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Training:
- train_cnn_v2.py: Accept --kernel-sizes as comma-separated list
- CNNv2 model: Per-layer kernel sizes (e.g., [1,3,5])
- Single value replicates across layers (e.g., "3" → [3,3,3])
Export:
- export_cnn_v2_weights.py: Backward compatible with old checkpoints
- Handles both kernel_size (old) and kernel_sizes (new) format
Documentation:
- CNN_V2.md: Updated code examples and config format
- HOWTO.md: Updated training examples to show comma-separated syntax
Binary format: Already supports per-layer kernel sizes (no changes)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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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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- Rename 'Static (L0)' → 'Static' (clearer, less confusing)
- Update channel labels: 'R/G/B/D' → 'Ch0 (R)/Ch1 (G)/Ch2 (B)/Ch3 (D)'
- Add 'Layer' prefix in weights table for consistency
- Document layer indexing: Static + Layer 1,2,3... (UI) ↔ weights.layers[0,1,2...]
- Add explanatory notes about 7D input and 4-of-8 channel display
- Create doc/CNN_V2_BINARY_FORMAT.md with complete .bin specification
- Cross-reference spec in CNN_V2.md and CNN_V2_WEB_TOOL.md
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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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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Updated:
- HOWTO.md: Complete pipeline, storage buffer, --validate mode
- TODO.md: Mark CNN v2 complete, add QAT TODO
- PROJECT_CONTEXT.md: Update Effects status
- CNN_V2.md: Mark complete, add storage buffer notes
- train_cnn_v2_full.sh: Add --help message
All documentation now reflects:
- Storage buffer architecture
- Binary weight format
- Live training progress
- Validation-only mode
- 8-bit quantization TODO
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Design document for CNN v2 with enhanced feature inputs:
- 7D static features: RGBD + UV + sin encoding + bias
- Per-layer configurable kernels (1×1, 3×3, 5×5)
- Float16 weight storage (~6.4 KB vs 3.2 KB)
- Multi-pass architecture with static feature compute
Implementation plan:
1. Static features compute shader (RGBD + UV + sin + bias)
2. C++ effect class (CNNv2Effect)
3. Training pipeline (train_cnn_v2.py, export_cnn_v2_shader.py)
4. Validation tooling (validate_cnn_v2.sh)
Files:
- doc/CNN_V2.md: Complete technical design (architecture, training, export)
- scripts/validate_cnn_v2.sh: End-to-end validation script
- TODO.md: Add CNN v2 as Priority 2 task
- doc/HOWTO.md: Add CNN v2 validation usage
Target: <10 KB for 64k demo constraint
handoff(Claude): CNN v2 design ready for implementation
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