorkey_mlx/engine.py` wrapping existing model/inference code. No core model changes.
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## Technical Approach
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### Phase 1: Engine adapter + packaging fixes
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#### 1a. Lower Python version to >=3.11
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- All source already uses `from __future__ import annotations` — no 3.12-only syntax
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- Update `pyproject.toml`: `requires-python = ">=3.11"`
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- Update `tool.ruff.target-version` and `tool.mypy.python_version`
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- Check dep version floors are 3.11-compatible (numpy >=2.4.2 may need lowering — numpy 2.0+ supports 3.11)
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- Run `uv run ruff check .` + `uv run mypy src/` to verify
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#### 1b. Create `src/corridorkey_mlx/engine.py`
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```python
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class CorridorKeyMLXEngine:
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def __init__(
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self,
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checkpoint_path: str | Path,
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device: str | None = None, # ignored on MLX, accepted for compat
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img_size: int = 2048, # production default
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use_refiner: bool = True,
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compile: bool = True,
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) -> None: ...
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def process_frame(
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self,
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image: np.ndarray, # uint8 HWC RGB
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mask_linear: np.ndarray, # uint8 HW or HW1 grayscale
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refiner_scale: float = 1.0,
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input_is_linear: bool = False,
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fg_is_straight: bool = True,
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despill_strength: float = 1.0,
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auto_despeckle: bool = True,
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despeckle_size: int = 400,
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) -> dict[str, np.ndarray]: ...
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```
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Constructor:
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- Validates `checkpoint_path` exists (absolute or resolved)
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- Calls existing `load_model()` with `img_size` and `compile`
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- Stores `use_refiner`, `img_size`
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- Logs warning if `device` is not None
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`process_frame()` pipeline:
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1. **Input validation**: assert uint8 HWC(3) for image, uint8 HW or HW1 for mask
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2. **Store original resolution** for output resize
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3. **Convert to float32 [0,1]**: `image / 255.0`, `mask / 255.0`
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4. **Reshape mask**: ensure `(H, W, 1)`
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5. **Resize** to `img_size x img_size` via PIL (bicubic) — reuse existing pattern
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6. **Preprocess**: call existing `normalize_rgb()` + `preprocess()` from `io/image.py`
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7. **Forward pass**: `self._model(x)` — raw output dict
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8. **Materialize** all outputs
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9. **Select outputs**: if `use_refiner=True`, use `alpha_final`/`fg_final`; else use `alpha_coarse`/`fg_coarse`
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10. **Apply refiner_scale** (output-space lerp): `alpha = lerp(alpha_coarse, alpha_final, refiner_scale)`
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11. **Postprocess** to uint8 via existing `postprocess_alpha()`/`postprocess_foreground()`
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12. **Resize outputs** back to original input resolution
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13. **Despill/despeckle**: no-op stubs for now (documented, warn once)
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14. **Composite**: `comp = (fg * alpha_3ch + bg * (1 - alpha_3ch))` with black bg, uint8
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15. **Return** `{"alpha": ..., "fg": ..., "comp": ..., "processed": fg}` — `processed` = fg until despill/despeckle implemented
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#### 1c. Export from `__init__.py`
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```python
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from corridorkey_mlx.engine import CorridorKeyMLXEngine
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```
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So callers can do `from corridorkey_mlx import CorridorKeyMLXEngine`.
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#### 1d. Fix DEFAULT_CHECKPOINT
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Remove relative-path default from `pipeline.py`. Engine requires explicit `checkpoint_path`.
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### Phase 2: Smoke script + tests
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#### 2a. `scripts/smoke_engine.py`
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- Takes `--image`, `--hint`, `--checkpoint` args
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- Instantiates `CorridorKeyMLXEngine`
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- Runs `process_frame()`
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- Prints output shapes and value ranges
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- Optionally saves outputs
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#### 2b. Tests in `tests/test_engine.py`
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- **test_engine_init_requires_checkpoint**: missing path raises error
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- **test_engine_output_keys**: process_frame returns `alpha`, `fg`, `comp`, `processed`
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- **test_engine_output_shapes**: all outputs match input spatial dims
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- **test_engine_output_dtypes**: all uint8
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- **test_engine_mask_shape_normalization**: HW and HW1 both accepted
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Use small synthetic inputs (e.g. 64x64 random) with checkpoint. Mark as `@pytest.mark.skipif` when checkpoint not available.
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### Phase 3: Documentation
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#### 3a. README section: "Using as a backend"
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Cover:
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- Editable install: `uv pip install -e ../corridorkey-mlx`
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- Canonical import: `from corridorkey_mlx import CorridorKeyMLXEngine`
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- Constructor params and defaults
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- Expected checkpoint format (.safetensors, converted)
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- Expected image/hint formats (uint8 HWC RGB, uint8 HW grayscale)
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- Output dict keys and shapes
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- Smoke command example
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- Migration note: standalone script vs backend engine usage
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#### 3b. Docstrings
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Engine class and `process_frame` get thorough docstrings covering:
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- Input formats (uint8 HWC RGB, uint8 HW mask)
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- Output formats and semantics
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- Preprocessing chain (resize, ImageNet norm, NHWC)
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- Which params are stubs (despill, despeckle)
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- Linear vs sRGB assumptions
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- img_size: 512 for dev, 2048 for production
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## Acceptance Criteria
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- [ ] `from corridorkey_mlx import CorridorKeyMLXEngine` works
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- [ ] Constructor accepts `checkpoint_path`, `device`, `img_size`, `use_refiner`, `compile`
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- [ ] `process_frame()` accepts numpy uint8 arrays, returns dict with `alpha`, `fg`, `comp`, `processed`
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- [ ] Outputs resized to original input resolution
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- [ ] `use_refiner=False` returns coarse predictions
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- [ ] `refiner_scale` blends between coarse and refined
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- [ ] Despill/despeckle are documented stubs (no-op, warn once)
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- [ ] `comp` composites fg over black background
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- [ ] `processed` = fg (until despill/despeckle implemented)
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- [ ] Python >=3.11 works
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- [ ] Smoke script runs one frame successfully
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- [ ] Existing 94 tests still pass
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- [ ] README documents backend usage + editable install
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## Design Decisions
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| Decision | Choice | Rationale |
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|---|---|---|
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| `refiner_scale` semantics | output-space lerp | no model changes needed |
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| `use_refiner=False` | use `alpha_coarse`/`fg_coarse` from output dict | model always runs full forward; adapter selects outputs |
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| despill/despeckle | no-op stubs, warn once | algorithms unknown; unblock integration now |
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| `comp` background | black (0,0,0) | common default for matte compositing |
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| `processed` meaning | same as `fg` for now | placeholder until despill/despeckle exist |
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| `device` param | accepted, ignored, log warning | compat with Torch engine signature |
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| default `img_size` | 2048 (engine), 512 (dev scripts) | model trained at 2048; scripts keep 512 for speed |
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| `input_is_linear` | accepted, no-op for now | ImageNet stats assume sRGB; linearization would break normalization |
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| checkpoint default | none — required param | relative paths break as library |
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## Dependencies & Risks
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- **numpy version floor**: `numpy>=2.4.2` may not support 3.11. Need to check and potentially lower to `>=1.26` or `>=2.0`.
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- **Despill/despeckle gap**: real implementations need the main repo's algorithm. Document as TODO.
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- **refiner_scale at compile time**: output-space lerp works with compiled model since it's post-forward. Logit-space would require model changes and recompilation.
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## Unresolved Questions
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1. `processed` — what exactly does main repo return here? Despilled fg? Masked fg?
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2. `comp` bg — always black or configurable?
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3. `use_refiner=False` — skip refiner forward pass (perf) or just ignore outputs (simpler)?
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4. `refiner_scale` — logit-space or output-space? (plan assumes output-space)
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5. despill/despeckle algorithms — need from main repo for real impl
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6. `input_is_linear` — does original model ever receive linear-light inputs?
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7. `mask_linear` naming — is it actually linear-light or just naming convention?
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@ -3,15 +3,13 @@ name = "corridorkey-mlx"
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version = "0.1.0"
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description = "MLX inference port of CorridorKey for Apple Silicon"
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readme = "README.md"
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requires-python = ">=3.12"
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requires-python = ">=3.11"
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dependencies = [
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"mlx>=0.31.0",
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"numpy>=2.4.2",
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"pillow>=12.1.1",
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"pydantic>=2.12.5",
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"rich>=14.3.3",
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"safetensors>=0.7.0",
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"typer>=0.24.1",
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"numpy>=2.0.0",
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"pillow>=10.0.0",
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"rich>=13.0.0",
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"safetensors>=0.4.0",
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]
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[dependency-groups]
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@ -38,7 +36,7 @@ packages = ["src/corridorkey_mlx"]
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testpaths = ["tests"]
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[tool.ruff]
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target-version = "py312"
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target-version = "py311"
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line-length = 99
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src = ["src"]
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@ -46,6 +44,6 @@ src = ["src"]
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select = ["E", "F", "I", "UP", "B", "SIM", "TCH"]
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[tool.mypy]
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python_version = "3.12"
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python_version = "3.11"
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strict = true
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mypy_path = "src"
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59
scripts/smoke_engine.py
Normal file
59
scripts/smoke_engine.py
Normal file
@ -0,0 +1,59 @@
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"""Smoke test for the CorridorKeyMLXEngine integration surface.
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Instantiates the engine, runs one frame, prints output shapes and value ranges.
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"""
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from __future__ import annotations
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import argparse
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from pathlib import Path
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import numpy as np
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from PIL import Image
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from corridorkey_mlx import CorridorKeyMLXEngine
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def main() -> None:
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parser = argparse.ArgumentParser(description="Smoke test: CorridorKeyMLXEngine")
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parser.add_argument("--image", type=Path, required=True, help="RGB input image")
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parser.add_argument("--hint", type=Path, required=True, help="Grayscale alpha hint")
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parser.add_argument(
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"--checkpoint",
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type=Path,
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default=Path("checkpoints/corridorkey_mlx.safetensors"),
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)
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parser.add_argument("--img-size", type=int, default=512)
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parser.add_argument("--output-dir", type=Path, default=None)
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args = parser.parse_args()
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print(f"Loading engine (img_size={args.img_size})...")
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engine = CorridorKeyMLXEngine(
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checkpoint_path=args.checkpoint,
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img_size=args.img_size,
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compile=False,
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)
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rgb = np.asarray(Image.open(args.image).convert("RGB"))
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mask = np.asarray(Image.open(args.hint).convert("L"))
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print(f"Input image: {rgb.shape} {rgb.dtype}")
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print(f"Input mask: {mask.shape} {mask.dtype}")
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result = engine.process_frame(rgb, mask)
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for key, arr in result.items():
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print(f" {key}: shape={arr.shape} dtype={arr.dtype} range=[{arr.min()}, {arr.max()}]")
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if args.output_dir is not None:
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out = Path(args.output_dir)
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out.mkdir(parents=True, exist_ok=True)
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Image.fromarray(result["alpha"], mode="L").save(out / "alpha.png")
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Image.fromarray(result["fg"], mode="RGB").save(out / "fg.png")
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Image.fromarray(result["comp"], mode="RGB").save(out / "comp.png")
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print(f"Saved outputs to {out}")
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print("Smoke test passed.")
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if __name__ == "__main__":
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main()
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@ -1,3 +1,7 @@
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"""CorridorKey MLX — inference port for Apple Silicon."""
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__version__ = "0.1.0"
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from corridorkey_mlx.engine import CorridorKeyMLXEngine
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__all__ = ["CorridorKeyMLXEngine"]
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241
src/corridorkey_mlx/engine.py
Normal file
241
src/corridorkey_mlx/engine.py
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@ -0,0 +1,241 @@
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"""CorridorKey MLX engine — drop-in backend for the main CorridorKey app.
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Wraps the MLX GreenFormer model behind a stable ``process_frame`` API that
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mirrors the Torch engine contract expected by the main CorridorKey repository.
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"""
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from __future__ import annotations
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import logging
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import warnings
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from pathlib import Path
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from typing import TYPE_CHECKING
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import mlx.core as mx
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import numpy as np
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from PIL import Image
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from corridorkey_mlx.inference.pipeline import load_model
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from corridorkey_mlx.io.image import (
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postprocess_alpha,
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postprocess_foreground,
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preprocess,
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)
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if TYPE_CHECKING:
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from corridorkey_mlx.model.corridorkey import GreenFormer
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logger = logging.getLogger(__name__)
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PRODUCTION_IMG_SIZE = 2048
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class CorridorKeyMLXEngine:
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"""MLX inference engine compatible with the main CorridorKey backend contract.
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Args:
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checkpoint_path: Absolute or resolvable path to converted MLX
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``.safetensors`` checkpoint. Required — no default.
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device: Ignored on MLX (Apple Silicon uses unified memory).
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Accepted for API compatibility with the Torch engine.
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img_size: Internal model resolution (square). The model was trained
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at 2048. Use 512 for fast dev iteration.
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||||
use_refiner: If True, return refined alpha/fg. If False, return
|
||||
coarse predictions (skips refiner in postprocessing, not forward pass).
|
||||
compile: If True, wrap model forward with ``mx.compile`` for faster
|
||||
repeated inference at the same resolution.
|
||||
|
||||
Example::
|
||||
|
||||
from corridorkey_mlx import CorridorKeyMLXEngine
|
||||
|
||||
engine = CorridorKeyMLXEngine("/path/to/corridorkey_mlx.safetensors")
|
||||
result = engine.process_frame(rgb_uint8, mask_uint8)
|
||||
# result["alpha"] — (H, W) uint8
|
||||
# result["fg"] — (H, W, 3) uint8
|
||||
# result["comp"] — (H, W, 3) uint8
|
||||
# result["processed"] — (H, W, 3) uint8
|
||||
"""
|
||||
|
||||
_despill_warned: bool = False
|
||||
_despeckle_warned: bool = False
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
checkpoint_path: str | Path,
|
||||
device: str | None = None,
|
||||
img_size: int = PRODUCTION_IMG_SIZE,
|
||||
use_refiner: bool = True,
|
||||
compile: bool = True,
|
||||
) -> None:
|
||||
checkpoint = Path(checkpoint_path)
|
||||
if not checkpoint.exists():
|
||||
msg = f"Checkpoint not found: {checkpoint}"
|
||||
raise FileNotFoundError(msg)
|
||||
|
||||
if device is not None:
|
||||
logger.info("device=%r ignored on MLX (unified memory)", device)
|
||||
|
||||
self._img_size = img_size
|
||||
self._use_refiner = use_refiner
|
||||
self._model: GreenFormer = load_model(
|
||||
checkpoint, img_size=img_size, compile=compile
|
||||
)
|
||||
|
||||
def process_frame(
|
||||
self,
|
||||
image: np.ndarray,
|
||||
mask_linear: np.ndarray,
|
||||
refiner_scale: float = 1.0,
|
||||
input_is_linear: bool = False,
|
||||
fg_is_straight: bool = True,
|
||||
despill_strength: float = 1.0,
|
||||
auto_despeckle: bool = True,
|
||||
despeckle_size: int = 400,
|
||||
) -> dict[str, np.ndarray]:
|
||||
"""Run inference on a single frame.
|
||||
|
||||
Args:
|
||||
image: RGB input, uint8 ``(H, W, 3)``.
|
||||
mask_linear: Coarse alpha hint, uint8 ``(H, W)`` or ``(H, W, 1)``.
|
||||
refiner_scale: Blend factor between coarse and refined output.
|
||||
1.0 = fully refined, 0.0 = fully coarse.
|
||||
input_is_linear: Accepted for compat; currently a no-op.
|
||||
ImageNet normalization assumes sRGB inputs.
|
||||
fg_is_straight: If True, foreground uses straight alpha for compositing.
|
||||
despill_strength: Stub — not yet implemented.
|
||||
auto_despeckle: Stub — not yet implemented.
|
||||
despeckle_size: Stub — not yet implemented.
|
||||
|
||||
Returns:
|
||||
Dict with uint8 numpy arrays:
|
||||
- ``alpha``: ``(H, W)`` alpha matte
|
||||
- ``fg``: ``(H, W, 3)`` foreground
|
||||
- ``comp``: ``(H, W, 3)`` foreground composited over black
|
||||
- ``processed``: ``(H, W, 3)`` same as ``fg`` (placeholder)
|
||||
"""
|
||||
# -- input validation --
|
||||
_validate_image(image)
|
||||
_validate_mask(mask_linear)
|
||||
|
||||
original_h, original_w = image.shape[:2]
|
||||
|
||||
# -- to float32 [0, 1] --
|
||||
rgb_f32 = image.astype(np.float32) / 255.0
|
||||
mask_f32 = mask_linear.astype(np.float32) / 255.0
|
||||
if mask_f32.ndim == 2:
|
||||
mask_f32 = mask_f32[:, :, np.newaxis]
|
||||
|
||||
# -- resize to model resolution --
|
||||
if rgb_f32.shape[0] != self._img_size or rgb_f32.shape[1] != self._img_size:
|
||||
rgb_pil = Image.fromarray(image).resize(
|
||||
(self._img_size, self._img_size), Image.BICUBIC
|
||||
)
|
||||
rgb_f32 = np.asarray(rgb_pil, dtype=np.float32) / 255.0
|
||||
|
||||
mask_u8 = mask_linear if mask_linear.ndim == 2 else mask_linear[:, :, 0]
|
||||
mask_pil = Image.fromarray(mask_u8, mode="L").resize(
|
||||
(self._img_size, self._img_size), Image.BICUBIC
|
||||
)
|
||||
mask_f32 = np.asarray(mask_pil, dtype=np.float32)[:, :, np.newaxis] / 255.0
|
||||
|
||||
# -- preprocess (ImageNet norm + concat) -> (1, H, W, 4) NHWC --
|
||||
x = preprocess(rgb_f32, mask_f32)
|
||||
|
||||
# -- forward --
|
||||
outputs = self._model(x)
|
||||
mx.eval(outputs) # noqa: S307 — mx.eval materializes lazy MLX arrays, not Python eval
|
||||
|
||||
# -- select coarse vs refined --
|
||||
alpha_coarse = outputs["alpha_coarse"]
|
||||
fg_coarse = outputs["fg_coarse"]
|
||||
alpha_refined = outputs["alpha_final"]
|
||||
fg_refined = outputs["fg_final"]
|
||||
|
||||
if not self._use_refiner or refiner_scale == 0.0:
|
||||
alpha_out = alpha_coarse
|
||||
fg_out = fg_coarse
|
||||
elif refiner_scale == 1.0:
|
||||
alpha_out = alpha_refined
|
||||
fg_out = fg_refined
|
||||
else:
|
||||
# output-space lerp
|
||||
s = refiner_scale
|
||||
alpha_out = alpha_coarse * (1.0 - s) + alpha_refined * s
|
||||
fg_out = fg_coarse * (1.0 - s) + fg_refined * s
|
||||
|
||||
# -- postprocess to uint8 --
|
||||
alpha_u8 = postprocess_alpha(alpha_out)
|
||||
fg_u8 = postprocess_foreground(fg_out)
|
||||
|
||||
# -- resize back to original --
|
||||
if alpha_u8.shape[0] != original_h or alpha_u8.shape[1] != original_w:
|
||||
target = (original_w, original_h)
|
||||
alpha_u8 = np.asarray(
|
||||
Image.fromarray(alpha_u8, mode="L").resize(target, Image.BICUBIC),
|
||||
dtype=np.uint8,
|
||||
)
|
||||
fg_u8 = np.asarray(
|
||||
Image.fromarray(fg_u8, mode="RGB").resize(target, Image.BICUBIC),
|
||||
dtype=np.uint8,
|
||||
)
|
||||
|
||||
# -- stubs: despill / despeckle --
|
||||
if despill_strength > 0.0 and not CorridorKeyMLXEngine._despill_warned:
|
||||
warnings.warn(
|
||||
"despill not yet implemented in MLX backend; strength ignored",
|
||||
stacklevel=2,
|
||||
)
|
||||
CorridorKeyMLXEngine._despill_warned = True
|
||||
|
||||
if auto_despeckle and not CorridorKeyMLXEngine._despeckle_warned:
|
||||
warnings.warn(
|
||||
"despeckle not yet implemented in MLX backend; ignored",
|
||||
stacklevel=2,
|
||||
)
|
||||
CorridorKeyMLXEngine._despeckle_warned = True
|
||||
|
||||
# -- composite over black --
|
||||
alpha_3ch = alpha_u8[:, :, np.newaxis].astype(np.float32) / 255.0
|
||||
fg_float = fg_u8.astype(np.float32)
|
||||
comp = (
|
||||
(fg_float * alpha_3ch).astype(np.uint8)
|
||||
if fg_is_straight
|
||||
else fg_u8.copy()
|
||||
)
|
||||
|
||||
return {
|
||||
"alpha": alpha_u8,
|
||||
"fg": fg_u8,
|
||||
"comp": comp,
|
||||
"processed": fg_u8,
|
||||
}
|
||||
|
||||
|
||||
def _validate_image(image: np.ndarray) -> None:
|
||||
"""Validate image is uint8 HWC RGB."""
|
||||
if not isinstance(image, np.ndarray):
|
||||
msg = f"image must be numpy ndarray, got {type(image).__name__}"
|
||||
raise TypeError(msg)
|
||||
if image.dtype != np.uint8:
|
||||
msg = f"image must be uint8, got {image.dtype}"
|
||||
raise ValueError(msg)
|
||||
if image.ndim != 3 or image.shape[2] != 3:
|
||||
msg = f"image must be (H, W, 3), got {image.shape}"
|
||||
raise ValueError(msg)
|
||||
|
||||
|
||||
def _validate_mask(mask: np.ndarray) -> None:
|
||||
"""Validate mask is uint8 HW or HW1."""
|
||||
if not isinstance(mask, np.ndarray):
|
||||
msg = f"mask must be numpy ndarray, got {type(mask).__name__}"
|
||||
raise TypeError(msg)
|
||||
if mask.dtype != np.uint8:
|
||||
msg = f"mask must be uint8, got {mask.dtype}"
|
||||
raise ValueError(msg)
|
||||
if mask.ndim == 2:
|
||||
return
|
||||
if mask.ndim == 3 and mask.shape[2] == 1:
|
||||
return
|
||||
msg = f"mask must be (H, W) or (H, W, 1), got {mask.shape}"
|
||||
raise ValueError(msg)
|
||||
137
tests/test_engine.py
Normal file
137
tests/test_engine.py
Normal file
@ -0,0 +1,137 @@
|
||||
"""Tests for CorridorKeyMLXEngine contract."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from corridorkey_mlx.engine import (
|
||||
CorridorKeyMLXEngine,
|
||||
_validate_image,
|
||||
_validate_mask,
|
||||
)
|
||||
|
||||
CHECKPOINT = Path("checkpoints/corridorkey_mlx.safetensors")
|
||||
HAS_CHECKPOINT = CHECKPOINT.exists()
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Input validation (no checkpoint needed)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestValidateImage:
|
||||
def test_rejects_non_ndarray(self) -> None:
|
||||
with pytest.raises(TypeError, match="numpy ndarray"):
|
||||
_validate_image("not_an_array") # type: ignore[arg-type]
|
||||
|
||||
def test_rejects_wrong_dtype(self) -> None:
|
||||
img = np.zeros((64, 64, 3), dtype=np.float32)
|
||||
with pytest.raises(ValueError, match="uint8"):
|
||||
_validate_image(img)
|
||||
|
||||
def test_rejects_wrong_shape(self) -> None:
|
||||
img = np.zeros((64, 64), dtype=np.uint8)
|
||||
with pytest.raises(ValueError, match="\\(H, W, 3\\)"):
|
||||
_validate_image(img)
|
||||
|
||||
def test_rejects_wrong_channels(self) -> None:
|
||||
img = np.zeros((64, 64, 4), dtype=np.uint8)
|
||||
with pytest.raises(ValueError, match="\\(H, W, 3\\)"):
|
||||
_validate_image(img)
|
||||
|
||||
def test_accepts_valid_image(self) -> None:
|
||||
img = np.zeros((64, 64, 3), dtype=np.uint8)
|
||||
_validate_image(img) # should not raise
|
||||
|
||||
|
||||
class TestValidateMask:
|
||||
def test_rejects_non_ndarray(self) -> None:
|
||||
with pytest.raises(TypeError, match="numpy ndarray"):
|
||||
_validate_mask("not_an_array") # type: ignore[arg-type]
|
||||
|
||||
def test_rejects_wrong_dtype(self) -> None:
|
||||
mask = np.zeros((64, 64), dtype=np.float32)
|
||||
with pytest.raises(ValueError, match="uint8"):
|
||||
_validate_mask(mask)
|
||||
|
||||
def test_accepts_hw(self) -> None:
|
||||
mask = np.zeros((64, 64), dtype=np.uint8)
|
||||
_validate_mask(mask) # should not raise
|
||||
|
||||
def test_accepts_hw1(self) -> None:
|
||||
mask = np.zeros((64, 64, 1), dtype=np.uint8)
|
||||
_validate_mask(mask) # should not raise
|
||||
|
||||
def test_rejects_hw3(self) -> None:
|
||||
mask = np.zeros((64, 64, 3), dtype=np.uint8)
|
||||
with pytest.raises(ValueError, match="\\(H, W\\) or \\(H, W, 1\\)"):
|
||||
_validate_mask(mask)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Engine init
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
class TestEngineInit:
|
||||
def test_missing_checkpoint_raises(self) -> None:
|
||||
with pytest.raises(FileNotFoundError, match="not found"):
|
||||
CorridorKeyMLXEngine(checkpoint_path="/nonexistent/weights.safetensors")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Engine integration (requires checkpoint)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
@pytest.mark.skipif(not HAS_CHECKPOINT, reason="Checkpoint not available")
|
||||
class TestEngineIntegration:
|
||||
"""Integration tests that load the real model."""
|
||||
|
||||
@pytest.fixture(scope="class")
|
||||
def engine(self) -> CorridorKeyMLXEngine:
|
||||
return CorridorKeyMLXEngine(
|
||||
checkpoint_path=CHECKPOINT,
|
||||
img_size=512,
|
||||
compile=False,
|
||||
)
|
||||
|
||||
def test_output_keys(self, engine: CorridorKeyMLXEngine) -> None:
|
||||
image = np.random.default_rng(42).integers(0, 256, (64, 64, 3), dtype=np.uint8)
|
||||
mask = np.random.default_rng(42).integers(0, 256, (64, 64), dtype=np.uint8)
|
||||
result = engine.process_frame(image, mask)
|
||||
assert set(result.keys()) == {"alpha", "fg", "comp", "processed"}
|
||||
|
||||
def test_output_shapes(self, engine: CorridorKeyMLXEngine) -> None:
|
||||
h, w = 100, 150
|
||||
image = np.random.default_rng(42).integers(0, 256, (h, w, 3), dtype=np.uint8)
|
||||
mask = np.random.default_rng(42).integers(0, 256, (h, w), dtype=np.uint8)
|
||||
result = engine.process_frame(image, mask)
|
||||
assert result["alpha"].shape == (h, w)
|
||||
assert result["fg"].shape == (h, w, 3)
|
||||
assert result["comp"].shape == (h, w, 3)
|
||||
assert result["processed"].shape == (h, w, 3)
|
||||
|
||||
def test_output_dtypes(self, engine: CorridorKeyMLXEngine) -> None:
|
||||
image = np.random.default_rng(42).integers(0, 256, (64, 64, 3), dtype=np.uint8)
|
||||
mask = np.random.default_rng(42).integers(0, 256, (64, 64), dtype=np.uint8)
|
||||
result = engine.process_frame(image, mask)
|
||||
for key, arr in result.items():
|
||||
assert arr.dtype == np.uint8, f"{key} dtype is {arr.dtype}"
|
||||
|
||||
def test_mask_hw1_accepted(self, engine: CorridorKeyMLXEngine) -> None:
|
||||
image = np.random.default_rng(42).integers(0, 256, (64, 64, 3), dtype=np.uint8)
|
||||
mask = np.random.default_rng(42).integers(0, 256, (64, 64, 1), dtype=np.uint8)
|
||||
result = engine.process_frame(image, mask)
|
||||
assert "alpha" in result
|
||||
|
||||
def test_refiner_scale_zero_returns_coarse(
|
||||
self, engine: CorridorKeyMLXEngine
|
||||
) -> None:
|
||||
image = np.random.default_rng(42).integers(0, 256, (64, 64, 3), dtype=np.uint8)
|
||||
mask = np.random.default_rng(42).integers(0, 256, (64, 64), dtype=np.uint8)
|
||||
result = engine.process_frame(image, mask, refiner_scale=0.0)
|
||||
assert result["alpha"].shape == (64, 64)
|
||||
218
uv.lock
generated
218
uv.lock
generated
@ -1,6 +1,10 @@
|
||||
version = 1
|
||||
revision = 3
|
||||
requires-python = ">=3.12"
|
||||
requires-python = ">=3.11"
|
||||
resolution-markers = [
|
||||
"python_full_version >= '3.12'",
|
||||
"python_full_version < '3.12'",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "annotated-doc"
|
||||
@ -11,15 +15,6 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/1e/d3/26bf1008eb3d2daa8ef4cacc7f3bfdc11818d111f7e2d0201bc6e3b49d45/annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320", size = 5303, upload-time = "2025-11-10T22:07:40.673Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "annotated-types"
|
||||
version = "0.7.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "anyio"
|
||||
version = "4.12.1"
|
||||
@ -71,10 +66,8 @@ dependencies = [
|
||||
{ name = "mlx" },
|
||||
{ name = "numpy" },
|
||||
{ name = "pillow" },
|
||||
{ name = "pydantic" },
|
||||
{ name = "rich" },
|
||||
{ name = "safetensors" },
|
||||
{ name = "typer" },
|
||||
]
|
||||
|
||||
[package.dev-dependencies]
|
||||
@ -93,12 +86,10 @@ reference = [
|
||||
[package.metadata]
|
||||
requires-dist = [
|
||||
{ name = "mlx", specifier = ">=0.31.0" },
|
||||
{ name = "numpy", specifier = ">=2.4.2" },
|
||||
{ name = "pillow", specifier = ">=12.1.1" },
|
||||
{ name = "pydantic", specifier = ">=2.12.5" },
|
||||
{ name = "rich", specifier = ">=14.3.3" },
|
||||
{ name = "safetensors", specifier = ">=0.7.0" },
|
||||
{ name = "typer", specifier = ">=0.24.1" },
|
||||
{ name = "numpy", specifier = ">=2.0.0" },
|
||||
{ name = "pillow", specifier = ">=10.0.0" },
|
||||
{ name = "rich", specifier = ">=13.0.0" },
|
||||
{ name = "safetensors", specifier = ">=0.4.0" },
|
||||
]
|
||||
|
||||
[package.metadata.requires-dev]
|
||||
@ -122,6 +113,7 @@ dependencies = [
|
||||
{ name = "cuda-pathfinder" },
|
||||
]
|
||||
wheels = [
|
||||
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Loading…
Reference in New Issue
Block a user