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- Add binary weight format (header + layer info + packed f16)
- New export_cnn_v2_weights.py for binary weight export
- Single cnn_v2_compute.wgsl shader with storage buffer
- Load weights in CNNv2Effect::load_weights()
- Create layer compute pipeline with 5 bindings
- Fast training config: 100 epochs, 3×3 kernels, 8→4→4 channels
Next: Complete bind group creation and multi-layer compute execution
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Complete multi-pass compute execution for CNNv2Effect.
Implementation:
- Layer texture creation (ping-pong buffers for intermediate results)
- Static features compute pipeline with bind group layout
- Bind group creation with 5 bindings (input mips + depth + output)
- compute() override for multi-pass execution
- Static features pass with proper workgroup dispatch
Architecture:
- Static features: 8×f16 packed as 4×u32 (RGBD + UV + sin + bias)
- Layer buffers: 2×RGBA32Uint textures (8 channels f16 each)
- Input mips: 3 levels (0, 1, 2) for multi-scale features
- Workgroup size: 8×8 threads
Status:
- Static features compute pass functional
- Layer pipeline infrastructure ready
- All 36/36 tests passing
Next: Layer shader integration, multi-layer execution
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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