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- --patch-search-window N: at dataset init, find per-patch (dx,dy) in
[-N,N]² that minimises grayscale MSE between source albedo and target;
result cached so __getitem__ pays only a list-lookup per sample.
- --resume [CKPT]: restore model + Adam state from a checkpoint; omit
path to auto-select the latest in --checkpoint-dir.
- Ctrl-C (SIGINT) finishes the current batch, then saves a checkpoint
before exiting; finally-block guarded so no spurious epoch-0 save.
- Review: remove unused sd variable, lift patch_idx out of duplicate
computation, move _LUMA to Constants block, update module docstring.
handoff(Gemini): cnn_v3/training updated — no C++ or test changes.
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handoff(Gemini): CNNv3Dataset now loads all samples once in __init__ into
self._cache; __getitem__ reads from cache instead of reloading PNGs each
call. Eliminates N×patches_per_image file loads per epoch.
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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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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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- 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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