CNN v2: Restore per-layer kernel sizes support

Training:
- train_cnn_v2.py: Accept --kernel-sizes as comma-separated list
- CNNv2 model: Per-layer kernel sizes (e.g., [1,3,5])
- Single value replicates across layers (e.g., "3" → [3,3,3])

Export:
- export_cnn_v2_weights.py: Backward compatible with old checkpoints
- Handles both kernel_size (old) and kernel_sizes (new) format

Documentation:
- CNN_V2.md: Updated code examples and config format
- HOWTO.md: Updated training examples to show comma-separated syntax

Binary format: Already supports per-layer kernel sizes (no changes)

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

1 files changed, 14 insertions, 4 deletions

diff --git a/training/export_cnn_v2_weights.py b/training/export_cnn_v2_weights.py
index 07254fc..bbe94dd 100755
--- a/training/export_cnn_v2_weights.py
+++ b/training/export_cnn_v2_weights.py
@@ -45,11 +45,20 @@ def export_weights_binary(checkpoint_path, output_path):
     state_dict = checkpoint['model_state_dict']
     config = checkpoint['config']
 
-    kernel_size = config.get('kernel_size', 3)
-    num_layers = config.get('num_layers', 3)
+    # Support both old (kernel_size) and new (kernel_sizes) format
+    if 'kernel_sizes' in config:
+        kernel_sizes = config['kernel_sizes']
+    elif 'kernel_size' in config:
+        kernel_size = config['kernel_size']
+        num_layers = config.get('num_layers', 3)
+        kernel_sizes = [kernel_size] * num_layers
+    else:
+        kernel_sizes = [3, 3, 3]  # fallback
+
+    num_layers = config.get('num_layers', len(kernel_sizes))
 
     print(f"Configuration:")
-    print(f"  Kernel size: {kernel_size}×{kernel_size}")
+    print(f"  Kernel sizes: {kernel_sizes}")
     print(f"  Layers: {num_layers}")
     print(f"  Architecture: uniform 12D→4D (bias=False)")
 
@@ -65,6 +74,7 @@ def export_weights_binary(checkpoint_path, output_path):
 
         layer_weights = state_dict[layer_key].detach().numpy()
         layer_flat = layer_weights.flatten()
+        kernel_size = kernel_sizes[i]
 
         layers.append({
             'kernel_size': kernel_size,
@@ -76,7 +86,7 @@ def export_weights_binary(checkpoint_path, output_path):
         all_weights.extend(layer_flat)
         weight_offset += len(layer_flat)
 
-        print(f"  Layer {i}: 12D→4D, {len(layer_flat)} weights")
+        print(f"  Layer {i}: 12D→4D, {kernel_size}×{kernel_size}, {len(layer_flat)} weights")
 
     # Convert to f16
     # TODO: Use 8-bit quantization for 2× size reduction