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>

1 files changed, 11 insertions, 5 deletions

diff --git a/training/train_cnn_v2.py b/training/train_cnn_v2.py
index abe07bc..70229ce 100755
--- a/training/train_cnn_v2.py
+++ b/training/train_cnn_v2.py
@@ -26,13 +26,13 @@ def compute_static_features(rgb, depth=None, mip_level=0):
 
     Args:
         rgb: (H, W, 3) RGB image [0, 1]
-        depth: (H, W) depth map [0, 1], optional
+        depth: (H, W) depth map [0, 1], optional (defaults to 1.0 = far plane)
         mip_level: Mip level for p0-p3 (0=original, 1=half, 2=quarter, 3=eighth)
 
     Returns:
         (H, W, 8) static features: [p0, p1, p2, p3, uv_x, uv_y, sin20_y, bias]
 
-    Note: p0-p3 are parametric features generated from specified mip level
+    Note: p0-p3 are parametric features from mip level. p3 uses depth (alpha channel) or 1.0
 
     TODO: Binary format should support arbitrary layout and ordering for feature vector (7D),
           alongside mip-level indication. Current layout is hardcoded as:
@@ -61,7 +61,7 @@ def compute_static_features(rgb, depth=None, mip_level=0):
     p0 = mip_rgb[:, :, 0].astype(np.float32)
     p1 = mip_rgb[:, :, 1].astype(np.float32)
     p2 = mip_rgb[:, :, 2].astype(np.float32)
-    p3 = depth if depth is not None else np.zeros((h, w), dtype=np.float32)
+    p3 = depth if depth is not None else np.ones((h, w), dtype=np.float32)  # Default 1.0 = far plane
 
     # UV coordinates (normalized [0, 1])
     uv_x = np.linspace(0, 1, w)[None, :].repeat(h, axis=0).astype(np.float32)
@@ -244,8 +244,11 @@ class PatchDataset(Dataset):
         input_patch = input_img[y1:y2, x1:x2]
         target_patch = target_img[y1:y2, x1:x2]  # RGBA
 
+        # Extract depth from target alpha channel (or default to 1.0)
+        depth = target_patch[:, :, 3] if target_patch.shape[2] == 4 else None
+
         # Compute static features for patch
-        static_feat = compute_static_features(input_patch.astype(np.float32), mip_level=self.mip_level)
+        static_feat = compute_static_features(input_patch.astype(np.float32), depth=depth, mip_level=self.mip_level)
 
         # Input RGBD (mip 0) - add depth channel
         input_rgbd = np.concatenate([input_patch, np.zeros((self.patch_size, self.patch_size, 1))], axis=-1)
@@ -284,8 +287,11 @@ class ImagePairDataset(Dataset):
         input_img = np.array(input_pil) / 255.0
         target_img = np.array(target_pil.convert('RGBA')) / 255.0  # Preserve alpha
 
+        # Extract depth from target alpha channel (or default to 1.0)
+        depth = target_img[:, :, 3] if target_img.shape[2] == 4 else None
+
         # Compute static features
-        static_feat = compute_static_features(input_img.astype(np.float32), mip_level=self.mip_level)
+        static_feat = compute_static_features(input_img.astype(np.float32), depth=depth, mip_level=self.mip_level)
 
         # Input RGBD (mip 0) - add depth channel
         h, w = input_img.shape[:2]