scirs2_neural/layers/conv/
conv2d.rs

1//! 2D Convolutional layer implementation (minimal stub)
2
3use super::common::{validate_conv_params, PaddingMode};
4use crate::error::{NeuralError, Result};
5use crate::layers::{Layer, ParamLayer};
6use scirs2_core::ndarray::{Array, IxDyn, ScalarOperand};
7use scirs2_core::numeric::Float;
8use std::fmt::Debug;
9
10/// 2D Convolutional layer for neural networks (minimal implementation)
11#[derive(Debug)]
12pub struct Conv2D<F: Float + Debug + Send + Sync> {
13    #[allow(dead_code)]
14    in_channels: usize,
15    #[allow(dead_code)]
16    out_channels: usize,
17    #[allow(dead_code)]
18    kernel_size: (usize, usize),
19    #[allow(dead_code)]
20    stride: (usize, usize),
21    #[allow(dead_code)]
22    padding_mode: PaddingMode,
23    weights: Array<F, IxDyn>,
24    bias: Option<Array<F, IxDyn>>,
25    use_bias: bool,
26    name: Option<String>,
27}
28
29impl<F: Float + Debug + Send + Sync + ScalarOperand + Default> Conv2D<F> {
30    /// Create a new Conv2D layer
31    pub fn new(
32        in_channels: usize,
33        out_channels: usize,
34        kernel_size: (usize, usize),
35        stride: (usize, usize),
36        name: Option<&str>,
37    ) -> Result<Self> {
38        validate_conv_params(in_channels, out_channels, kernel_size, stride)
39            .map_err(NeuralError::InvalidArchitecture)?;
40
41        let weightsshape = vec![out_channels, in_channels, kernel_size.0, kernel_size.1];
42        let weights = Array::zeros(IxDyn(&weightsshape));
43
44        let bias = Some(Array::zeros(IxDyn(&[out_channels])));
45
46        Ok(Self {
47            in_channels,
48            out_channels,
49            kernel_size,
50            stride,
51            padding_mode: PaddingMode::Valid,
52            weights,
53            bias,
54            use_bias: true,
55            name: name.map(String::from),
56        })
57    }
58}
59
60impl<F: Float + Debug + Send + Sync + ScalarOperand + Default> Layer<F> for Conv2D<F> {
61    fn forward(&self, input: &Array<F, IxDyn>) -> Result<Array<F, IxDyn>> {
62        // Minimal implementation - just return input for now
63        Ok(input.clone())
64    }
65
66    fn layer_type(&self) -> &str {
67        "Conv2D"
68    }
69
70    fn inputshape(&self) -> Option<Vec<usize>> {
71        None
72    }
73
74    fn outputshape(&self) -> Option<Vec<usize>> {
75        None
76    }
77
78    fn name(&self) -> Option<&str> {
79        self.name.as_deref()
80    }
81
82    fn backward(
83        &self,
84        _input: &Array<F, scirs2_core::ndarray::IxDyn>,
85        grad_output: &Array<F, scirs2_core::ndarray::IxDyn>,
86    ) -> Result<Array<F, scirs2_core::ndarray::IxDyn>> {
87        // Placeholder implementation - return gradient as-is
88        Ok(grad_output.clone())
89    }
90
91    fn update(&mut self, _learningrate: F) -> Result<()> {
92        // Placeholder implementation
93        Ok(())
94    }
95
96    fn as_any(&self) -> &dyn std::any::Any {
97        self
98    }
99
100    fn as_any_mut(&mut self) -> &mut dyn std::any::Any {
101        self
102    }
103
104    fn parameter_count(&self) -> usize {
105        let weights_count =
106            self.out_channels * self.in_channels * self.kernel_size.0 * self.kernel_size.1;
107        let bias_count = if self.use_bias { self.out_channels } else { 0 };
108        weights_count + bias_count
109    }
110}
111
112impl<F: Float + Debug + Send + Sync + ScalarOperand + Default> ParamLayer<F> for Conv2D<F> {
113    fn get_parameters(&self) -> Vec<Array<F, IxDyn>> {
114        let mut params = vec![self.weights.clone()];
115        if let Some(ref bias) = self.bias {
116            params.push(bias.clone());
117        }
118        params
119    }
120
121    fn set_parameters(&mut self, params: Vec<Array<F, IxDyn>>) -> Result<()> {
122        match (self.use_bias, params.len()) {
123            (true, 2) => {
124                self.weights = params[0].clone();
125                self.bias = Some(params[1].clone());
126            }
127            (false, 1) => {
128                self.weights = params[0].clone();
129            }
130            _ => {
131                let expected = if self.use_bias { 2 } else { 1 };
132                let got = params.len();
133                return Err(NeuralError::InvalidArchitecture(format!(
134                    "Expected {expected} parameters, got {got}"
135                )));
136            }
137        }
138        Ok(())
139    }
140
141    fn get_gradients(&self) -> Vec<Array<F, scirs2_core::ndarray::IxDyn>> {
142        // Placeholder implementation - return empty vector
143        Vec::new()
144    }
145}
scirs2_neural/layers/conv/conv2d.rs

scirs2_neural/layers/conv/
conv2d.rs
