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// 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> {
let uv = p.xy / uniforms.resolution;
let original = (textureSample(original_input, smplr, uv) - 0.5) * 2.0; // Normalize to [-1,1]
var result = vec4<f32>(0.0);
// Layer 0: 7→4 (RGBD output)
if (params.layer_index == 0) {
result = cnn_conv3x3_7to4_src(txt, smplr, uv, uniforms.resolution, weights_layer0);
result = cnn_tanh(result); // Keep in [-1,1]
}
else if (params.layer_index == 1) {
result = cnn_conv5x5_7to4(txt, smplr, uv, uniforms.resolution,
original, weights_layer1);
result = cnn_tanh(result); // Keep in [-1,1]
}
else if (params.layer_index == 2) { // last layer
let gray_out = cnn_conv3x3_7to1(txt, smplr, uv, uniforms.resolution,
original, weights_layer2);
// At this point here, 'gray_out' is what the training script should have learned.
// Below is some extra code for visual output, excluded from training:
result = vec4<f32>(gray_out, gray_out, gray_out, 1.0); // Keep in [-1,1]
let blended = mix(original, result, params.blend_amount);
return (blended + 1.0) * 0.5; // Denormalize to [0,1] for display
}
return result;
}
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