Script + pytest tests to verify MLX inference works at CorridorKey's
training resolution. Uses samples/ by default, synthetic fallback.
Reports timing, peak memory, output diagnostics.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Drop-in MLX backend for main CorridorKey repo. Engine class wraps
existing model/inference with process_frame() API returning alpha,
fg, comp, processed as uint8 numpy arrays. Lowers Python to >=3.11,
removes unused deps, adds smoke script and 16 contract tests.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Cache nn.Upsample instances in DecoderHead/GreenFormer __init__
(eliminated ~7 allocations per forward pass)
- Add mx.compile() support via load_model(compile=True)
- Benchmark harness: eager vs compiled, multi-resolution, parity checks
- Tiled inference with overlap blending for large images
- Profiling utilities with forced mx.eval for accurate timing
- Reference comparison script (scripts/compare_reference.py)
- 12 new tests (compiled consistency + tiling)
- README performance section with Apple Silicon guidance
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Wire GreenFormer (backbone + decoders + refiner), add image I/O,
inference pipeline, CLI entry point, and 3 test files (23 new tests).
E2e parity vs PyTorch: max abs err < 1.5e-4 across all outputs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
