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Infrastructure for enhanced CNN post-processing with 7D feature input.
Phase 1: Shaders
- Static features compute (RGBD + UV + sin10_x + bias → 8×f16)
- Layer template (convolution skeleton, packing/unpacking)
- 3 mip level support for multi-scale features
Phase 2: C++ Effect
- CNNv2Effect class (multi-pass architecture)
- Texture management (static features, layer buffers)
- Build integration (CMakeLists, assets, tests)
Phase 3: Training Pipeline
- train_cnn_v2.py: PyTorch model with static feature concatenation
- export_cnn_v2_shader.py: f32→f16 quantization, WGSL generation
- Configurable architecture (kernels, channels)
Phase 4: Validation
- validate_cnn_v2.sh: End-to-end pipeline
- Checkpoint → shaders → build → test images
Tests: 36/36 passing
Next: Complete render pipeline implementation (bind groups, multi-pass)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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