CNN v2: Add --mip-level option for parametric features

Add mip level control for p0-p3 features (0=original, 1=half, 2=quarter, 3=eighth).
Uses pyrDown/pyrUp for proper Gaussian filtering during mip generation.

Changes:
- compute_static_features(): Accept mip_level param, generate mip via cv2 pyramid
- PatchDataset/ImagePairDataset: Pass mip_level to feature computation
- CLI: Add --mip-level arg with choices [0,1,2,3]
- Save mip_level in checkpoint config for tracking
- Doc updates: HOWTO.md and CNN_V2.md

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

2 files changed, 30 insertions, 6 deletions

diff --git a/doc/CNN_V2.md b/doc/CNN_V2.md
index e56b022..49086ca 100644
--- a/doc/CNN_V2.md
+++ b/doc/CNN_V2.md
@@ -245,12 +245,28 @@ fn pack_channels(values: vec4<f32>) -> vec4<u32> {
 **Static Feature Extraction:**
 
 ```python
-def compute_static_features(rgb, depth):
-    """Generate parametric features (8D: p0-p3 + spatial)."""
+def compute_static_features(rgb, depth, mip_level=0):
+    """Generate parametric features (8D: p0-p3 + spatial).
+
+    Args:
+        mip_level: 0=original, 1=half res, 2=quarter res, 3=eighth res
+    """
     h, w = rgb.shape[:2]
 
-    # Parametric features (example: use input RGBD, but could be mips/gradients)
-    p0, p1, p2, p3 = rgb[..., 0], rgb[..., 1], rgb[..., 2], depth
+    # Generate mip level for p0-p3 (downsample then upsample)
+    if mip_level > 0:
+        mip_rgb = rgb.copy()
+        for _ in range(mip_level):
+            mip_rgb = cv2.pyrDown(mip_rgb)
+        for _ in range(mip_level):
+            mip_rgb = cv2.pyrUp(mip_rgb)
+        if mip_rgb.shape[:2] != (h, w):
+            mip_rgb = cv2.resize(mip_rgb, (w, h), interpolation=cv2.INTER_LINEAR)
+    else:
+        mip_rgb = rgb
+
+    # Parametric features from mip level
+    p0, p1, p2, p3 = mip_rgb[..., 0], mip_rgb[..., 1], mip_rgb[..., 2], depth
 
     # UV coordinates (normalized)
     uv_x = np.linspace(0, 1, w)[None, :].repeat(h, axis=0)
@@ -308,6 +324,7 @@ class CNNv2(nn.Module):
 # Hyperparameters
 kernel_sizes = [3, 3, 3]     # Per-layer kernel sizes (e.g., [1,3,5])
 num_layers = 3               # Number of CNN layers
+mip_level = 0                # Mip level for p0-p3: 0=orig, 1=half, 2=quarter, 3=eighth
 learning_rate = 1e-3
 batch_size = 16
 epochs = 5000
@@ -318,8 +335,8 @@ epochs = 5000
 # Training loop (standard PyTorch f32)
 for epoch in range(epochs):
     for rgb_batch, depth_batch, target_batch in dataloader:
-        # Compute static features (8D)
-        static_feat = compute_static_features(rgb_batch, depth_batch)
+        # Compute static features (8D) with mip level
+        static_feat = compute_static_features(rgb_batch, depth_batch, mip_level)
 
         # Input RGBD (4D)
         input_rgbd = torch.cat([rgb_batch, depth_batch.unsqueeze(1)], dim=1)
@@ -342,6 +359,7 @@ torch.save({
     'config': {
         'kernel_sizes': [3, 3, 3],  # Per-layer kernel sizes
         'num_layers': 3,
+        'mip_level': 0,             # Mip level used for p0-p3
         'features': ['p0', 'p1', 'p2', 'p3', 'uv.x', 'uv.y', 'sin10_x', 'bias']
     },
     'epoch': epoch,
diff --git a/doc/HOWTO.md b/doc/HOWTO.md
index 9c67106..9003fe1 100644
--- a/doc/HOWTO.md
+++ b/doc/HOWTO.md
@@ -166,6 +166,12 @@ Config: 100 epochs, 3×3 kernels, 8→4→4 channels, patch-based (harris detect
   --input training/input/ --target training/target_2/ \
   --kernel-sizes 1,3,5 \
   --epochs 5000 --batch-size 16
+
+# Mip-level for p0-p3 features (0=original, 1=half, 2=quarter, 3=eighth)
+./training/train_cnn_v2.py \
+  --input training/input/ --target training/target_2/ \
+  --mip-level 1 \
+  --epochs 100 --batch-size 16
 ```
 
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