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- Add test_cnn_v3_parity.cc: zero_weights + random_weights tests
- Add gen_test_vectors.py: PyTorch reference implementation for enc0/enc1/bn/dec1/dec0
- Add test_vectors.h: generated C header with enc0, dec1, output expected values
- Fix declare_nodes(): intermediate textures at fractional resolutions (W/2, W/4)
using new NodeRegistry::default_width()/default_height() getters
- Add layer-by-layer readback (enc0, dec1) for regression coverage
- Final parity: enc0 max_err=1.95e-3, dec1 max_err=1.95e-3, out max_err=4.88e-4
handoff(Claude): CNN v3 parity done. Next: train_cnn_v3.py (FiLM MLP training).
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- cnn_v3/src/cnn_v3_effect.{h,cc}: full Effect subclass with 5 compute
passes (enc0→enc1→bottleneck→dec1→dec0), shared weights storage buffer,
per-pass uniform buffers, set_film_params() API
- Fixed WGSL/C++ struct alignment: vec3u has align=16, so CnnV3Params4ch
is 64 bytes and CnnV3ParamsEnc1 is 96 bytes (not 48/80)
- Weight offsets computed as explicit formulas (e.g. 20*4*9+4) for clarity
- Registered in CMake, shaders.h/cc, demo_effects.h, test_demo_effects.cc
- 35/35 tests pass
handoff(Gemini): CNN v3 Phase 5 next — parity validation (Python ref vs WGSL)
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