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**Architecture changes:**
- Static features (8D): p0-p3 (parametric) + uv_x, uv_y, sin(10×uv_x), bias
- Input RGBD (4D): fed separately to all layers
- All layers: uniform 12D→4D (4 prev/input + 8 static → 4 output)
- Bias integrated in static features (bias=False in PyTorch)
**Weight calculations:**
- 3 layers × (12 × 3×3 × 4) = 1296 weights
- f16: 2.6 KB (vs old variable arch: ~6.4 KB)
**Updated files:**
*Training (Python):*
- train_cnn_v2.py: Uniform model, takes input_rgbd + static_features
- export_cnn_v2_weights.py: Binary export for storage buffers
- export_cnn_v2_shader.py: Per-layer shader export (debugging)
*Shaders (WGSL):*
- cnn_v2_static.wgsl: p0-p3 parametric features (mips/gradients)
- cnn_v2_compute.wgsl: 12D input, 4D output, vec4 packing
*Tools:*
- HTML tool (cnn_v2_test): Updated for 12D→4D, layer visualization
*Docs:*
- CNN_V2.md: Updated architecture, training, validation sections
- HOWTO.md: Reference HTML tool for validation
*Removed:*
- validate_cnn_v2.sh: Obsolete (used CNN v1 tool)
All code consistent with bias=False (bias in static features as 1.0).
handoff(Claude): CNN v2 architecture finalized and documented
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- Add --cnn-version <1|2> flag to select between CNN v1 and v2
- Implement beat_phase modulation for dynamic blend in both CNN effects
- Fix CNN v2 per-layer uniform buffer sharing (each layer needs own buffer)
- Fix CNN v2 y-axis orientation to match render pass convention
- Add Scene1Effect as base visual layer to test_demo timeline
- Reorganize CNN v2 shaders into cnn_v2/ subdirectory
- Update asset paths and documentation for new shader organization
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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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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