CNN v2: Use alpha channel for p3 depth feature + layer visualization

Training changes (train_cnn_v2.py):
- p3 now uses target image alpha channel (depth proxy for 2D images)
- Default changed from 0.0 → 1.0 (far plane semantics)
- Both PatchDataset and ImagePairDataset updated

Test tools (cnn_test.cc):
- New load_depth_from_alpha() extracts PNG alpha → p3 texture
- Fixed bind group layout: use UnfilterableFloat for R32Float depth
- Added --save-intermediates support for CNN v2:
  * Each layer_N.png shows 4 channels horizontally (1812×345 grayscale)
  * layers_composite.png stacks all layers vertically (1812×1380)
  * static_features.png shows 4 feature channels horizontally
- Per-channel visualization enables debugging layer-by-layer differences

HTML tool (index.html):
- Extract alpha channel from input image → depth texture
- Matches training data distribution for validation

Note: Current weights trained with p3=0 are now mismatched. Both tools
use p3=alpha consistently, so outputs remain comparable for debugging.
Retrain required for optimal quality.

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

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