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Training changes:
- Changed p3 default depth from 0.0 to 1.0 (far plane semantics)
- Extract depth from target alpha channel in both datasets
- Consistent alpha-as-depth across training/validation
Test tool enhancements (cnn_test):
- Added load_depth_from_alpha() for R32Float depth texture
- Fixed bind group layout for UnfilterableFloat sampling
- Added --save-intermediates with per-channel grayscale composites
- Each layer saved as 4x wide PNG (p0-p3 stacked horizontally)
- Global layers_composite.png for vertical layer stack overview
Investigation notes:
- Static features p4-p7 ARE computed and bound correctly
- Sin_20_y pattern visibility difference between tools under investigation
- Binary weights timestamp (Feb 13 20:36) vs HTML tool (Feb 13 22:12)
- Next: Update HTML tool with canonical binary weights
handoff(Claude): HTML tool weights update pending - base64 encoded
canonical weights ready in /tmp/weights_b64.txt for line 392 replacement.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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Moved main.cc, stub_main.cc, and test_demo.cc from src/ to src/app/
for better organization. Updated cmake/DemoExecutables.cmake paths.
handoff(Claude): App files reorganized into src/app/ directory
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1. Loss printed at every epoch with \r (no scrolling)
2. Validation only on final epoch (not all checkpoints)
3. Process all input images (not just img_000.png)
Training output now shows live progress with single line update.
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- Add QAT (quantization-aware training) notes
- Requires training with fake quantization
- Target: ~1.6 KB weights (vs 3.2 KB f16)
- Shader unpacking needs adaptation (4× u8 per u32)
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- Export weights from epoch 70 checkpoint (3.2 KB binary)
- Disable shader template generation (use manual cnn_v2_compute.wgsl)
- Build successful with real weights
- Ready for integration testing
Storage buffer architecture complete:
- Dynamic layer count support
- ~0.3ms overhead vs constants (negligible)
- Single shader, flexible configuration
- Binary format: header + layer info + f16 weights
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- Create bind groups per layer with ping-pong buffers
- Update layer params uniform per dispatch
- Execute all layers in sequence with proper input/output swapping
- Ready for weight export and end-to-end testing
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- Add binary weight format (header + layer info + packed f16)
- New export_cnn_v2_weights.py for binary weight export
- Single cnn_v2_compute.wgsl shader with storage buffer
- Load weights in CNNv2Effect::load_weights()
- Create layer compute pipeline with 5 bindings
- Fast training config: 100 epochs, 3×3 kernels, 8→4→4 channels
Next: Complete bind group creation and multi-layer compute execution
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