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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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handoff(Gemini): train_cnn_v3.py now has uv script metadata block
(torch, torchvision, numpy, pillow, opencv-python). HOW_TO_CNN §2
Prerequisites updated with uv quick-start alternative.
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target.png can have a different resolution than albedo.png in simple
samples; patch slicing into the smaller target produced 0×0 tensors,
crashing torch.stack in the DataLoader collate.
handoff(Gemini): target resized in _load_sample (LANCZOS) + note in HOW_TO_CNN §1c.
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handoff(Gemini): added render + batch-pack example commands at end of section 1b
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blender_export.py:
- Replace broken compositor FileOutput approach with native OPEN_EXR_MULTILAYER
render output; all enabled passes included automatically, no socket wiring needed
- Suppress Fra:/Mem: render spam via os.dup2 fd redirect; per-frame progress
printed to stderr via render_post handler
pack_blender_sample.py:
- get_pass_r: try .R/.X/.Y/.Z/'' suffixes + aliases param for Depth→Z fallback
- combined_rgba loaded once via ("Combined","Image") loop; shared by transp+target
- Remove unused sys import
HOW_TO_CNN.md: update channel table to native EXR naming (Depth.Z, IndexOB.X,
Shadow.X), fix example command, note Shadow defaults to 255 when absent
handoff(Gemini): blender pipeline now produces correct multilayer EXR with all
G-buffer passes; pack script handles native channel naming
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Fixes KeyError when blend file uses a non-default view layer name.
Adds --view-layer NAME arg; pass '?' to list available layers.
Defaults to index 0 with a clear error if the name is not found.
handoff(Gemini): blender_export.py view layer selection now robust
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- pack_photo_sample.py: --target now required (no albedo fallback)
- gen_sample.py: bash wrapper with positional args (input target output_dir)
- input/photo7.jpg: copy of photo2 (second style target)
- target_1: photo2_1_out→photo2_out, photo2_2_out→photo7_out
- dataset/simple/sample_001..007: 7 packed photo/target pairs
handoff(Gemini): training data ready; next step is train_cnn_v3.py run
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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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- 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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