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Double encoder capacity: enc0 4→8ch, enc1 8→16ch, bottleneck 16→16ch,
dec1 32→8ch, dec0 16→4ch. Total weights 2476→7828 f16 (~15.3 KB).
FiLM MLP output 40→72 params (L1: 16×40→16×72).
16-ch textures split into _lo/_hi rgba32uint pairs (enc1, bottleneck).
enc0 and dec1 textures changed from rgba16float to rgba32uint (8ch).
GBUF_RGBA32UINT node gains CopySrc for parity test readback.
- WGSL shaders: all 5 passes rewritten for new channel counts
- C++ CNNv3Effect: new weight offsets/sizes, 8ch uniform structs
- Web tool (shaders.js + tester.js): matching texture formats and bindings
- Parity test: readback_rgba32uint_8ch helper, updated vector counts
- Training scripts: default enc_channels=[8,16], updated docstrings
- Docs + architecture PNG regenerated
handoff(Gemini): CNN v3 [8,16] upgrade complete. All code, tests, web
tool, training scripts, and docs updated. Next: run training pass.
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- cnn_v3/training/infer_cnn_v3.py: PyTorch inference tool; simple mode
(single PNG, zeroed geometry) and full mode (sample directory); supports
--identity-film (γ=1 β=0) to match C++ default, --cond for FiLM MLP,
--blend, --debug-hex for pixel comparison
- tools/cnn_test.cc: full rewrite, v3 only; packs 20-channel features
on CPU (training format: [0,1] oct normals, pyrdown mip), uploads to
GPU, runs CNNv3Effect, reads back RGBA16Float, saves PNG; --sample-dir
for full G-buffer input, --weights for .bin override, --debug-hex
- cmake/DemoTests.cmake: add cnn_v3/src include path, drop unused
offscreen_render_target.cc from cnn_test sources
- cnn_v3/docs/HOWTO.md: new §10 documenting both tools, comparison
workflow, and feature-format convention (training vs runtime)
handoff(Gemini): cnn_test + infer_cnn_v3.py ready for parity testing.
Run both with --identity-film / --debug-hex on same image to compare.
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