Feat instance normalization

docs/SUMMARY.md

@@ -147,6 +147,7 @@
     * [tensor.reduce\_log\_sum\_exp](framework/operators/tensor/tensor.reduce\_log\_sum\_exp.md)
     * [tensor.unique](framework/operators/tensor/tensor.unique.md)
     * [tensor.compress](framework/operators/tensor/tensor.compress.md)
+    * [tensor.instance_normalization](framework/operators/tensor/tensor.instance_normalization.md)
     * [tensor.layer_normalization](framework/operators/tensor/tensor.layer_normalization.md)
     * [tensor.scatter\_nd](framework/operators/tensor/tensor.scatter\_nd.md)
     * [tensor.dynamic_quantize_linear](framework/operators/tensor/tensor.dequantize_linear.md)

docs/framework/operators/tensor/tensor.instance_normalization.md

@@ -0,0 +1,106 @@
+# tensor.instance_normalization
+
+```rust
+   fn instance_normalization(
+    self: @Tensor<T>,
+    scale: @Tensor<T>,
+    bias: @Tensor<T>,
+    epsilon: Option<T>,
+) -> Tensor<T>;
+```
+
+Computes instance normalization on a given input tensor.
+
+The overall computation has two stages:
+1. The first stage normalizes the elements to have zero mean and unit variance for each instance.
+2. The second stage scales and shifts the results of the first stage using the provided scale and bias tensors.
+
+## Args
+
+* `self` (`@Tensor<T>`) - The input tensor with dimensions `(N x C x D1 x D2 ... Dn)`, where `N` is the batch size,
+  `C` is the number of channels, and `D1`, `D2`, ..., `Dn` are the remaining dimensions.
+* `scale` (`@Tensor<T>`) - Scale tensor of shape `(C)`.
+* `bias` (`Option<@Tensor<T>>`) - Bias tensor of shape `(C)`. If `None`, no bias is applied.
+* `epsilon` (`Option<T>`) (default is zero) - The epsilon value to use to avoid division by zero.
+
+## Panics
+
+* Panics if the scale tensor's shape is not `(C)`.
+* Panics if the bias tensor is provided and its shape is not `(C)`.
+
+## Returns
+
+A new tensor `Tensor<T>` with the same shape as the input tensor, after applying instance normalization.
+
+## Example
+
+```rust
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP16x16TensorPartialEq;
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::FP16x16Tensor;
+use orion::numbers::{FixedTrait, FP16x16};
+
+fn instance_normalization_example() -> Tensor<FP16x16> {
+
+    let epsilon = Option::Some( FixedTrait::new(6554, false));
+
+    let mut shape = ArrayTrait::<usize>::new();
+        shape.append(2);
+        shape.append(3);
+        shape.append(2);
+        shape.append(1);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 24149, sign: false });
+    data.append(FP16x16 { mag: 35894, sign: false });
+    data.append(FP16x16 { mag: 38633, sign: true });
+    data.append(FP16x16 { mag: 37793, sign: true });
+    data.append(FP16x16 { mag: 23838, sign: false });
+    data.append(FP16x16 { mag: 5937, sign: false });
+    data.append(FP16x16 { mag: 13047, sign: true });
+    data.append(FP16x16 { mag: 55527, sign: false });
+    data.append(FP16x16 { mag: 97165, sign: true });
+    data.append(FP16x16 { mag: 77657, sign: false });
+    data.append(FP16x16 { mag: 7142, sign: false });
+    data.append(FP16x16 { mag: 96338, sign: false });
+    data.append(FP16x16 { mag: 24716, sign: true });
+    let X = TensorTrait::new(shape.span(), data.span());
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 41865, sign: true });
+    data.append(FP16x16 { mag: 81535, sign: false });
+    data.append(FP16x16 { mag: 81322, sign: true });   
+    let scale = TensorTrait::new(shape.span(), data.span());
+
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    let mut data = ArrayTrait::new();
+    data.append(FP16x16 { mag: 11243, sign: true });
+    data.append(FP16x16 { mag: 122125, sign: false });
+    data.append(FP16x16 { mag: 97543, sign: false });
+    let bias = TensorTrait::new(shape.span(), data.span());
+
+    return X.instance_normalization(@scale,@bias, epsilon);
+}
+>>>
+   [[[[-0.72982788],
+    [ 0.38671875]],
+
+   [[ 0.83106995],
+     [ 2.89585876]],
+
+   [[ 0.97167969],
+    [ 2.00509644]]],
+
+  [[[-0.78804016],
+    [ 0.44493103]],
+
+   [[ 2.93608093],
+    [ 0.79092407]],
+
+   [[ 0.31443787],
+    [ 2.66233826]]]]
+```