feat: CNN RGBD→grayscale with 7-channel augmented input

Upgrade CNN architecture to process RGBD input, output grayscale, with
7-channel layer inputs (RGBD + UV coords + grayscale).

Architecture changes:
- Inner layers: Conv2d(7→4) output RGBD
- Final layer: Conv2d(7→1) output grayscale
- All inputs normalized to [-1,1] for tanh activation
- Removed CoordConv2d in favor of unified 7-channel input

Training (train_cnn.py):
- SimpleCNN: 7→4 (inner), 7→1 (final) architecture
- Forward: Normalize RGBD/coords/gray to [-1,1]
- Weight export: array<array<f32, 8>, 36> (inner), array<f32, 8>, 9> (final)
- Dataset: Load RGBA (RGBD) input

Shaders (cnn_conv3x3.wgsl):
- Added cnn_conv3x3_7to4: 7-channel input → RGBD output
- Added cnn_conv3x3_7to1: 7-channel input → grayscale output
- Both normalize inputs and use flattened weight arrays

Documentation:
- CNN_EFFECT.md: Updated architecture, training, weight format
- CNN_RGBD_GRAYSCALE_SUMMARY.md: Implementation summary
- HOWTO.md: Added training command example

Next: Train with RGBD input data

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

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