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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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- 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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- pack_photo_sample.py: --target now required (no albedo fallback)
- gen_sample: 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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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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- 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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- Add test_cnn_v3_parity.cc: zero_weights + random_weights tests
- Add gen_test_vectors.py: PyTorch reference implementation for enc0/enc1/bn/dec1/dec0
- Add test_vectors.h: generated C header with enc0, dec1, output expected values
- Fix declare_nodes(): intermediate textures at fractional resolutions (W/2, W/4)
using new NodeRegistry::default_width()/default_height() getters
- Add layer-by-layer readback (enc0, dec1) for regression coverage
- Final parity: enc0 max_err=1.95e-3, dec1 max_err=1.95e-3, out max_err=4.88e-4
handoff(Claude): CNN v3 parity done. Next: train_cnn_v3.py (FiLM MLP training).
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G-buffer (Phase 1):
- Add NodeTypes GBUF_ALBEDO/DEPTH32/R8/RGBA32UINT to NodeRegistry
- GBufferEffect: MRT raster pass (albedo+normal_mat+depth) + pack compute
- Shaders: gbuf_raster.wgsl (MRT), gbuf_pack.wgsl (feature packing, 32B/px)
- Shadow/SDF passes stubbed (placeholder textures), CMake integration deferred
Training infrastructure (Phase 2):
- blender_export.py: headless EXR export with all G-buffer render passes
- pack_blender_sample.py: EXR → per-channel PNGs (oct-normals, 1/z depth)
- pack_photo_sample.py: photo → zero-filled G-buffer sample layout
handoff(Gemini): G-buffer phases 3-5 remain (U-Net shaders, CNNv3Effect, parity)
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Initialize CNN v3 subdirectory with training pipeline layout:
- docs/, scripts/, shaders/, src/, tools/, weights/ for organization
- training/input/ with sample images
- training/target_1/, target_2/ for multi-style training
- README.md documenting structure
Training images tracked in repo for easy collaboration.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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