CNN v2 training: Add --grayscale-loss option for luminance-based loss computation

Add option to compute loss on grayscale (Y = 0.299*R + 0.587*G + 0.114*B) instead of full RGBA channels. Useful for training models that prioritize luminance accuracy over color accuracy.

Changes:
- training/train_cnn_v2.py: Add --grayscale-loss flag and grayscale conversion in loss computation
- scripts/train_cnn_v2_full.sh: Add --grayscale-loss parameter support
- doc/CNN_V2.md: Document grayscale loss in training configuration and checkpoint format
- doc/HOWTO.md: Add usage examples for --grayscale-loss flag

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

1 files changed, 12 insertions, 2 deletions

diff --git a/doc/CNN_V2.md b/doc/CNN_V2.md
index 78854ce..c827187 100644
--- a/doc/CNN_V2.md
+++ b/doc/CNN_V2.md
@@ -326,12 +326,13 @@ class CNNv2(nn.Module):
 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
+grayscale_loss = False       # Compute loss on grayscale (Y) instead of RGBA
 learning_rate = 1e-3
 batch_size = 16
 epochs = 5000
 
 # Dataset: Input RGB, Target RGBA (preserves alpha channel from image)
-# Model outputs RGBA, loss compares all 4 channels
+# Model outputs RGBA, loss compares all 4 channels (or grayscale if --grayscale-loss)
 
 # Training loop (standard PyTorch f32)
 for epoch in range(epochs):
@@ -344,7 +345,15 @@ for epoch in range(epochs):
 
         # Forward pass
         output = model(input_rgbd, static_feat)
-        loss = criterion(output, target_batch)
+
+        # Loss computation (grayscale or RGBA)
+        if grayscale_loss:
+            # Convert RGBA to grayscale: Y = 0.299*R + 0.587*G + 0.114*B
+            output_gray = 0.299 * output[:, 0:1] + 0.587 * output[:, 1:2] + 0.114 * output[:, 2:3]
+            target_gray = 0.299 * target[:, 0:1] + 0.587 * target[:, 1:2] + 0.114 * target[:, 2:3]
+            loss = criterion(output_gray, target_gray)
+        else:
+            loss = criterion(output, target_batch)
 
         # Backward pass
         optimizer.zero_grad()
@@ -361,6 +370,7 @@ torch.save({
         'kernel_sizes': [3, 3, 3],  # Per-layer kernel sizes
         'num_layers': 3,
         'mip_level': 0,             # Mip level used for p0-p3
+        'grayscale_loss': False,    # Whether grayscale loss was used
         'features': ['p0', 'p1', 'p2', 'p3', 'uv.x', 'uv.y', 'sin10_x', 'bias']
     },
     'epoch': epoch,