oxihuman_morph/
neural_blend_shape.rs

1// Copyright (C) 2026 COOLJAPAN OU (Team KitaSan)
2// SPDX-License-Identifier: Apache-2.0
3#![allow(dead_code)]
4
5//! Neural network driven blend shape stub.
6
7/// Activation function types for neural blend shape inference.
8#[derive(Debug, Clone, Copy, PartialEq)]
9pub enum NbsActivation {
10    Relu,
11    Tanh,
12    Sigmoid,
13}
14
15/// A neural-network-driven blend shape evaluator stub.
16#[derive(Debug, Clone)]
17pub struct NeuralBlendShape {
18    pub input_dim: usize,
19    pub output_dim: usize,
20    pub activation: NbsActivation,
21    pub weights: Vec<f32>,
22    pub bias: Vec<f32>,
23    pub enabled: bool,
24}
25
26impl NeuralBlendShape {
27    pub fn new(input_dim: usize, output_dim: usize) -> Self {
28        NeuralBlendShape {
29            input_dim,
30            output_dim,
31            activation: NbsActivation::Relu,
32            weights: vec![0.0; input_dim * output_dim],
33            bias: vec![0.0; output_dim],
34            enabled: true,
35        }
36    }
37}
38
39/// Create a new neural blend shape evaluator.
40pub fn new_neural_blend_shape(input_dim: usize, output_dim: usize) -> NeuralBlendShape {
41    NeuralBlendShape::new(input_dim, output_dim)
42}
43
44/// Run a forward pass (stub: returns zeroed weights).
45pub fn nbs_forward(nbs: &NeuralBlendShape, input: &[f32]) -> Vec<f32> {
46    /* Stub: returns zero output of length output_dim */
47    let _ = input;
48    vec![0.0; nbs.output_dim]
49}
50
51/// Set the activation function.
52pub fn nbs_set_activation(nbs: &mut NeuralBlendShape, activation: NbsActivation) {
53    nbs.activation = activation;
54}
55
56/// Enable or disable the evaluator.
57pub fn nbs_set_enabled(nbs: &mut NeuralBlendShape, enabled: bool) {
58    nbs.enabled = enabled;
59}
60
61/// Load weights from a flat slice (stub: copies up to available length).
62pub fn nbs_load_weights(nbs: &mut NeuralBlendShape, weights: &[f32]) {
63    let n = weights.len().min(nbs.weights.len());
64    nbs.weights[..n].copy_from_slice(&weights[..n]);
65}
66
67/// Serialize to JSON-like string.
68pub fn nbs_to_json(nbs: &NeuralBlendShape) -> String {
69    format!(
70        r#"{{"input_dim":{},"output_dim":{},"enabled":{}}}"#,
71        nbs.input_dim, nbs.output_dim, nbs.enabled
72    )
73}
74
75#[cfg(test)]
76mod tests {
77    use super::*;
78
79    #[test]
80    fn test_new_dims() {
81        let nbs = new_neural_blend_shape(8, 4);
82        assert_eq!(nbs.input_dim, 8 /* input dim must match */,);
83        assert_eq!(nbs.output_dim, 4 /* output dim must match */,);
84    }
85
86    #[test]
87    fn test_default_enabled() {
88        let nbs = new_neural_blend_shape(4, 2);
89        assert!(nbs.enabled /* should be enabled by default */,);
90    }
91
92    #[test]
93    fn test_forward_output_length() {
94        let nbs = new_neural_blend_shape(4, 6);
95        let out = nbs_forward(&nbs, &[0.0; 4]);
96        assert_eq!(
97            out.len(),
98            6, /* forward output length must match output_dim */
99        );
100    }
101
102    #[test]
103    fn test_forward_disabled_still_runs() {
104        let mut nbs = new_neural_blend_shape(4, 3);
105        nbs_set_enabled(&mut nbs, false);
106        let out = nbs_forward(&nbs, &[1.0; 4]);
107        assert_eq!(
108            out.len(),
109            3, /* output length unchanged when disabled */
110        );
111    }
112
113    #[test]
114    fn test_set_activation() {
115        let mut nbs = new_neural_blend_shape(2, 2);
116        nbs_set_activation(&mut nbs, NbsActivation::Tanh);
117        assert_eq!(
118            nbs.activation,
119            NbsActivation::Tanh, /* activation must be set */
120        );
121    }
122
123    #[test]
124    fn test_load_weights() {
125        let mut nbs = new_neural_blend_shape(2, 2);
126        nbs_load_weights(&mut nbs, &[1.0, 2.0, 3.0, 4.0]);
127        assert!((nbs.weights[0] - 1.0).abs() < 1e-6, /* first weight must match */);
128    }
129
130    #[test]
131    fn test_load_weights_partial() {
132        let mut nbs = new_neural_blend_shape(4, 4);
133        nbs_load_weights(&mut nbs, &[5.0, 6.0]);
134        assert!((nbs.weights[0] - 5.0).abs() < 1e-6, /* partial load should succeed */);
135    }
136
137    #[test]
138    fn test_to_json_contains_dims() {
139        let nbs = new_neural_blend_shape(3, 5);
140        let j = nbs_to_json(&nbs);
141        assert!(j.contains("\"input_dim\""), /* json must contain input_dim */);
142        assert!(j.contains("\"output_dim\""), /* json must contain output_dim */);
143    }
144
145    #[test]
146    fn test_weight_count() {
147        let nbs = new_neural_blend_shape(3, 4);
148        assert_eq!(
149            nbs.weights.len(),
150            12, /* weight vector length must be input*output */
151        );
152    }
153
154    #[test]
155    fn test_bias_count() {
156        let nbs = new_neural_blend_shape(3, 4);
157        assert_eq!(
158            nbs.bias.len(),
159            4, /* bias length must equal output_dim */
160        );
161    }
162}
oxihuman_morph/neural_blend_shape.rs

oxihuman_morph/
neural_blend_shape.rs
