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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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Fix two issues causing validation errors in test_demo:
1. Remove redundant pipeline creation without layout (static_pipeline_)
2. Change vec3<u32> to 3× u32 fields in StaticFeatureParams struct
WGSL vec3<u32> aligns to 16 bytes (std140), making struct 32 bytes,
while C++ struct was 16 bytes. Explicit fields ensure consistent layout.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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Update positional encoding to use vertical coordinate at higher frequency.
Changes:
- train_cnn_v2.py: sin10_x → sin20_y (computed from uv_y)
- cnn_v2_static.wgsl: sin10_x → sin20_y (computed from uv_y)
- index.html: sin10_x → sin20_y (STATIC_SHADER)
- CNN_V2.md: Update feature descriptions and examples
- CNN_V2_BINARY_FORMAT.md: Update static features documentation
Feature vector: [p0, p1, p2, p3, uv_x, uv_y, sin20_y, bias]
Rationale: Higher frequency (20 vs 10) + vertical axis provides better
spatial discrimination for position encoding.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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Document future enhancement for arbitrary feature vector layouts.
Proposed feature descriptor in binary format v3:
- Specify feature types, sources, and ordering
- Enable runtime experimentation without shader recompilation
- Examples: [R,G,B,dx,dy,uv_x,bias] or [mip1.r,mip2.g,laplacian,uv_x,sin20_x,bias]
Added TODOs in:
- CNN_V2_BINARY_FORMAT.md: Detailed proposal with struct layout
- CNN_V2.md: Future extensions section
- train_cnn_v2.py: compute_static_features() docstring
- cnn_v2_static.wgsl: Shader header comment
- cnn_v2_effect.cc: Version check comment
Current limitation: Hardcoded [p0,p1,p2,p3,uv_x,uv_y,sin10_x,bias] layout.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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Binary format v2 includes mip_level in header (20 bytes, was 16).
Effect reads mip_level and passes to static features shader via uniform.
Shader samples from correct mip texture based on mip_level.
Changes:
- export_cnn_v2_weights.py: Header v2 with mip_level field
- cnn_v2_effect.h: Add StaticFeatureParams, mip_level member, params buffer
- cnn_v2_effect.cc: Read mip_level from weights, create/bind params buffer, update per-frame
- cnn_v2_static.wgsl: Accept params uniform, sample from selected mip level
Binary format v2:
- Header: 20 bytes (magic, version=2, num_layers, total_weights, mip_level)
- Backward compatible: v1 weights load with mip_level=0
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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Updated comments to clarify that per-layer kernel sizes are supported.
Code already handles this correctly via LayerInfo.kernel_size field.
Changes:
- cnn_v2_effect.h: Add comment about per-layer kernel sizes
- cnn_v2_compute.wgsl: Clarify LayerParams provides per-layer config
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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**Architecture changes:**
- Static features (8D): p0-p3 (parametric) + uv_x, uv_y, sin(10×uv_x), bias
- Input RGBD (4D): fed separately to all layers
- All layers: uniform 12D→4D (4 prev/input + 8 static → 4 output)
- Bias integrated in static features (bias=False in PyTorch)
**Weight calculations:**
- 3 layers × (12 × 3×3 × 4) = 1296 weights
- f16: 2.6 KB (vs old variable arch: ~6.4 KB)
**Updated files:**
*Training (Python):*
- train_cnn_v2.py: Uniform model, takes input_rgbd + static_features
- export_cnn_v2_weights.py: Binary export for storage buffers
- export_cnn_v2_shader.py: Per-layer shader export (debugging)
*Shaders (WGSL):*
- cnn_v2_static.wgsl: p0-p3 parametric features (mips/gradients)
- cnn_v2_compute.wgsl: 12D input, 4D output, vec4 packing
*Tools:*
- HTML tool (cnn_v2_test): Updated for 12D→4D, layer visualization
*Docs:*
- CNN_V2.md: Updated architecture, training, validation sections
- HOWTO.md: Reference HTML tool for validation
*Removed:*
- validate_cnn_v2.sh: Obsolete (used CNN v1 tool)
All code consistent with bias=False (bias in static features as 1.0).
handoff(Claude): CNN v2 architecture finalized and documented
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- Add --cnn-version <1|2> flag to select between CNN v1 and v2
- Implement beat_phase modulation for dynamic blend in both CNN effects
- Fix CNN v2 per-layer uniform buffer sharing (each layer needs own buffer)
- Fix CNN v2 y-axis orientation to match render pass convention
- Add Scene1Effect as base visual layer to test_demo timeline
- Reorganize CNN v2 shaders into cnn_v2/ subdirectory
- Update asset paths and documentation for new shader organization
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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