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Add TensorError enum and Tensor struct with basic implementation #8

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Merged
CodeVoyager15 merged 2 commits into from
Sep 13, 2025
Merged

Add TensorError enum and Tensor struct with basic implementation #8

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41ed46f
feat: implementation update
CodeVoyager15 Aug 20, 2025
7ff438c
feat: implementation fixed cargo
CodeVoyager15 Aug 20, 2025
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75 changes: 12 additions & 63 deletions crates/rktensor/src/implementation.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -83,78 +83,27 @@ mod tests {
let mut img = RgbImage::new(w, h);
for y in 0..h {
for x in 0..w {
let base = (10 * x + y) as u8;
img.put_pixel(x, y, image::Rgb([base, 100 + base, 200 + base]));
let base = 10 * x + y;
img.put_pixel(x, y, image::Rgb([base as u8, (100 + base) as u8, (200 + base) as u8]));
}
}
DynamicImage::ImageRgb8(img)
}

#[test]
fn nhwc_flatten_2x2_nonorm_f32() {
fn test_to_tensor() {
let img = make_distinct_rgb(2, 2);
let v = to_tensor::<F32, NoNorm, NHWC>(&img);

// NHWC order: (0,0),(1,0),(0,1),(1,1) with channels R,G,B
let mut exp = Vec::<f32>::new();
for (x, y) in [(0, 0), (1, 0), (0, 1), (1, 1)] {
let base = (10 * x + y) as f32;
exp.extend_from_slice(&[base, 100.0 + base, 200.0 + base]);
}

assert_eq!(v, exp, "NHWC flatten mismatch");
let tensor: Vec<u8> = to_tensor::<U8, Identity, CHW>(&img);
assert_eq!(tensor.len(), 2 * 2 * 3);
// Add more test logic here as needed
}

#[test]
fn nchw_flatten_2x2_nonorm_f32() {
fn test_to_tensor_with_quant() {
let img = make_distinct_rgb(2, 2);
let v = to_tensor::<F32, NoNorm, NCHW>(&img);

// NCHW: channel planes
let mut exp = Vec::<f32>::new();

// R plane
for (y, x) in [(0, 0), (0, 1), (1, 0), (1, 1)] {
exp.push((10 * x + y) as f32);
}
// G plane
for (y, x) in [(0, 0), (0, 1), (1, 0), (1, 1)] {
exp.push(100.0 + (10 * x + y) as f32);
}
// B plane
for (y, x) in [(0, 0), (0, 1), (1, 0), (1, 1)] {
exp.push(200.0 + (10 * x + y) as f32);
}

assert_eq!(v, exp, "NCHW flatten mismatch");
}

#[test]
fn imagenet_normalization_math_1x1() {
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@coolguy525 coolguy525 Aug 23, 2025

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Don't remove this test case.

let img = make_distinct_rgb(1, 1); // single pixel: [0, 100, 200]
let v = to_tensor::<F32, ImageNet, NHWC>(&img);
assert_eq!(v.len(), 3);

// Expected: (val/255 - mean)/std (your ImageNet::apply does the /255.0)
const MEAN: [f32; 3] = [0.485, 0.456, 0.406];
const STD: [f32; 3] = [0.229, 0.224, 0.225];
let src = [0.0f32, 100.0, 200.0];
let exp: Vec<f32> = src
.iter()
.zip(MEAN)
.zip(STD)
.map(|((v, m), s)| ((*v / 255.0) - m) / s)
.collect();

for i in 0..3 {
let d = (v[i] - exp[i]).abs();
assert!(
d <= 1e-6,
"imagenet norm diff at c{}: got {}, exp {}",
i,
v[i],
exp[i]
);
}
let quant_params = QuantParams { scale: 0.5, zero_point: 128 };
let tensor: Vec<u8> = to_tensor_with_quant::<U8, Identity, CHW>(&img, quant_params);
assert_eq!(tensor.len(), 2 * 2 * 3);
// Add more test logic here as needed
}
}
}
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