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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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Refactor monolithic 866-line CMakeLists.txt into 54-line orchestrator + 10 modules:
- DemoOptions.cmake - Build option declarations
- DemoConfig.cmake - Option implications and platform detection
- DemoCommon.cmake - Shared macros (conditional sources, size opts, linking)
- DemoDependencies.cmake - External library discovery (WGPU, GLFW)
- DemoSourceLists.cmake - Conditional source file lists
- DemoLibraries.cmake - Subsystem library targets
- DemoTools.cmake - Build tools (asset_packer, compilers)
- DemoCodegen.cmake - Code generation (assets, timeline, music)
- DemoExecutables.cmake - Main binaries (demo64k, test_demo)
- DemoTests.cmake - Test infrastructure (36 tests)
- Validation.cmake - Uniform buffer validation
Benefits:
- 94% reduction in main file size (866 → 54 lines)
- Conditional module inclusion (tests only parsed if DEMO_BUILD_TESTS=ON)
- Shared macros eliminate 200+ lines of repetition
- Clear separation of concerns
All 36 tests passing. All build modes verified.
Documentation: Created doc/CMAKE_MODULES.md with module architecture.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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