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- train_cnn_v3.py: CNNv3 U-Net+FiLM model, training loop, CLI
- cnn_v3_utils.py: image I/O, pyrdown, depth_gradient, assemble_features,
apply_channel_dropout, detect_salient_points, CNNv3Dataset
- Patch-based training (default 64×64) with salient-point extraction
(harris/shi-tomasi/fast/gradient/random detectors, pre-cached at init)
- Channel dropout for geometric/context/temporal channels
- Random FiLM conditioning per sample for joint MLP+U-Net training
- docs: HOWTO.md §3 updated with commands and flag reference
- TODO.md: Phase 6 marked done, export script noted as next step
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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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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G-buffer (Phase 1):
- Add NodeTypes GBUF_ALBEDO/DEPTH32/R8/RGBA32UINT to NodeRegistry
- GBufferEffect: MRT raster pass (albedo+normal_mat+depth) + pack compute
- Shaders: gbuf_raster.wgsl (MRT), gbuf_pack.wgsl (feature packing, 32B/px)
- Shadow/SDF passes stubbed (placeholder textures), CMake integration deferred
Training infrastructure (Phase 2):
- blender_export.py: headless EXR export with all G-buffer render passes
- pack_blender_sample.py: EXR → per-channel PNGs (oct-normals, 1/z depth)
- pack_photo_sample.py: photo → zero-filled G-buffer sample layout
handoff(Gemini): G-buffer phases 3-5 remain (U-Net shaders, CNNv3Effect, parity)
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Initialize CNN v3 subdirectory with training pipeline layout:
- docs/, scripts/, shaders/, src/, tools/, weights/ for organization
- training/input/ with sample images
- training/target_1/, target_2/ for multi-style training
- README.md documenting structure
Training images tracked in repo for easy collaboration.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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