CNN v2: parametric static features - Phases 1-4

Infrastructure for enhanced CNN post-processing with 7D feature input.

Phase 1: Shaders
- Static features compute (RGBD + UV + sin10_x + bias → 8×f16)
- Layer template (convolution skeleton, packing/unpacking)
- 3 mip level support for multi-scale features

Phase 2: C++ Effect
- CNNv2Effect class (multi-pass architecture)
- Texture management (static features, layer buffers)
- Build integration (CMakeLists, assets, tests)

Phase 3: Training Pipeline
- train_cnn_v2.py: PyTorch model with static feature concatenation
- export_cnn_v2_shader.py: f32→f16 quantization, WGSL generation
- Configurable architecture (kernels, channels)

Phase 4: Validation
- validate_cnn_v2.sh: End-to-end pipeline
- Checkpoint → shaders → build → test images

Tests: 36/36 passing

Next: Complete render pipeline implementation (bind groups, multi-pass)

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

1 files changed, 68 insertions, 0 deletions

diff --git a/workspaces/main/shaders/cnn_v2_layer_template.wgsl b/workspaces/main/shaders/cnn_v2_layer_template.wgsl
new file mode 100644
index 0000000..1bf6819
--- /dev/null
+++ b/workspaces/main/shaders/cnn_v2_layer_template.wgsl
@@ -0,0 +1,68 @@
+// CNN v2 Layer Template (placeholder for generated shaders)
+// This file documents the structure - actual layers generated by export script
+
+// Example: Layer 0 (1×1 kernel, 8→16 channels)
+// const KERNEL_SIZE: u32 = 1u;
+// const IN_CHANNELS: u32 = 8u;  // 7 features + bias
+// const OUT_CHANNELS: u32 = 16u;
+// const weights: array<f32, 128> = array(...);
+
+@group(0) @binding(0) var static_features: texture_2d<u32>;
+@group(0) @binding(1) var layer_input: texture_2d<u32>;  // Previous layer output
+@group(0) @binding(2) var output_tex: texture_storage_2d<rgba32uint, write>;
+
+fn unpack_static_features(coord: vec2<i32>) -> array<f32, 8> {
+  let packed = textureLoad(static_features, coord, 0);
+  let v0 = unpack2x16float(packed.x);
+  let v1 = unpack2x16float(packed.y);
+  let v2 = unpack2x16float(packed.z);
+  let v3 = unpack2x16float(packed.w);
+  return array<f32, 8>(v0.x, v0.y, v1.x, v1.y, v2.x, v2.y, v3.x, v3.y);
+}
+
+fn unpack_layer_channels(coord: vec2<i32>) -> array<f32, 8> {
+  let packed = textureLoad(layer_input, coord, 0);
+  let v0 = unpack2x16float(packed.x);
+  let v1 = unpack2x16float(packed.y);
+  let v2 = unpack2x16float(packed.z);
+  let v3 = unpack2x16float(packed.w);
+  return array<f32, 8>(v0.x, v0.y, v1.x, v1.y, v2.x, v2.y, v3.x, v3.y);
+}
+
+fn pack_channels(values: array<f32, 8>) -> vec4<u32> {
+  return vec4<u32>(
+    pack2x16float(vec2<f32>(values[0], values[1])),
+    pack2x16float(vec2<f32>(values[2], values[3])),
+    pack2x16float(vec2<f32>(values[4], values[5])),
+    pack2x16float(vec2<f32>(values[6], values[7]))
+  );
+}
+
+@compute @workgroup_size(8, 8)
+fn main(@builtin(global_invocation_id) id: vec3<u32>) {
+  let coord = vec2<i32>(id.xy);
+  let dims = textureDimensions(static_features);
+
+  if (coord.x >= i32(dims.x) || coord.y >= i32(dims.y)) {
+    return;
+  }
+
+  // Load static features (always available)
+  let static_feat = unpack_static_features(coord);
+
+  // Convolution loop (example for generated code)
+  // var output: array<f32, OUT_CHANNELS>;
+  // for (var c: u32 = 0u; c < OUT_CHANNELS; c++) {
+  //   var sum: f32 = 0.0;
+  //   for (var ky: i32 = -radius; ky <= radius; ky++) {
+  //     for (var kx: i32 = -radius; kx <= radius; kx++) {
+  //       let sample_coord = coord + vec2<i32>(kx, ky);
+  //       // Load static + prev layer, multiply weights, accumulate
+  //     }
+  //   }
+  //   output[c] = max(0.0, sum);  // ReLU
+  // }
+
+  // Placeholder output
+  textureStore(output_tex, coord, vec4<u32>(0u));
+}