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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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- train_cnn_v3.py: CNNv3 U-Net+FiLM model, training loop, CLI
- cnn_v3_utils.py: image I/O, pyrdown, depth_gradient, assemble_features,
apply_channel_dropout, detect_salient_points, CNNv3Dataset
- Patch-based training (default 64×64) with salient-point extraction
(harris/shi-tomasi/fast/gradient/random detectors, pre-cached at init)
- Channel dropout for geometric/context/temporal channels
- Random FiLM conditioning per sample for joint MLP+U-Net training
- docs: HOWTO.md §3 updated with commands and flag reference
- TODO.md: Phase 6 marked done, export script noted as next step
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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- CNN_V3.md: status line, architecture channel counts (8/16→4/8), FiLM MLP
output count (96→40 params), size budget table (real implemented values)
- HOWTO.md: Phase status table (5→done, add phase 6 training TODO), sections
3-5 rewritten to reflect what exists vs what is still planned
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- cnn_v3/src/cnn_v3_effect.{h,cc}: full Effect subclass with 5 compute
passes (enc0→enc1→bottleneck→dec1→dec0), shared weights storage buffer,
per-pass uniform buffers, set_film_params() API
- Fixed WGSL/C++ struct alignment: vec3u has align=16, so CnnV3Params4ch
is 64 bytes and CnnV3ParamsEnc1 is 96 bytes (not 48/80)
- Weight offsets computed as explicit formulas (e.g. 20*4*9+4) for clarity
- Registered in CMake, shaders.h/cc, demo_effects.h, test_demo_effects.cc
- 35/35 tests pass
handoff(Gemini): CNN v3 Phase 5 next — parity validation (Python ref vs WGSL)
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5 compute shaders + cnn_v3/common snippet:
enc0: Conv(20→4,3×3) + FiLM + ReLU full-res
enc1: AvgPool + Conv(4→8,3×3) + FiLM + ReLU half-res
bottleneck: AvgPool + Conv(8→8,1×1) + ReLU quarter-res
dec1: NearestUp + cat(enc1) + Conv(16→4) + FiLM half-res
dec0: NearestUp + cat(enc0) + Conv(8→4) + FiLM + Sigmoid full-res
Parity rules: zero-pad conv, AvgPool down, NearestUp, FiLM after
conv+bias, skip=concat, OIHW weights+bias layout. Matches PyTorch
train_cnn_v3.py forward() exactly.
Registered in workspaces/main/assets.txt + src/effects/shaders.cc.
Weight layout + Params struct documented in cnn_v3/docs/HOWTO.md §7.
Next: Phase 4 — C++ CNNv3Effect + FiLM uniform upload.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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- Wire GBufferEffect into demo build: assets.txt, DemoSourceLists.cmake,
demo_effects.h, shaders.h/cc. ShaderComposer::Compose() applied to
gbuf_raster.wgsl (resolves #include "common_uniforms").
- Add GBufferEffect construction test. 35/35 passing.
- Write cnn_v3/docs/HOWTO.md: G-buffer wiring, training data prep,
training plan, per-pixel validation workflow, phase status table,
troubleshooting guide.
- Add project hooks: remind to update HOWTO.md on cnn_v3/ edits;
warn on direct str_view(*_wgsl) usage bypassing ShaderComposer.
- Update PROJECT_CONTEXT.md and TODO.md: Phase 1 done,
Phase 3 (WGSL U-Net shaders) is next active.
handoff(Gemini): CNN v3 Phase 3 is next - WGSL enc/dec/bottleneck/FiLM
shaders in cnn_v3/shaders/. See cnn_v3/docs/CNN_V3.md Architecture
section and cnn_v3/docs/HOWTO.md section 3 for spec. GBufferEffect
outputs feat_tex0 + feat_tex1 (rgba32uint, 20ch, 32 bytes/pixel).
C++ CNNv3Effect (Phase 4) takes those as input nodes.
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- CNN_V3.md: complete design document
- U-Net enc_channels=[4,8], ~5 KB f16 weights
- FiLM conditioning (5D → γ/β per level, CPU-side MLP)
- 20-channel feature buffer, 32 bytes/pixel: two rgba32uint textures
- feat_tex0: albedo.rgb, normal.xy, depth, depth_grad.xy (f16)
- feat_tex1: mat_id, prev.rgb, mip1.rgb, mip2.rgb, shadow, transp (u8)
- 4-pass G-buffer: raster MRT + SDF compute + lighting + pack
- Per-pixel parity framework: PyTorch / HTML WebGPU / C++ WebGPU (≤1/255)
- Training pipelines: Blender full G-buffer + photo-only (channel dropout)
- train_cnn_v3_full.sh spec (modelled on v2 script)
- HTML tool adaptation plan from cnn_v2/tools/cnn_v2_test/index.html
- Binary format v3 header spec
- 8-phase ordered implementation checklist
- TODO.md: add CNN v3 U-Net+FiLM future task with phases
- cnn_v3/README.md: update status to design phase
handoff(Gemini): CNN v3 design complete. Phase 0 (stub G-buffer) unblocks
all other phases — one compute shader writing feat_tex0+feat_tex1 with
synthetic values from the current framebuffer. See cnn_v3/docs/CNN_V3.md
Implementation Checklist.
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