From 47397444b30b0f461b1633297a68300179586fda Mon Sep 17 00:00:00 2001
From: skal <pascal.massimino@gmail.com>
Date: Tue, 10 Feb 2026 08:01:25 +0100
Subject: feat: Add CNN post-processing effect with modular WGSL architecture
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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_layer.wgsl | 46 ++++++++++++++++++++++++++++++
 1 file changed, 46 insertions(+)
 create mode 100644 workspaces/main/shaders/cnn/cnn_layer.wgsl

(limited to 'workspaces/main/shaders/cnn/cnn_layer.wgsl')

diff --git a/workspaces/main/shaders/cnn/cnn_layer.wgsl b/workspaces/main/shaders/cnn/cnn_layer.wgsl
new file mode 100644
index 0000000..e026ce8
--- /dev/null
+++ b/workspaces/main/shaders/cnn/cnn_layer.wgsl
@@ -0,0 +1,46 @@
+// CNN layer shader - uses modular convolution snippets
+// Supports multi-pass rendering with residual connections
+
+@group(0) @binding(0) var smplr: sampler;
+@group(0) @binding(1) var txt: texture_2d<f32>;
+
+#include "common_uniforms"
+#include "cnn_activation"
+#include "cnn_conv3x3"
+#include "cnn_weights_generated"
+
+struct CNNLayerParams {
+    layer_index: i32,
+    use_residual: i32,
+    _pad: vec2<f32>,
+};
+
+@group(0) @binding(2) var<uniform> uniforms: CommonUniforms;
+@group(0) @binding(3) var<uniform> params: CNNLayerParams;
+
+@vertex fn vs_main(@builtin(vertex_index) i: u32) -> @builtin(position) vec4<f32> {
+    var pos = array<vec2<f32>, 3>(
+        vec2<f32>(-1.0, -1.0), vec2<f32>(3.0, -1.0), vec2<f32>(-1.0, 3.0)
+    );
+    return vec4<f32>(pos[i], 0.0, 1.0);
+}
+
+@fragment fn fs_main(@builtin(position) p: vec4<f32>) -> @location(0) vec4<f32> {
+    let uv = p.xy / uniforms.resolution;
+    var result = vec4<f32>(0.0);
+
+    // Single layer for now (layer 0)
+    if (params.layer_index == 0) {
+        result = cnn_conv3x3(txt, smplr, uv, uniforms.resolution,
+                            weights_layer0, bias_layer0);
+        result = cnn_tanh(result);
+    }
+
+    // Residual connection
+    if (params.use_residual != 0) {
+        let input = textureSample(txt, smplr, uv);
+        result = input + result * 0.3;
+    }
+
+    return result;
+}
-- 
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