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//! Pooling operations (max pool, average pool) for Matrix
use crate::TruenoError;
use super::super::super::Matrix;
impl Matrix<f32> {
/// 2D Max Pooling operation for CNN downsampling
///
/// Applies max pooling over a 2D input tensor with specified kernel size and stride.
///
/// # Arguments
/// * `kernel` - (kernel_height, kernel_width) pooling window size
/// * `stride` - (stride_height, stride_width) step size
///
/// # Examples
/// ```
/// # fn main() -> Result<(), Box<dyn std::error::Error>> {
/// use trueno::matrix::Matrix;
/// let input = Matrix::from_vec(4, 4, vec![
/// 1.0, 2.0, 3.0, 4.0,
/// 5.0, 6.0, 7.0, 8.0,
/// 9.0, 10.0, 11.0, 12.0,
/// 13.0, 14.0, 15.0, 16.0,
/// ])?;
/// let pooled = input.max_pool2d((2, 2), (2, 2))?;
/// assert_eq!(pooled.shape(), (2, 2));
/// assert_eq!(pooled.get(0, 0), Some(&6.0)); // max of [1,2,5,6]
/// assert_eq!(pooled.get(1, 1), Some(&16.0)); // max of [11,12,15,16]
/// # Ok(())
/// # }
/// ```
pub fn max_pool2d(
&self,
kernel: (usize, usize),
stride: (usize, usize),
) -> Result<Matrix<f32>, TruenoError> {
let (kh, kw) = kernel;
let (sh, sw) = stride;
if kh == 0 || kw == 0 || sh == 0 || sw == 0 {
return Err(TruenoError::InvalidInput(
"Kernel and stride dimensions must be positive".into(),
));
}
if kh > self.rows || kw > self.cols {
return Err(TruenoError::InvalidInput(format!(
"Kernel size ({}, {}) larger than input ({}, {})",
kh, kw, self.rows, self.cols
)));
}
let out_h = (self.rows - kh) / sh + 1;
let out_w = (self.cols - kw) / sw + 1;
let mut result = Matrix::new(out_h, out_w);
for i in 0..out_h {
for j in 0..out_w {
let mut max_val = f32::NEG_INFINITY;
for ki in 0..kh {
for kj in 0..kw {
let val = self.data[(i * sh + ki) * self.cols + (j * sw + kj)];
max_val = max_val.max(val);
}
}
result.data[i * out_w + j] = max_val;
}
}
Ok(result)
}
/// 2D Average Pooling operation for CNN downsampling
///
/// Applies average pooling over a 2D input tensor with specified kernel size and stride.
///
/// # Arguments
/// * `kernel` - (kernel_height, kernel_width) pooling window size
/// * `stride` - (stride_height, stride_width) step size
///
/// # Examples
/// ```
/// # fn main() -> Result<(), Box<dyn std::error::Error>> {
/// use trueno::matrix::Matrix;
/// let input = Matrix::from_vec(4, 4, vec![
/// 1.0, 2.0, 3.0, 4.0,
/// 5.0, 6.0, 7.0, 8.0,
/// 9.0, 10.0, 11.0, 12.0,
/// 13.0, 14.0, 15.0, 16.0,
/// ])?;
/// let pooled = input.avg_pool2d((2, 2), (2, 2))?;
/// assert_eq!(pooled.shape(), (2, 2));
/// assert!((pooled.get(0, 0).unwrap_or(&0.0) - 3.5).abs() < 1e-5); // avg of [1,2,5,6]
/// # Ok(())
/// # }
/// ```
pub fn avg_pool2d(
&self,
kernel: (usize, usize),
stride: (usize, usize),
) -> Result<Matrix<f32>, TruenoError> {
let (kh, kw) = kernel;
let (sh, sw) = stride;
if kh == 0 || kw == 0 || sh == 0 || sw == 0 {
return Err(TruenoError::InvalidInput(
"Kernel and stride dimensions must be positive".into(),
));
}
if kh > self.rows || kw > self.cols {
return Err(TruenoError::InvalidInput(format!(
"Kernel size ({}, {}) larger than input ({}, {})",
kh, kw, self.rows, self.cols
)));
}
let out_h = (self.rows - kh) / sh + 1;
let out_w = (self.cols - kw) / sw + 1;
let kernel_size = (kh * kw) as f32;
let mut result = Matrix::new(out_h, out_w);
for i in 0..out_h {
for j in 0..out_w {
let mut sum = 0.0;
for ki in 0..kh {
for kj in 0..kw {
sum += self.data[(i * sh + ki) * self.cols + (j * sw + kj)];
}
}
result.data[i * out_w + j] = sum / kernel_size;
}
}
Ok(result)
}
}