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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_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_conv3x3_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]
}
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