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architecture PNG
- Replace 1×1 pointwise bottleneck with Conv(8→8, 3×3, dilation=2):
effective RF grows from ~13px to ~29px at ¼res (~+1 KB weights)
- Add Sobel edge loss in training (--edge-loss-weight, default 0.1)
- Add FiLM 2-phase training: freeze MLP for warmup epochs then
unfreeze at lr×0.1 (--film-warmup-epochs, default 50)
- Update weight layout: BN 72→584 f16, total 1964→2476 f16 (4952 B)
- Cascade offsets in C++ effect, JS tool, export/gen_test_vectors scripts
- Regenerate test_vectors.h (1238 u32); parity max_err=9.77e-04
- Generate dark-theme U-Net+FiLM architecture PNG (gen_architecture_png.py)
- Replace ASCII art in CNN_V3.md and HOW_TO_CNN.md with PNG embed
handoff(Gemini): bottleneck dilation + Sobel loss + FiLM warmup landed.
Next: run first real training pass (see cnn_v3/docs/HOWTO.md §3).
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Replace raw shadow (ch18) with dif = max(0,dot(normal,KEY_LIGHT))*shadow
across all layers. Channel count stays 20, weight shapes unchanged.
- gbuf_pack.wgsl: t1.z = pack4x8unorm(mip2.g, mip2.b, dif, transp); t1.w = 0u
- gbuf_deferred.wgsl: read dif from unpack4x8unorm(t1.z).z
- gbuf_view.wgsl: revert to 4×5 grid, ch18=dif label, ch19=trns label
- tools/shaders.js: FULL_PACK_SHADER adds oct_decode + computes dif
- cnn_v3_utils.py: assemble_features() computes dif on-the-fly via oct_decode
- docs: CNN_V3.md, HOWTO.md, HOW_TO_CNN.md, GBUF_DIF_MIGRATION.md updated
handoff(Gemini): shadow→dif migration done, ready for first training pass
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WebGPU auto-reflects the BGL from the shader; a declared-but-unused sampler
binding is omitted from the layout, causing CreateBindGroup to reject it.
Removed binding 6 (sampler) entirely — all reads use textureLoad(). Renumbered
f0/f1 from 7/8 to 6/7 to match.
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C++ GBufViewEffect: renders all 20 feature channels from feat_tex0/feat_tex1
in a 4×5 tiled grid. Custom BGL with WGPUTextureSampleType_Uint; bind group
rebuilt per frame via wgpuRenderPipelineGetBindGroupLayout.
Web tool: "Load sample directory" button — webkitdirectory picker, FULL_PACK_SHADER
compute (matches gbuf_pack.wgsl packing), runFromFeat() skips photo-pack step,
computePSNR() readback + comparison vs target.png side-by-side.
36/36 tests pass. Docs updated: HOWTO.md §9, README, PROJECT_CONTEXT, TODO,
COMPLETED.
handoff(Gemini): CNN v3 Phase 7 done. Next: run train_cnn_v3.py (see HOWTO §3).
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3-file tool, 939 lines total. Implements full U-Net+FiLM inference in
the browser: Pack→Enc0→Enc1→Bottleneck→Dec1→Dec0 compute passes,
layer visualisation (Feat/Enc0/Enc1/BN/Dec1/Output), FiLM MLP sliders,
drag-drop weights + image/video, Save PNG, diff/blend view modes.
HOW_TO_CNN.md §7 updated to reflect tool is implemented.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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