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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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- cnn_v3/tools/weights.js: new file — base64-encoded cnn_v3_weights.bin +
cnn_v3_film_mlp.bin; loaded at startup so the tool works without dropping files
- tester.js: preload() falls back to embedded weights.js constants when fetch
fails; logs "Loaded embedded" vs "Preloaded" to distinguish the two paths
- index.html: load weights.js before tester.js
- export_cnn_v3_weights.py: add --html / --html-output flags that call
update_weights_js() to regenerate weights.js after a training run
- HOW_TO_CNN.md: update pipeline diagram, §3 export commands, §7 HTML tool
section (file table, workflow, weights.js description), Appendix A
handoff(Gemini): weights.js now the canonical source for HTML tool defaults;
regenerate with `uv run export_cnn_v3_weights.py <ckpt> --output ... --html`
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- Add WEIGHTS_CNN_V3 and WEIGHTS_CNN_V3_FILM_MLP to workspaces/main/assets.txt
- Add opencv-python and pillow to export_cnn_v3_weights.py uv inline deps
- Update HOW_TO_CNN.md §3 export target → workspaces/main/weights/
- Update HOW_TO_CNN.md §4 weight loading → SafeGetAsset (asset system)
handoff(Gemini): cnn_v3 weight assets registered; export and C++ load path documented
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C++:
- cnn_v3_effect.cc: fix declare_nodes comment (output node declared by caller)
- cnn_v3_effect.cc: add TODO(phase-7) marker for FiLM MLP replacement
WGSL:
- cnn_v3_bottleneck.wgsl: consolidate _pad fields onto one line, explain why
array<u32,3> is invalid in uniform address space
- cnn_v3_enc0.wgsl: fix "12xu8" → "12ch u8norm" in header comment
- cnn_v3_dec0.wgsl: clarify parity note (sigmoid after FiLM+ReLU, not raw conv)
- cnn_v3_common.wgsl: clarify unpack_8ch pack layout (low/high 16 bits)
Python:
- cnn_v3_utils.py: replace PIL-based _upsample_nearest (uint8 round-trip) with
pure numpy index arithmetic; rename _resize_rgb → _resize_img (handles any
channel count); add comment on normal zero-pad workaround
- export_cnn_v3_weights.py: add cross-ref to cnn_v3_effect.cc constants;
clarify weight count comments with Conv notation
Test:
- test_cnn_v3_parity.cc: enc0/dec1 layer failures now return 0 (were print-only)
handoff(Gemini): CNN v3 review complete, 36/36 tests passing.
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- export_cnn_v3_weights.py: .pth → cnn_v3_weights.bin (f16 packed u32) + cnn_v3_film_mlp.bin (f32)
- HOW_TO_CNN.md: full pipeline playbook (data collection, training, export, C++ wiring, parity, HTML tool)
- TODO.md: mark export script done
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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