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authorskal <pascal.massimino@gmail.com>2026-03-22 16:21:25 +0100
committerskal <pascal.massimino@gmail.com>2026-03-22 16:21:25 +0100
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tree75f350e4e89003b914e0645e47118f88992d66c0 /TODO.md
parentc8d5c02bae82e506f6bb507c9ed07aeba3a1bb87 (diff)
feat(cnn_v3): add G-buffer visualizer + web sample loader (Phase 7)
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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@@ -79,9 +79,14 @@ PyTorch / HTML WebGPU / C++ WebGPU.
5. ✅ Parity validation: test vectors + `test_cnn_v3_parity.cc`. max_err=4.88e-4 (≤1/255).
- Key fix: intermediate nodes at fractional resolutions (W/2, W/4) via `NodeRegistry::default_width()/default_height()`
-**Next: export + real training run**
-- `train_cnn_v3.py` + `cnn_v3_utils.py` written (Phase 6 training script done)
-- ✅ `export_cnn_v3_weights.py` — convert trained `.pth` → `.bin` (f16)
+6. ✅ Training script: `train_cnn_v3.py` + `cnn_v3_utils.py` written
+ - ✅ `export_cnn_v3_weights.py` — convert trained `.pth` → `.bin` (f16)
+7. ✅ Validation tools:
+ - `GBufViewEffect` — C++ 4×5 channel grid (all 20 G-buffer channels)
+ - Web tool "Load sample directory" — G-buffer pack → CNN inference → PSNR
+ - See `cnn_v3/docs/HOWTO.md` §9
+
+**Next: run a real training pass**
- See `cnn_v3/docs/HOWTO.md` §3 for training commands
## Future: CNN v3 "2D Mode" (G-buffer-free)