archive(cnn): move CNN v1 to cnn_v1/ subdirectory

Consolidate CNN v1 (CNNEffect) into dedicated directory:
- C++ effect: src/effects → cnn_v1/src/
- Shaders: workspaces/main/shaders/cnn → cnn_v1/shaders/
- Training: training/train_cnn.py → cnn_v1/training/
- Docs: doc/CNN*.md → cnn_v1/docs/

Updated all references:
- CMake source list
- C++ includes (relative paths: ../../cnn_v1/src/)
- Asset paths (../../cnn_v1/shaders/)
- Documentation cross-references

CNN v1 remains active in timeline. For new work, use CNN v2 with
enhanced features (7D static, storage buffer, sigmoid activation).

Tests: 34/34 passing (100%)

1 files changed, 0 insertions, 55 deletions

diff --git a/workspaces/main/shaders/cnn/cnn_layer.wgsl b/workspaces/main/shaders/cnn/cnn_layer.wgsl
deleted file mode 100644
index cbd1686..0000000
--- a/workspaces/main/shaders/cnn/cnn_layer.wgsl
+++ /dev/null
@@ -1,55 +0,0 @@
-// CNN layer shader - uses modular convolution snippets
-// Supports multi-pass rendering with residual connections
-// DO NOT EDIT - Generated by train_cnn.py
-
-@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_conv5x5"
-#include "cnn_weights_generated"
-
-struct CNNLayerParams {
-    layer_index: i32,
-    blend_amount: f32,
-    _pad: vec2<f32>,
-};
-
-@group(0) @binding(2) var<uniform> uniforms: CommonUniforms;
-@group(0) @binding(3) var<uniform> params: CNNLayerParams;
-@group(0) @binding(4) var original_input: texture_2d<f32>;
-
-@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> {
-    // Match PyTorch linspace
-    let uv = (p.xy - 0.5) / (uniforms.resolution - 1.0);
-    let original_raw = textureSample(original_input, smplr, uv);
-    let original = (original_raw - 0.5) * 2.0;  // Normalize to [-1,1]
-    let gray = (dot(original_raw.rgb, vec3<f32>(0.2126, 0.7152, 0.0722)) - 0.5) * 2.0;
-    var result = vec4<f32>(0.0);
-
-    // Layer 0: 7→4 (RGBD output, normalizes [0,1] input)
-    if (params.layer_index == 0) {
-        result = cnn_conv5x5_7to4_src(txt, smplr, uv, uniforms.resolution, weights_layer0);
-        result = cnn_tanh(result);
-    }
-    else if (params.layer_index == 1) {
-        result = cnn_conv3x3_7to4(txt, smplr, uv, uniforms.resolution, gray, weights_layer1);
-        result = cnn_tanh(result);  // Keep in [-1,1]
-    }
-    else if (params.layer_index == 2) {
-        let sum = cnn_conv3x3_7to1(txt, smplr, uv, uniforms.resolution, gray, weights_layer2);
-        let gray_out = 1.0 / (1.0 + exp(-sum));  // Sigmoid activation
-        result = vec4<f32>(gray_out, gray_out, gray_out, 1.0);
-        return mix(original_raw, result, params.blend_amount); // [0,1]
-    }
-    return result;  // [-1,1]
-}