feat(cnn_v3): Phase 3 complete — WGSL U-Net inference shaders

5 compute shaders + cnn_v3/common snippet:
  enc0: Conv(20→4,3×3) + FiLM + ReLU             full-res
  enc1: AvgPool + Conv(4→8,3×3) + FiLM + ReLU    half-res
  bottleneck: AvgPool + Conv(8→8,1×1) + ReLU     quarter-res
  dec1: NearestUp + cat(enc1) + Conv(16→4) + FiLM half-res
  dec0: NearestUp + cat(enc0) + Conv(8→4) + FiLM + Sigmoid full-res

Parity rules: zero-pad conv, AvgPool down, NearestUp, FiLM after
conv+bias, skip=concat, OIHW weights+bias layout. Matches PyTorch
train_cnn_v3.py forward() exactly.

Registered in workspaces/main/assets.txt + src/effects/shaders.cc.
Weight layout + Params struct documented in cnn_v3/docs/HOWTO.md §7.

Next: Phase 4 — C++ CNNv3Effect + FiLM uniform upload.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

1 files changed, 72 insertions, 0 deletions

diff --git a/cnn_v3/shaders/cnn_v3_dec1.wgsl b/cnn_v3/shaders/cnn_v3_dec1.wgsl
new file mode 100644
index 0000000..28ae3dc
--- /dev/null
+++ b/cnn_v3/shaders/cnn_v3_dec1.wgsl
@@ -0,0 +1,72 @@
+// CNN v3 — Decoder level 1
+// NearestUp2x(bottleneck) + cat(enc1_skip) -> Conv(16->4, 3x3, zero-pad) + FiLM + ReLU
+//
+// Inputs: bottleneck_tex (rgba32uint, 8xf16)  quarter-res
+//         enc1_tex       (rgba32uint, 8xf16)  half-res    (skip connection)
+// Output: dec1_out       (rgba16float, 4ch)   half-res    (dispatch at half-res dims)
+//
+// Weight layout (f16, OIHW + bias):
+//   [0 .. 16*4*9)  conv: w[out][in][ky][kx]   (in=16: 8 bottleneck + 8 enc1 skip)
+//   [576 .. +4)    bias: b[out]
+
+#include "cnn_v3/common"
+
+const DEC1_IN:  u32 = 16u;
+const DEC1_OUT: u32 = 4u;
+
+struct Params {
+    weight_offset: u32,
+    _pad: vec3u,
+    gamma: vec4f,
+    beta:  vec4f,
+}
+
+@group(0) @binding(0) var bottleneck_tex: texture_2d<u32>;
+@group(0) @binding(1) var enc1_tex: texture_2d<u32>;
+@group(0) @binding(2) var<storage, read> weights: array<u32>;
+@group(0) @binding(3) var<uniform> params: Params;
+@group(0) @binding(4) var dec1_out: texture_storage_2d<rgba16float, write>;
+
+// Load 16 concatenated channels at half-res coord hcoord:
+//   ch 0-7:  bottleneck nearest-up (bottleneck_tex[hcoord/2])
+//   ch 8-15: enc1 skip             (enc1_tex[hcoord])
+// Returns zeros for OOB hcoord (zero-padding for the conv).
+fn load_dec1_concat(hcoord: vec2i, half_dims: vec2i) -> array<f32, 16> {
+    if (hcoord.x < 0 || hcoord.y < 0 || hcoord.x >= half_dims.x || hcoord.y >= half_dims.y) {
+        return array<f32, 16>(0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.);
+    }
+    let quart_dims = half_dims / 2;
+    let qc = clamp(hcoord / 2, vec2i(0), quart_dims - vec2i(1));
+    let b  = unpack_8ch(bottleneck_tex, qc);
+    let s  = unpack_8ch(enc1_tex, hcoord);
+    return array<f32, 16>(
+        b[0], b[1], b[2], b[3], b[4], b[5], b[6], b[7],
+        s[0], s[1], s[2], s[3], s[4], s[5], s[6], s[7]
+    );
+}
+
+@compute @workgroup_size(8, 8)
+fn dec1_main(@builtin(global_invocation_id) id: vec3u) {
+    let half_dims = vec2i(textureDimensions(enc1_tex));
+    let coord     = vec2i(id.xy);
+    if (coord.x >= half_dims.x || coord.y >= half_dims.y) { return; }
+
+    let wo = params.weight_offset;
+    var out: array<f32, DEC1_OUT>;
+
+    for (var o: u32 = 0u; o < DEC1_OUT; o++) {
+        var sum = get_w(wo, DEC1_OUT * DEC1_IN * 9u + o);  // bias
+        for (var ky: i32 = -1; ky <= 1; ky++) {
+            for (var kx: i32 = -1; kx <= 1; kx++) {
+                let feat = load_dec1_concat(coord + vec2i(kx, ky), half_dims);
+                let ki   = u32(ky + 1) * 3u + u32(kx + 1);
+                for (var i: u32 = 0u; i < DEC1_IN; i++) {
+                    sum += get_w(wo, o * DEC1_IN * 9u + i * 9u + ki) * feat[i];
+                }
+            }
+        }
+        out[o] = max(0.0, params.gamma[o] * sum + params.beta[o]);
+    }
+
+    textureStore(dec1_out, coord, vec4f(out[0], out[1], out[2], out[3]));
+}