burn-tensor 0.1.0

This library provides multiple tensor implementations hidden behind an easy to use API that supports reverse mode automatic differentiation. 
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use crate::tensor::{
    backend::tch::{TchShape, TchTensor},
    ops::*,
};
use std::ops::Range;

impl<
        P: tch::kind::Element + std::fmt::Debug + Copy + Default,
        const D1: usize,
        const D2: usize,
    > TensorOpsIndex<P, D1, D2> for TchTensor<P, D1>
{
    fn index(&self, indexes: [Range<usize>; D2]) -> Self {
        let mut tensor = self.tensor.shallow_clone();

        for i in 0..D2 {
            let index = indexes[i].clone();
            let start = index.start as i64;
            let length = (index.end - index.start) as i64;
            tensor = tensor.narrow(i as i64, start, length);
        }
        let shape = self.shape.index(indexes);
        let kind = self.kind.clone();

        Self {
            kind,
            tensor,
            shape,
        }
    }

    fn index_assign(&self, indexes: [Range<usize>; D2], values: &Self) -> Self {
        let tensor_original = self.tensor.copy();
        let tch_shape = TchShape::from(self.shape.clone());

        let mut tensor = tensor_original.view_(&tch_shape.dims);

        for i in 0..D2 {
            let index = indexes[i].clone();
            let start = index.start as i64;
            let length = (index.end - index.start) as i64;

            tensor = tensor.narrow(i as i64, start, length);
        }

        tensor.copy_(&values.tensor);

        let shape = self.shape.clone();
        let kind = self.kind.clone();

        Self {
            kind,
            tensor: tensor_original,
            shape,
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::tensor::Data;

    #[test]
    fn should_support_full_indexing_1d() {
        let data = Data::<f64, 1>::from([0.0, 1.0, 2.0]);
        let tensor = TchTensor::from_data(data.clone(), tch::Device::Cpu);

        let data_actual = tensor.index([0..3]).into_data();

        assert_eq!(data, data_actual);
    }

    #[test]
    fn should_support_partial_indexing_1d() {
        let data = Data::<f64, 1>::from([0.0, 1.0, 2.0]);
        let tensor = TchTensor::from_data(data.clone(), tch::Device::Cpu);

        let data_actual = tensor.index([1..3]).into_data();

        let data_expected = Data::from([1.0, 2.0]);
        assert_eq!(data_expected, data_actual);
    }

    #[test]
    fn should_support_full_indexing_2d() {
        let data = Data::<f64, 2>::from([[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]);
        let tensor = TchTensor::from_data(data.clone(), tch::Device::Cpu);

        let data_actual_1 = tensor.index([0..2]).into_data();
        let data_actual_2 = tensor.index([0..2, 0..3]).into_data();

        assert_eq!(data, data_actual_1);
        assert_eq!(data, data_actual_2);
    }

    #[test]
    fn should_support_partial_indexing_2d() {
        let data = Data::<f64, 2>::from([[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]);
        let tensor = TchTensor::from_data(data.clone(), tch::Device::Cpu);

        let data_actual = tensor.index([0..2, 0..2]).into_data();

        let data_expected = Data::from([[0.0, 1.0], [3.0, 4.0]]);
        assert_eq!(data_expected, data_actual);
    }

    #[test]
    fn should_support_indexe_assign_1d() {
        let data = Data::<f64, 1>::from([0.0, 1.0, 2.0]);
        let data_assigned = Data::<f64, 1>::from([10.0, 5.0]);

        let tensor = TchTensor::from_data(data.clone(), tch::Device::Cpu);
        let tensor_assigned = TchTensor::from_data(data_assigned.clone(), tch::Device::Cpu);

        let data_actual = tensor.index_assign([0..2], &tensor_assigned).into_data();

        let data_expected = Data::<f64, 1>::from([10.0, 5.0, 2.0]);
        assert_eq!(data_expected, data_actual);
    }

    #[test]
    fn should_support_indexe_assign_2d() {
        let data = Data::<f64, 2>::from([[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]);
        let data_assigned = Data::<f64, 2>::from([[10.0, 5.0]]);

        let tensor = TchTensor::from_data(data.clone(), tch::Device::Cpu);
        let tensor_assigned = TchTensor::from_data(data_assigned.clone(), tch::Device::Cpu);

        let data_actual = tensor
            .index_assign([1..2, 0..2], &tensor_assigned)
            .into_data();

        let data_expected = Data::<f64, 2>::from([[0.0, 1.0, 2.0], [10.0, 5.0, 5.0]]);
        assert_eq!(data_expected, data_actual);
    }
}