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use crate::tensor::TensorView;
pub fn conv3d_forward(
input: &TensorView,
kernel: &TensorView,
bias: Option<&[f32]>,
output: &mut TensorView,
pad: (usize, usize, usize),
stride: (usize, usize, usize),
) {
if stride.0 == 0 || stride.1 == 0 || stride.2 == 0 {
return;
}
if !input.is_valid_layout() || !kernel.is_valid_layout() || !output.is_valid_layout() {
return;
}
let n = input.shape[0];
let c = input.shape[1];
let d = input.shape[2];
let h = input.shape[3];
let w = input.shape[4];
let cout = kernel.shape[0];
if kernel.shape[1] != c {
return;
}
if output.shape[0] != n || output.shape[1] != cout {
return;
}
if let Some(b) = bias {
if b.len() < cout {
return;
}
}
let pad_d = pad.0 as isize;
let pad_h = pad.1 as isize;
let pad_w = pad.2 as isize;
let d_isize = d as isize;
let h_isize = h as isize;
let w_isize = w as isize;
for n in 0..n {
for co in 0..cout {
for od in 0..output.shape[2] {
for oh in 0..output.shape[3] {
for ow in 0..output.shape[4] {
let mut acc = 0f32;
for ci in 0..c {
for kd in 0..kernel.shape[2] {
for kh in 0..kernel.shape[3] {
for kw in 0..kernel.shape[4] {
let id = (od * stride.0 + kd) as isize - pad_d;
let ih = (oh * stride.1 + kh) as isize - pad_h;
let iw = (ow * stride.2 + kw) as isize - pad_w;
if id >= 0
&& ih >= 0
&& iw >= 0
&& id < d_isize
&& ih < h_isize
&& iw < w_isize
{
let idu = id as usize;
let ihu = ih as usize;
let iwu = iw as usize;
let i = match input.idx_linear(n, ci, idu, ihu, iwu) {
Some(v) => v,
None => continue,
};
let k = match kernel.idx_linear(co, ci, kd, kh, kw) {
Some(v) => v,
None => continue,
};
acc += input.data[i] * kernel.data[k];
}
}
}
}
}
if let Some(out_i) = output.idx_linear(n, co, od, oh, ow) {
output.data[out_i] = acc + bias.map(|b| b[co]).unwrap_or(0.0);
}
}
}
}
}
}
}