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Diffstat (limited to 'doc/CNN_V2.md')
| -rw-r--r-- | doc/CNN_V2.md | 11 |
1 files changed, 5 insertions, 6 deletions
diff --git a/doc/CNN_V2.md b/doc/CNN_V2.md index fa00b32..2d1d4c4 100644 --- a/doc/CNN_V2.md +++ b/doc/CNN_V2.md @@ -20,14 +20,13 @@ CNN v2 extends the original CNN post-processing effect with parametric static fe - Binary weight format v2 for runtime loading - Sigmoid activation for layer 0 and final layer (smooth [0,1] mapping) -**Status:** ✅ Complete. Training pipeline functional, validation tools ready, mip-level support integrated. +**Status:** ✅ Complete. Sigmoid activation, stable training, validation tools operational. -**Known Issues:** -- ⚠️ **Old checkpoints incompatible** - Models trained with `clamp()` activation won't work correctly with new `sigmoid()` implementation. Retrain from scratch with latest code. +**Breaking Change:** +- Models trained with `clamp()` incompatible. Retrain required. **TODO:** - 8-bit quantization with QAT for 2× size reduction (~1.6 KB) -- Debug cnn_test vs HTML tool output difference --- @@ -339,7 +338,7 @@ class CNNv2(nn.Module): # Layer 0: input RGBD (4D) + static (8D) = 12D x = torch.cat([input_rgbd, static_features], dim=1) x = self.layers[0](x) - x = torch.clamp(x, 0, 1) # Output layer 0 (4 channels) + x = torch.sigmoid(x) # Soft [0,1] for layer 0 # Layer 1+: previous output (4D) + static (8D) = 12D for i in range(1, len(self.layers)): @@ -348,7 +347,7 @@ class CNNv2(nn.Module): if i < len(self.layers) - 1: x = F.relu(x) else: - x = torch.clamp(x, 0, 1) # Final output [0,1] + x = torch.sigmoid(x) # Soft [0,1] for final layer return x # RGBA output ``` |