tract-core 0.2.0

Tiny, no-nonsense, self contained, TensorFlow and ONNX inference
Documentation 
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use ndarray::prelude::*;
use num_traits::AsPrimitive;

use crate::ops::prelude::*;

#[derive(Debug, Clone, new)]
pub struct Shape {
    dt: DatumType,
}

impl Shape {
    pub fn coerce_to<T>(shape: &[usize]) -> TractResult<SharedTensor>
    where
        T: Datum,
        usize: AsPrimitive<T>,
    {
        let array = Array1::from_vec(shape.iter().map(|i| i.as_()).collect());
        Ok(array.into())
    }
}

impl Op for Shape {
    fn name(&self) -> Cow<str> {
        "Shape".into()
    }
}

impl StatelessOp for Shape {
    /// Evaluates the operation given the input tensors.
    fn eval(&self, inputs: TVec<SharedTensor>) -> TractResult<TVec<SharedTensor>> {
        let shape = inputs[0].shape();
        Ok(tvec![dispatch_numbers!(Self::coerce_to(self.dt)(&shape))?])
    }
}

impl InferenceRulesOp for Shape {
    fn rules<'r, 'p: 'r, 's: 'r>(
        &'s self,
        s: &mut Solver<'r>,
        inputs: &'p SharedTensorsProxy,
        outputs: &'p SharedTensorsProxy,
    ) -> InferenceResult {
        s.equals(&inputs.len, 1)?;
        s.equals(&outputs.len, 1)?;
        s.equals(&outputs[0].rank, 1)?;
        s.given(&inputs[0].rank, move |s, r| {
            s.equals(&outputs[0].shape[0], r.to_dim())
        })?;
        s.given(&outputs[0].shape[0], move |s, r| {
            if let Ok(d) = r.to_integer() {
                s.equals(&inputs[0].rank, d)?;
            }
            Ok(())
        })?;
        s.given(&inputs[0].shape, move |s, shape| {
            if shape.iter().any(|&d| d.to_integer().is_err()) {
                s.equals(&outputs[0].datum_type, DatumType::TDim)?;
                let array1: Array1<TDim> = Array1::from_iter(shape);
                let tensor: SharedTensor = array1.into();
                s.equals(&outputs[0].value, tensor)
            } else if self.dt == DatumType::I64 {
                s.equals(&outputs[0].datum_type, DatumType::I64)?;
                let array1: Array1<i64> = Array1::from_vec(
                    shape
                        .iter()
                        .map(|&i| i.to_integer().unwrap() as i64)
                        .collect(),
                );
                let tensor: SharedTensor = array1.into();
                s.equals(&outputs[0].value, tensor)
            } else {
                s.equals(&outputs[0].datum_type, DatumType::I32)?;
                let array1: Array1<i32> = Array1::from_vec(
                    shape
                        .iter()
                        .map(|&i| i.to_integer().unwrap() as i32)
                        .collect(),
                );
                let tensor: SharedTensor = array1.into();
                s.equals(&outputs[0].value, tensor)
            }
        })
    }
}

/*
#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn shape_inference_1() {
        let input = TensorFact {
            datum_type: typefact!(DatumType::F32),
            shape: shapefact![1, _, _; ..],
            value: valuefact!(_),
        };

        let output = TensorFact {
            datum_type: typefact!(DatumType::TDim),
            shape: shapefact![_],
            value: valuefact!(_),
        };

        assert_forward!(Shape::new(DatumType::I32), input, output);
    }

    #[test]
    fn shape_inference_2() {
        let input = TensorFact {
            datum_type: typefact!(DatumType::F32),
            shape: shapefact![1, _, _],
            value: valuefact!(_),
        };

        let output = TensorFact {
            datum_type: typefact!(DatumType::TDim),
            shape: shapefact![3],
            value: valuefact!(_),
        };

        assert_forward!(Shape::new(DatumType::I32), input, output);
    }

    #[test]
    fn shape_inference_3() {
        let input = TensorFact {
            datum_type: typefact!(DatumType::F32),
            shape: shapefact![1, 2, 3],
            value: valuefact!(_),
        };

        let output = TensorFact {
            datum_type: typefact!(DatumType::TDim),
            shape: shapefact![3],
            value: valuefact!(Tensor::dims(&[3], &[1.to_dim(), 2.to_dim(), 3.to_dim()]).unwrap()),
        };

        assert_forward!(Shape::new(DatumType::I32), input, output);
    }

    #[test]
    fn shape_inference_4() {
        let input = TensorFact {
            datum_type: typefact!(_),
            shape: shapefact![1, 2, 3],
            value: valuefact!(_),
        };

        let output = TensorFact {
            datum_type: typefact!(DatumType::TDim),
            shape: shapefact![3],
            value: valuefact!(Tensor::dims(&[3], &[1.to_dim(), 2.to_dim(), 3.to_dim()]).unwrap()),
        };

        assert_backward!(Shape::new(DatumType::I32), input, output);
    }
}
*/