From 47397444b30b0f461b1633297a68300179586fda Mon Sep 17 00:00:00 2001 From: skal Date: Tue, 10 Feb 2026 08:01:25 +0100 Subject: feat: Add CNN post-processing effect with modular WGSL architecture MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Implements multi-layer convolutional neural network shader for stylized post-processing of 3D rendered scenes: **Core Components:** - CNNEffect: C++ effect class with single-layer rendering (expandable to multi-pass) - Modular WGSL snippets: cnn_activation, cnn_conv3x3/5x5/7x7, cnn_weights_generated - Placeholder identity-like weights for initial testing (to be replaced by trained weights) **Architecture:** - Flexible kernel sizes (3×3, 5×5, 7×7) via separate snippet files - ShaderComposer integration (#include resolution) - Residual connections (input + processed output) - Supports parallel convolutions (design ready, single conv implemented) **Size Impact:** - ~3-4 KB shader code (snippets + main shader) - ~2-4 KB weights (depends on network architecture when trained) - Total: ~5-8 KB (acceptable for 64k demo) **Testing:** - CNNEffect added to test_demo_effects.cc - 36/36 tests passing (100%) **Next Steps:** - Training script (scripts/train_cnn.py) to generate real weights - Multi-layer rendering with ping-pong textures - Weight quantization for size optimization handoff(Claude): CNN effect foundation complete, ready for training integration --- workspaces/main/shaders/cnn/cnn_weights_generated.wgsl | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) create mode 100644 workspaces/main/shaders/cnn/cnn_weights_generated.wgsl (limited to 'workspaces/main/shaders/cnn/cnn_weights_generated.wgsl') diff --git a/workspaces/main/shaders/cnn/cnn_weights_generated.wgsl b/workspaces/main/shaders/cnn/cnn_weights_generated.wgsl new file mode 100644 index 0000000..98c17ff --- /dev/null +++ b/workspaces/main/shaders/cnn/cnn_weights_generated.wgsl @@ -0,0 +1,17 @@ +// Generated CNN weights and biases +// DO NOT EDIT MANUALLY - regenerate with scripts/train_cnn.py + +// Placeholder identity-like weights for initial testing +// Layer 0: 3x3 convolution +const weights_layer0: array, 9> = array( + mat4x4(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), + mat4x4(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), + mat4x4(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), + mat4x4(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), + mat4x4(1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0), + mat4x4(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), + mat4x4(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), + mat4x4(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), + mat4x4(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0) +); +const bias_layer0 = vec4(0.0, 0.0, 0.0, 0.0); -- cgit v1.2.3